RELEASE NOTESVERSION 2024.1

Dragonfly 3D World
Learn all about the new features, product enhancements, and other improvements
implemented in the Dragonfly 3D World and Dragonfly Pro 2024.1 software release.

Copyright© 2024 Comet Technologies Canada Inc. All rights reserved.
The present end-user documentation is confidential and proprietary information of Comet Technologies Canada Inc. (the“Company”). Only licensees of the Company have a right to use the information contained herein. Only licensees have theright to copy and/or transfer the information for internal use, unless otherwise agreed with the Company. Any unauthorizeduse, disclosure, transfer, or reproduction of this confidential information may give rise to a right in the Company to seek a legal
remedy against such use, disclosure, transfer, or reproduction.
Except as expressly provided otherwise in writing, the information provided in this document is provided AS IS, without any
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Any use of the documentation or the information contained herein is at the risk of the user. Documentation may include
technical or other inaccuracies or typographical errors. Information is subject to change without notice.

Trademarks
Dragonfly and the Dragonfly logos are trademarks of Comet Technologies Canada Inc.

Third-Party Trademarks
Python is a trademark or registered trademark of the Python Software Foundation. Adobe, Acrobat, Flash, and Reader are
either registered trademarks or trademarks of Adobe Systems Incorporated in the United States and/or other countries. Intel,
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NoticesPortions of Dragonfly’s 3D engine licensed from the University of Münster.
Dragonfly is NOT a medical device.

Cover Images
Clockwise from the top left: 3D rendering of a Venus flytrap (Dionaea muscipula) scanned on a Comet Yxlon CT system, 3D
rendering of a clipped boron dataset, segmented Ankylosaurus jawbone (courtesy of DigiMorph), vector field mapping of a 4D
deformation study looking at the interface between an additive manufactured spacer and a human vertebra while undergoing
continuous compression (CT data courtesy TESCAN/sample courtesy Rush University).

Comet Technologies Canada Inc.
460, rue Ste-Catherine Ouest, Suite 600
Montréal (Québec), Canada H3B 1A7

Dragonfly – a brand of Comet

dragonfly.comet.tech

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Contents
New Branding ................................................................................................................................................. 7
New Features and Product Enhancements ............................................................................................. 8
Dragonfly Social......................................................................................................................................................................... 9
Registering an Account ............................................................................................................................................................................ 9Connecting to Dragonfly Social ..........................................................................................................................................................11

Segmentation Wizard ............................................................................................................................................................ 12
Sparse Labeling for Deep Model Training ......................................................................................................................................12Starting Sessions with a Trained Model ..........................................................................................................................................12
Revised Model Generation Strategies ..............................................................................................................................................14
Support for Multiple Datasets .............................................................................................................................................................15

Deep Learning Tool ................................................................................................................................................................ 16
Ready-To-Use Deep Model Library ...................................................................................................................................................17
Ready-To-Use Models ................................................................................................................................................................................................. 19

Updates for Segment with AI and Filter with AI...........................................................................................................................20
Additional Pre-Trained Models ...........................................................................................................................................................21
Sparse Labeling .........................................................................................................................................................................................23
New Architectures ....................................................................................................................................................................................23
More Efficient Patch Blending for Accelerated Inference.........................................................................................................24
Compile for Faster Inference................................................................................................................................................................25
New Advanced Tools ..............................................................................................................................................................................26
Patch Inspection ............................................................................................................................................................................................................. 27
Generate Augmented Deep Learning Training Sets ....................................................................................................................................... 28

Automatic Loading/Unloading of Models ......................................................................................................................................28
Automatic Saving of Models................................................................................................................................................................29
Export Training Sets in a Single File ..................................................................................................................................................29
External Processes for Deep Learning Task Processing ............................................................................................................30
Select Dataset for Visual Feedback ...................................................................................................................................................31
Validation Settings ...................................................................................................................................................................................31
Easy Sharing of Deep Models ..............................................................................................................................................................32

NGSolve Integration............................................................................................................................................................... 33
Open Fiber Segmentation.................................................................................................................................................... 35
Automated Porosity Segmentation.................................................................................................................................. 37

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Compute Measurements ...................................................................................................................................................... 39
Radiomics ....................................................................................................................................................................................................40
Gray Level Co-occurrence Matrix (GLCM) ........................................................................................................................................................... 41
Gray Level Dependence Matrix (GLDM) ............................................................................................................................................................... 42
Gray Level Run Length Matrix (GLRLM) Features ............................................................................................................................................. 42
Gray Level Size Zone Matrix (GLSZM) Features ................................................................................................................................................. 43
Neighbouring Gray Tone Difference Matrix (NGTDM) Features ................................................................................................................ 43

Longest Length..........................................................................................................................................................................................44
Phi and Theta Computations ...............................................................................................................................................................45

Analyze and Classify Measurements ................................................................................................................................ 47
Histogram Analysis ................................................................................................................................................................. 50
Meshes ........................................................................................................................................................................................ 50
3D Mesh Painter .......................................................................................................................................................................................51
Mesh Smoothing with Constraint ......................................................................................................................................................52
Remeshing with Poisson Surface Reconstruction .......................................................................................................................53
Mesh Capping ............................................................................................................................................................................................54
Adjustable Thickness for Outlined Meshes ....................................................................................................................................54

ROI Painter ................................................................................................................................................................................. 55
ROI Painting Preferences .......................................................................................................................................................................57

Thresholding ............................................................................................................................................................................. 57
3D Rendering ............................................................................................................................................................................ 58
Multiple Lights ...........................................................................................................................................................................................58
Lighting and Shadows ............................................................................................................................................................................60
Tone Mapping ...........................................................................................................................................................................................61
Suppression of Moiré Effects ...............................................................................................................................................................62
Material Presets .........................................................................................................................................................................................63

Settings for ROIs and Multi-ROIs ...................................................................................................................................... 64
Planes........................................................................................................................................................................................... 65
Exploded Views .........................................................................................................................................................................................65
Show Grayscale .........................................................................................................................................................................................66

3D View Controls..................................................................................................................................................................... 67
Automatic View Alignment ...................................................................................................................................................................67
Constrain Pan Movements ....................................................................................................................................................................67
Gyroscope Axis Indicators .....................................................................................................................................................................68
New preferences for the Axis Indicator ...........................................................................................................................................69
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CT Reconstruction ................................................................................................................................................................... 69
Organizer.................................................................................................................................................................................... 70
Image Registration ................................................................................................................................................................. 70
Natural Language Search ..................................................................................................................................................... 71
Importing RAW Data.............................................................................................................................................................. 72
File Export .................................................................................................................................................................................. 72
Image Filtering ......................................................................................................................................................................... 73
Developer Tools ....................................................................................................................................................................... 74
Tutorial Videos...........................................................................................................................................................................................75

Menu Bar Changes ......................................................................................................................................76
Workflows Menu ..................................................................................................................................................................... 76
Utilities Menu............................................................................................................................................................................ 76
Help Menu ................................................................................................................................................................................. 76

Changes to Contextual Menus .................................................................................................................77
Dataset Contextual Menu .................................................................................................................................................... 77
ROI Contextual Menu ............................................................................................................................................................ 78
Multi-ROI Contextual Menu ................................................................................................................................................ 79
Mesh Contextual Menu ........................................................................................................................................................ 79
Graph Contextual Menu ....................................................................................................................................................... 80
Vector Field Contextual Menu............................................................................................................................................ 80
Views Contextual Menu ........................................................................................................................................................ 80

Preferences Changes .................................................................................................................................. 81
Views Preferences ................................................................................................................................................................... 81
Deep Learning Preferences ................................................................................................................................................. 81

System Requirements ................................................................................................................................ 82
Third-Party Packages ................................................................................................................................. 82
Publicly Available Source Code ............................................................................................................... 83
TensorFlow................................................................................................................................................................................. 83
Qt................................................................................................................................................................................................... 83
Porous Microstructure Analysis (PuMA) ......................................................................................................................... 83
Netgen/NGSolve ..................................................................................................................................................................... 84

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Learning Dragonfly .................................................................................................................................... 85
Dragonfly Workshops............................................................................................................................................................ 85
Online Training......................................................................................................................................................................... 85

Requesting New Features ......................................................................................................................... 85
Reporting Issues ......................................................................................................................................... 86

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New Branding
This software release features our new branding for the Dragonfly product line. It
introduces the new name — Dragonfly 3D World — for this version of Dragonfly andincludes updated logos and icons, as seen on the front cover of this publication and on
the title bar of the application.
New Dragonfly 3D World icon

These changes are part of an extensive rebranding initiative to align Dragonfly as abrand of Comet. Comet is a globally leading, innovative technology company based in
Switzerland with a focus on plasma control and X-ray technology. Comet’s innovative
solutions are in demand in the semiconductor and electronics market, the aerospace andautomobile industry, and in security inspection. Headquartered in Flamatt, Switzerland,
Comet has a presence in all world markets and employs more than 1,700 people
worldwide, including about 600 in Switzerland. Besides production facilities in China,
Denmark, Germany, Malaysia, Switzerland, and the USA, Comet maintains various othersubsidiaries in Canada, China, Japan, Korea, Taiwan, and the USA. Comet (COTN) is listedon the SIX Swiss Exchange. To learn more about Comet, go to https://comet.tech/.
You can expect to hear more from us soon about additions to the Dragonfly productline, such as Dragonfly 2D World and Dragonfly View, as well as targeted applications
that meet the needs of industrial users, such as Void Inspect and Battery Insights.

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New Features and Product Enhancements
The software release for the newly branded Dragonfly 3D World 2024.1 and Dragonfly
Pro 2024.1 includes new features and performance enhancements that are sure to boost
your productivity. Just some of the highlights of this new release include:•

The introduction of Dragonfly Social, an online community platform that lets Dragonfly users
start discussions, chat, access material, post announcements, as well as share extensions anddeep models (see Dragonfly Social on page 9).

•

Support for sparsely-labeled ground truths, which means that you can prepare training datafor deep model training quicker (see Sparse Labeling for Deep Model Training on page 12).

•

Access to an expanded remote library of ready-to-use deep models for image enhancement
and segmentation (see Ready-To-Use Deep Model Library on page 17).

•

Accelerated inference (see More Efficient Patch Blending for Accelerated Inference on page24) and advanced tools for inspecting patches and generating augmented training sets fordeep learning (see New Advanced Tools on page 26).

•

Integration of NGSolve for quantifying fluid flow and heat dissipation in engineering andresearch applications (see NGSolve Integration on page 33).

•

Automated segmentation routines, including extraction of individual fibers from datasets ofreinforced composites (see Open Fiber Segmentation on page 35) and automated methods
to reliably segment porosity in single-material samples, such as castings (see Automated
Porosity Segmentation on page 37).

•

Advanced image analytics to answer nuanced research questions and radiomics texture
extraction to identify and analyze image features (see Compute Measurements on page 39
and Radiomics Features on page 40).

•

The production version of Dragonfly 3D World’s Analyze and Classify Measurements module,
which lets you import scalar data from multi-ROIs, meshes, graphs, and vector fields for the
cross-table analysis of feature vectors (see Analyze and Classify Measurements on page 47).

•

Multiple lights, ‘material’ definitions, and improved tone mapping to make your 3D
renderings even more sensational (see 3D Rendering on page 58).

•

Extended accessibility, with the option to install the application on a system/laptop without adedicated GPU (see System Requirements on page 82).

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Dragonfly Social
Dragonfly Social is a new online community platform that lets Dragonfly users start
discussions, access material, post announcements, as well as upload and download
extensions and deep models for the application. In addition, an integrated chatbot thatlets users can ask basic questions about working with Dragonfly and learn more about
best practices will soon be added to the platform.
Dragonfly Social

Registering an Account
You can register a new Dragonfly Social account as follows:
1. Do one of the following:oEnter your information in the Introducing Dragonfly Social dialog, as shown in
the following screenshot, and then continue to Step 3.

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oChoose Help > Manage Dragonfly Social Accounts on the menu bar.
The Manage Dragonfly Social Accounts dialog appears.

2. Do the following in the Manage Dragonfly Social Accounts dialog:oClick the Register New Account button, as shown below.

oEnter your information in the Register New Account dialog.

3. Click the Register button.
The End User Licence Agreement dialog appears.
4. Review the terms and conditions and click Agree if you accept the terms andconditions.
The Dragonfly Social Account Created Successfully dialog appears.
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5. Click OK in the dialog.
6. Verify your email.
If verification is successful, you will be able to login with the registered account.

Connecting to Dragonfly Social
After you have registered for an account, you can connect to Dragonfly Social by doing
the following. You should note that you need to be connected to the Internet to access
Dragonfly Social.
1. Do one of the following:oClick Connect in the Connect Now dialog, as shown below.

oChoose Help > Connect to Dragonfly Social As > Login As on the Dragonfly
menu bar to connect to the platform.

o

Go to https://dragonflysocial.comet.tech/ from any browser on a system
connected to the Internet.

2. Sign into your account.

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Segmentation Wizard
Sparse labeling for deep model training, starting sessions with a trained deep model,
easy access to our remote library of ready-to-use segmentation models, and support forworking with multiple datasets are all available for the Segmentation Wizard in thissoftware release. Other deep learning workflow enhancements also included in the
Segmentation Wizard are described in the topic Deep Learning Tool on page 16.

Sparse Labeling for Deep Model Training
One of the significant challenges of training models for semantic segmentation can be
the need to densely label ground truth multi-ROIs. To address this issue and improve
workflow efficiency, this software release reduces labeling requirements for deep modeltraining by providing support for sparse labeling. In many cases, you should be able toachieve comparable results to those obtained by training with densely labeled groundtruths, which can be much more laborious to obtain.

Starting Sessions with a Trained Model
In some cases, you may want to start a Segmentation Wizard session with a deep modelthat was already trained either by yourself or by the Dragonfly team. This option is now
available in the Model Selection dialog, shown below, which appears whenever you
launch the Segmentation Wizard.
Model Selection dialog

You can either select a trained model in the Model drop-down menu or click the Remote
Library

button to open the Remote Library of Ready-to-Use Segmentation Models

dialog, which is shown on the following page.

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Remote Library of Ready-to-Use Segmentation Models dialog

Downloaded ready-to-use models will appear in the Model drop-down menu the nexttime that you launch Dragonfly 3D World. Refer to the topic Ready-To-Use Deep Model
Library on page 17 for additional information about ready-to-use-models.
NOTE

Release Notes

You can also open the Remote Library of Ready-to-Use Segmentation Models dialog from the
Remote Library button on the Models tab after you launch the Segmentation Wizard, as shown
below.

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Revised Model Generation Strategies
This software release provides revised model generation strategies based on theavailability of pre-trained models and sparse labeling for deep model training. You
should note that the Model Generation Strategy dialog, which includes all default anduser-defined strategies, appears whenever you click Train and the models list is empty.
You can also click the Open button the Models tab to open the dialog, shown below.
Model Generation Strategy dialog

The following table provides a summary of the changes and additions to the default
model generation strategies. Any strategy not listed below remains unchanged fromprevious versions.
Model generation strategies updates

Model

Description

Machine Learning (Classical) Models

Renamed, but still includes the two random forest models available in the original‘Sparse Training Data’ strategy.

Pre-Trained U-Net Depth = 5

The models in this new strategy provide both good and fast results with the 2D UNet pre-trained model, as well as better but slower results with the 2.5D U-Net pretrained model. You should note that starting with a pre-trained model often
provides better and faster results with smaller training sets than using an untrainedmodel.
The models in this new strategy include:

Release Notes•

Pre-trained 2D U-Net with depth level 5 and initial filters count of 64.

•

Pre-trained 2.5D (3 slices) U-Net with depth level 5 and initial filters count of 64.

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Support for Multiple Datasets
This software release includes support for working with multiple datasets in the
Segmentation Wizard. For example, in cases in which you are working with 2D datasets it
may not be possible to label an adequate number of pixels for training and validation ona single image. Imported datasets will appear in the Data sets box, as shown below.
Segmentation Wizard panel

Do the following to load multiple datasets in the Segmentation Wizard:
1. Choose Artificial Intelligence > Segmentation Wizard on the menu bar.
The Data Selection dialog appears.
2. Click the Add Dataset button to add the required dataset(s), as shown below.

3. Choose an image or images for each input dataset.

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Deep Learning Tool
This software release includes new features and innovations for Dragonfly’s Deep
Learning Tool that are sure to boost your productivity. These include support forsparsely labeled ground truths, a dedicated remote library of ready-to-use deep models
for image enhancement and segmentation, accelerated inference, advanced tools forinspecting patches and generating augmented training sets, new architectures fortraining regression and semantic segmentation models, as well as other enhancements.
In addition to updating to TensorFlow 2.11, you should note the changes for the options
on the Model panel. These changes as described below.
Model panel options

Import/Export Zip… Lets you export and import deep models for sharing withcolleagues and the Dragonfly community (see Easy Sharing of Deep Models on page 32).
Import Remote… Lets you browse our library of ready-to-use models and download
selected models to your local library (see Ready-To-Use Deep Model Library on page 17).
Reset… This button, which previously appeared at the bottom of the Deep Learning Tool
dialog, lets you return models to their untrained state by randomizing their weights. If
you do this, your model architecture will be preserved, but you will lose the 'learnt
knowledge' of the model.
Load/Unload… These options were deprecated in this version of Dragonfly. In thisrelease, deep models are automatically loaded and unloaded. Refer to Automatic
Loading/Unloading of Models on page 29 for additional information about this change.

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Ready-To-Use Deep Model Library
Ready-to-use deep models can greatly accelerate workflows for image enhancement andsegmentation tasks. This software release includes a new utility that lets you easilybrowse our library of ready-to-use deep models and download selected models to yourlocal library. Downloaded models can be used immediately or fine-tuned by performing
additional training with your own datasets.
In the example below, a ready-to-use deep model for segmenting jawbone, dentin, andenamel was quickly fine-tuned with a small dataset of five slices and then applied to adataset of a Lutra lutra mandible.
Result of applying the ready-to-use ‘UniversalJawSeg’ model with classes for enamel, dentin, and jawbone. Original microCT
dataset acquired by Dr. Kornelius Kupczik (Universidad de Chile).

Click the Import Remote button in the Deep Learning Tool dialog, as shown below, toopen the Remote Library of Ready-to-Use Regression and Segmentation Models dialog.
Deep Learning Tool dialog

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Models are listed as(available to download) and(already installed in local library).

In addition, the general documentation provides information about each model’sarchitecture, parameters, input dimensions, the number of classes for semantic
segmentation models, as well as usage notes such as calibration requirements.
The Remote Library of Ready-to-Use Regression and Segmentation Models dialog isshown below.

Release Notes

NOTE

Ready-to-use models are filtered by version, that is, only models that can be used by the currently
installed version of Dragonfly will appear in the list.

NOTE

You need to be connected to the Internet to access the remote library of ready-to-use deep models.

NOTE

Contact us if you are interested in sharing any of your own ready-to-use deep models. You can also
upload trained models to the online platform Dragonfly Social (see Dragonfly Social on page 9).

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Ready-To-Use Models
The ready-to-use models listed below are now available for download from the Remote
Library of Ready-to-Use Regression and Segmentation Models dialog.
Ready-to-use models

Model

Description

AMDefectModelv-1.0

Trained for segmenting pores, material, and background on calibrated datasets of AM metal cubes.
Architecture: U-Net
Parameters:- Depth level: 5- Initial filter count: 64
Input dimension: 2.5D, input slice count: 3Classes: 3 (pores, material, background)
Input data must be calibrated such that the background = 0 and material = 100.
REFERENCE
Taute, C., Möller, H., Du Plessis, A., Tshibalanganda, M. and Leary, M., 2021. Characterization of
additively manufactured AlSilOMg cubes with different porosities. Journal of the Southern African
Institute of Mining and Metallurgy, 121(4), pp.143-150.

Generic-denoising_v-1.0

This model was trained for general denoising with the following settings:
Architecture: U-Net
Parameters:- Depth level: 5- Initial filter count: 64
Input dimension: 2D

UniversalBoneSeg

This model can be used for segmenting bone and was pre-trained on Bones500Kv01 and then finetuned on 15 calibrated datasets consisting of human, chimpanzee, mouse, rabbit, sheep, and squirrel
specimens. Spacing values ranged from 5 to 50 µm. The settings of the model are as follows:
Architecture: U-Net
Parameters:- Depth level: 5- Initial filter count: 64
Input dimension: 2DClasses: 2 (background, bone)
Input data must be calibrated such that background = 0 and bone = 100.

UniversalJawSeg

This model can be used for segmenting jawbone, dentin, and enamel and was pre-trained on
ORS500Kv01 and then fine-tuned on four calibrated datasets from the repository "LeRepo". Spacing
values ranged from 15 to 50 µm. The settings of the model are as follows:
Architecture: U-Net
Parameters:- Depth level: 5- Initial filter count: 64
Input dimension: 2DClasses: 4 (background, bone, dentin, enamel)
Input data must be calibrated such that background = 0 and enamel = 100.

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Updates for Segment with AI and Filter with AI
The option to download ready-to-use deep models for semantic segmentation or
regression tasks is also available in the Segment with AI and Filter with AI dialogs. Clickthe Import Remotebutton, as shown below, to open the Remote Library of Ready-to-Use Regression and Segmentation Models dialog.
Segment with AI and Filter with AI dialogs

Additional changes to the dialogs include the addition of the description for the selectedmodel.

Release Notes

NOTE

Refer to the topic Ready-To-Use Deep Model Library on page 17 for additional information about
ready-to-use deep models.

NOTE

The ‘Segment on Multiple Axes’ feature, which lets you apply a semantic segmentation model on
multiple axes and predict class labels based on a selected type of type of vote, is not available in thissoftware release. This feature will be reinstated as soon as possible.

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Additional Pre-Trained Models
You can now download additional pre-trained deep models to help you get started
quickly with implementing Dragonfly’s deep learning solutions. You should note thatstarting with a pre-trained model often provides better and faster results with smaller
training sets than using an untrained model.
The following additional pre-trained models are now available for download from the
Deep Learning Tool and from the Segmentation Wizard.
New pre-trained models

Release Notes

Model

Description

Generic Trans U-Net dl-5 ifc-32

Architecture: Trans U-Net
Parameters:- Patch size: 224- Depth level: 5- Initial filter count: 32- Use batch normalization: True
Input dimension: 2D

Generic Trans U-Net dl-5 ifc-32 slice-3

Architecture: Trans U-Net
Parameters:- Patch size: 224- Depth level: 5- Initial filter count: 32- Use batch normalization: True
Input dimension: 2,5D, input slices count: 3Generic Attention U-Net dl-5 ifc-64

Architecture: Attention U-Net
Parameters:- Depth level: 5- Initial filter count: 64
Input dimension: 2D

Generic INet fc-64

Architecture: INet
Parameters:- Filter count: 64
Input dimension: 2D

Generic Attention U-Net dl-5 ifc-64 slice-3

Architecture: Attention U-Net
Parameters:- Depth level: 5- Initial filter count: 64
Input dimension: 2,5D, input slices count: 3Generic INet fc-64 slice-3

Architecture: INet
Parameters:- Filter count: 64
Input dimension: 2.5D, input slices count: 3

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Pre-trained models are available in the Model Generator dialogs as shown below. Referto the Dragonfly 3D World Help topic ‘Pre-Trained Models’ for additional information
about working with pre-trained models.
Deep Learning Tool Model Generator dialog

Segmentation Wizard Model Generator dialog

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Sparse Labeling
As an option for accelerating workflows for training semantic segmentation models, thissoftware release includes support for sparse labeling. With sparse labeling, you shouldbe able to achieve comparable results to those obtained with densely labeled groundtruths, which can be much more laborious to obtain.

New Architectures
Several new deep model architectures are available in this software release for training
regression and semantic segmentation models. These newly added architectures arelisted below.
FreqNet… A frequency-domain image super-resolution model that can explicitly learn
the reconstruction of high-frequency details from low resolution images. Refer to R. Cai
et al., FreqNet: A Frequency-domain Image Super-Resolution Network with Discrete Cosine
Transform, rXiv:2111.10800v1, 2012 (https://arxiv.org/pdf/2111.10800.pdf) to learn more
about this model.
Multi-Level Wavelet U-Net… A U-Net style encoder/decoder network with discrete
wavelet transform (DWT) encoding and inverse wavelet transform (IWT) decoding forimage denoising and single image super-resolution. Haar wavelets are used to performthe transformations. Refer to P. Liu et al., Multi-level Wavelet Convolutional Neural
Networks, arXiv:1907.03128v1, 2019 (https://arxiv.org/pdf/1907.03128.pdf) for additional
information about this model.
Multi-Stage Wavelet Denoise… A multi-stage image denoising model based on a
dynamic convolutional block, two cascaded wavelet transform and enhancement blocks,
and a residual block. Refer to C. Tian et al., Multi-stage image denoising with the wavelet
transform, arXiv:2209.12394v3, 2022 (https://arxiv.org/pdf/2209.12394.pdf) for additional
information about this model.
Non-local Fourier U-Net… A U-Net style encoder/decoder network using non-local fast
Fourier convolution (NL-FCC) layers that has shown promising results in image superresolution. The NL-FFC layer combines local and global features in spectral domain andspatial domain. Refer to A. K. Sinha et al., NL-FFC: Non-Local Fast Fourier Convolution for
Image Super Resolution, 2022 IEEE/CVF Conference on Computer Vision and Pattern
Recognition Workshops (DOI: 10.1109/CVPRW56347.2022.00062).
Swin UNETR… A U-Net architecture combining a pure Swin transformer encoder and adeconvolutional decoder. Refer to A. Hatamizadeh et al., Swin UNETR: Swin Transformers

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for Semantic Segmentation of Brain Tumors in MRI Images, arXiv:2201.01266v1, 2022
(https://arxiv.org/pdf/2201.01266.pdf) for additional information about this model.
TransUNet… A U-Net architecture using transformer layers as a bottleneck. The
architecture has been optimized with TransBTS hyperparameters and registers. Refer to J.
Chen et al., TransUNet: Transformers Make Strong Encoders for Medical Image
Segmentation, arXiv:2102.04306v1, 2021 (https://arxiv.org/pdf/2102.04306.pdf) for
additional information about this model, as well as W. Wang et al., TransBTS: Multimodal
Brain Tumor Segmentation Using Transformer, arXiv:2103.04430v2, 2021
(https://arxiv.org/pdf/2103.04430.pdf) and T. Darcet et al., Vision Transformers Need
Registers, arXiv:2309.16588v1, 2023 (https://arxiv.org/pdf/2309.16588.pdf).

More Efficient Patch Blending for Accelerated InferenceThis software release includes more efficient patch blending for accelerated inference.
The following changes were implemented to increase inference speeds:•

Removal of the maximum patch size constraint and optimization of patch size
computation to reduce patch blending.

•Model predictions and patch blending are now done in parallel.

Refer to the following graphs for a comparison of inference speeds recorded for versions
2022.2 and 2024.1 for a semantic segmentation task on high-performance and typicalGPUs. Test datasets varied in slice sizes from 500×500 pixels to 2500×2500 pixels with aconstant depth of 782 pixels.
Inference times for high-memory and low-memory GPUs for Dragonfly versions 2022.2 and 2024.1

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Compile for Faster InferenceThis software release includes the opportunity to increase inference efficiency for lower
end GPUs with TensorFlow-TensorRT (TF-TRT), a deep-learning compiler for TensorFlow
that optimizes models for inference. This process includes five types of optimizations —precision calibration, layers and tensor fusion, kernel auto-tuning, dynamic tensor
memory, and multiple stream execution. Inference efficiency can be a concern when
deploying deep models on systems with limited GPUs because of latency, memory
utilization, and power consumption.
The option to compile deep models for faster inference is available in the Deep Learning
preferences, as shown below.
Deep Learning preferences

NOTE

TensorFlow-TensorRT (TF-TRT) is a deep-learning compiler for TensorFlow that optimizes TF models
for inference on NVIDIA devices. TF-TRT is the TensorFlow integration for NVIDIA’s TensorRT (TRT)
High-Performance Deep-Learning Inference SDK. It focuses specifically on running an already-trained
network quickly and efficiently on NVIDIA hardware.
Refer to https://docs.nvidia.com/deeplearning/frameworks/tf-trt-user-guide/index.html for more
information about TF-TRT.

The following options are available for deep model compilation:
Compile deep models for faster inference (beta)… If selected, deep models will becompiled for faster inference. If not selected, deep models will be compiled with thedefault settings as in previous versions of Dragonfly.
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Precision mode… You can select from two precision modes to compile a deep model for
inference — ‘Prioritize inference speed over model accuracy' or ‘Prioritize model
accuracy over inference speed’.
As shown in the Segmentation Comparator dialog, prioritizing inference speed overmodel accuracy may adversely affect accuracy.
Comparison of Precision modes with a ground truth

NOTE

Optimized inference usually works best for simple models, such as U-Net. It might not work well or it
may not be used to compile more complex models, such as Sensor3D. There are also only limited
benefits for optimizing inference for high-end GPUs.

New Advanced Tools
Two new advanced tools — Inspect Patches and Generate Augmented Trainset — areavailable on the Advanced Tools tab on the Model Training panel in this release.
Advanced Tools tab

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Patch Inspection
Patch size can be a critical factor when training deep models for semantic segmentationand must be selected in consideration of the specific characteristics of the input data andthe selected model architecture. Generally, patch sizes should strike a balance between
computational efficiency, contextual information, and performance for the specificsemantic segmentation task at hand.
To review patches, click the Advanced Tools tab on the Model Training panel and thenclick the Inspect Patches button. Patches will be extracted based on the current patch
size, data augmentation settings, and the selected input count and dimension. In the
Patches Review dialog, you can review training and validation patches, visually inspect
each patch, and extract additional information about all patches.
Patches Review dialog

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Generate Augmented Deep Learning Training Sets
In some cases, you may want to generate an augmented training set to increase training
speed. Augmented training sets, which include training and validation sets, aregenerated based on the current patch size, data augmentation settings, and the selected
input count and dimension.
To generate an augmented training set, open the Advanced Tools tab on the Model
Training panel and then click Generate Augmented Trainset. Training and validation
sets, as well as masks, are added to the Data Properties and Settings panel as shown.
Extracted augmented training set

NOTE

You can train models with augmented training sets without using data augmentation. This may
improve training speed but will have less diversity in augmentation.

Automatic Loading/Unloading of Models
With the goal of providing a better and more uniform experience when using the Deep
Learning Tool, this software release includes automatic loading and unloading of deepmodels. This means that there is no longer any need to load or unload models manuallyprior to training, generating previews, or applying a model. This implemented change
will also ensure that there is never more than one model loaded at any given time.
NOTE

Release Notes

The Load and Unload buttons were removed from the Deep Learning Tool dialog and the model
states Not Loaded and Ready were deprecated in this software version.

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Automatic Saving of Models
You should note that models are now saved automatically after training is completed or
stopped. This change and others, can be seen in the updates to the options available atthe bottom of the Deep Learning Tool dialog, as shown below.
Comparison between previous versions (top) and version 2024.1 (bottom)

Reset… This option, which lets you return models to their untrained state byrandomizing their weights, was moved to the Model box on the Model Overview panel.
Reload… This option, which lets you revert a model back to its last saved version, wasrenamed as Revert and is now available only on the Model Editing panel.
Save… This option, which lets you save changes to a model’s architecture, is now only
available on the Model Editing panel.

Export Training Sets in a Single File
New options in the Deep Learning Tool, circled below, let you export all your training
data, as well as import training data that was exported.
Export as AI and Import as AI buttons

Export as AI… Saves all training data inputs, outputs, and masks as slices in the
ORSObject (*.ORSObject extension) file format. Saving only data that is used for training
can significantly reduce the size of training sets.
Import as AI… Imports all training data that was exported as AI. All saved inputs,
outputs, and masks for each training set will be added to the dialog when the data isimported, minimizing any additional manual entry.
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External Processes for Deep Learning Task Processing
You should note that TensorFlow functionality, such as generating models, modeltraining, and inference, can be run as an external process in Dragonfly 3D World andDragonfly Pro for Windows installations. The main benefit of this is that memory can bereleased for subsequent processes after training or inference is completed. In previous
versions, memory used by TensorFlow could only be released by exiting Dragonfly.
You need to check the setting ‘Execute Deep Learning tasks in an external process’ to run
separate processes for TensorFlow functionality, as circled below.
Deep Learning preferences

Release Notes

NOTE

Running TensorFlow functionality as an external process is not available for Linux installations. In thiscase, the ‘Execute Deep Learning tasks in an external process’ option is disabled by default.

NOTE

This feature is only available as beta in this software release.

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Select Dataset for Visual Feedback
You can now select any dataset for visual feedback. For example, in cases in which you
are using multiple training sets to train a model or if you want to monitor training
progress on a dataset that is not part of your training data. The option for selecting adataset for visual feedback is available in the Deep Learning Tool dialog on the Inputs
tab as shown below.
Input options for Visual feedback

Validation Settings
New validation settings — Use voxel-wise sampling and Use patch-wise sampling —are available in this software release for training segmentation models. These settings,
circled below, are available on the Training panel’s Inputs tab. You should note that ineither case, the maximum number of patches will be extracted from the training set. The
difference is that in the case of voxel-wise sampling, patches will contain voxels
designated as training, validation, and test, while in the case of patch-wise sampling,voxels in sampled patches will be exclusively for training, validation, or test.
Validation settings

NOTE

Release Notes

In the Segmentation Wizard, sampling is now done exclusively voxel-wise when data is split fortraining, validation, and test. This setting cannot be changed.

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Easy Sharing of Deep Models
To enable easy sharing of deep models, this software release includes the option toexport models to a ZIP file and to import models from ZIP files. The Import Zip andExport ZIP options are circled on the screenshot.
Import Zip and Export Zip buttons

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NGSolve Integration
For quantifying fluid flow and heat dissipation in engineering and research applications,this software release includes the integration of Netgen/NGSolve. You should note that
Netgen is a mesh generation tool while NGSolve is a widely used high-performance
finite element analysis software. This implementation in Dragonfly includes a NavierStokes equation for solving transient fluid mechanics, a Stokes model to solve steadystate fluid flow, and a heat equation for steady-state conduction. In addition, new partial
differential equations and solution algorithms can be implemented easily thanks to
Dragonfly’s flexible Python interface.
Below is an example of a fluid mechanics simulation done with NGSolve in Dragonfly,
thanks to the vector field generation capability of Dragonfly.
Fluid mechanics simulation: Navier-Stokes flow around an obstacle with square cross-section

You should note that Dragonfly provides the option to assign boundary conditions usinga mesh painter (see 3D Mesh Painter on page 51).

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Choose Workflows > NGSolve on the menu bar to open the NGSolve Computation
dialog. Shown below is the Meshing tab, on which you can generate finite element
meshes from regions of interest and multi-ROIs, and the Solver tab, on which you canchoose a solver, the simulation parameters, and the required outputs.
NGSolve Computation dialog

Tutorials and implementation examples for NGSolve and the mesh generator Netgen areavailable on the NGSolve YouTube channel (https://www.youtube.com/@NGSolve).
Documentation about Netgen/NGSolve is available at https://docu.ngsolve.org/latest/.
NOTE

You can set boundary conditions for simulations for solving heat equations and other simulations
with the 3D Mesh Painter (see 3D Mesh Painter on page 51).

You can export generated meshes from Dragonfly for use in other finite element software.

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Open Fiber Segmentation
This software release includes an innovative segmentation tool for automaticallyextracting individual fibers from datasets of short fiber reinforced composites. Based on
the Open Fiber Segmentation algorithm developed by Facundo Sosa-Rey et al.
(https://github.com/lm2-poly/OpenFiberSeg), the input is a region of interest of
segmented fibers and the output is a multi-ROI in which each label is an instance of thesegmented fibers. The method only requires minimal input — selection of the fiber
parameters of minimum length, smoothing diameter, and maximum stitching distance,
as well as the convexity check parameters. The output allows for the analysis ofcorrelated length, orientation distribution, and other parameters.
Choose Workflows > Open Fiber Segmentation to open the Open Fiber Segmentation
panel, shown below.
Open Fiber Segmentation panel and segmented fibers in a 3D view

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The following parameters and settings are available for the instance segmentation offibers with the Open Fiber Segmentation plugin.
Parameters for the instance segmentation of fibers

Parameter

Description

Fiber parameters
Minimum fiber length (pixels)

Is the minimum value of fiber length, in pixels, that the algorithm
will accept as a fiber.

Fiber diameter (default unit)

Is used mostly for representation, will smooth the fibers with theselected diameter value.

Max stitching distance (default unit)

Is the maximum distance between two fibers that will beconsidered to stitch both fibers as one.

Post process all fibers

If checked, all fibers will go through the post processing
algorithm for smoothing, gap filling.

Convexity check parameters
Initial water level (pixels)

Is the distance parameter to find all the convex regions using a‘cv.watershed’ function.
NOTE If the initial water level is kept below 1.0, smaller regions
will not be erased but spurious detections may occur. If the initialwater level is above 1.0, then smaller regions will be erased but
computation time will be faster.

Convexity defect distance (pixels)

Is the distance parameter, which is defined as a convexity defectsize to find regions that are not convex, to verify if two fibers aretouching.

Preview 3D fiber extraction
Preview (number of slices in X)
NOTE

Release Notes

Will compute the specified number of slices in X as a preview.

Refer to the publication Facundo Sosa-Rey et al. OpenFiberSeg: Open-source segmentation ofindividual fibers and porosity. Composites Science and Technology, Volume 226, 2022, 109497, ISSN
0266-3538. DOI: https://doi.org/10.1016/j.compscitech.2022.109497.

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Automated Porosity Segmentation
The Dragonfly 3D World 2024.1 release provides automated thresholding methods toreliably segment porosity in single-material samples with closed pores, such as castings.
This lets you quickly and accurately prepare data for analyzing pores with parameterssuch as volume fraction percentage, pore size distribution, sphericity, and others.
Right-click the image data that you need to segment and then choose Automated
Porosity Segmentation in the pop-up menu. You can then choose a method to segment
pores. The result of an automated porosity segmentation is shown below.
Automated porosity segmentation (colored red) of an automotive part

The following options are available for applying an automated porosity segmentation.
Basic Otsu… This is the fastest available method and typically captures the largest poresin the sample but may not detect smaller ones. The Basic Otsu method includes creatinga region of interest for the air and void space based on the lower Otsu threshold for thefull 3D dataset. This ROI is then refined to remove segmented exterior air using the‘Process Islands’ tool to remove the largest component within the ROI.
Segmentation of pores according to the Basic Otsu method for a casting dataset

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Advanced Otsu… This fast and reliable method typically captures smaller pores than the
Basic Otsu method and creates two regions of interest that describe the pores andmaterial in the sample respectively.
In this method, a region of interest is first defined for the material using the upper Otsu
threshold. Internal pores are then filled and a dilate function is applied with a 3D kernelof size 5 to increase the ROI size beyond the edge of the segmented material. The ROI isthen split at the Otsu threshold to effectively separate the pores from the segmented
material. The layer around the material in the resulting ‘Porosity ROI’ is then removed
with the ‘Process Islands’ tool.
Segmentation of pores according to the Advanced Otsu method for a casting dataset

Sobel Threshold… This advanced method can find the smallest pores in a sample andcan be applied to challenging data. For example, in cases in which brightness variations
are present across the image and in cases of low signal to noise.
For the segmentation step, this method uses Sobel image filtering without any presmoothing to highlight the edges of pores. This initial ROI is then ‘closed’ and innerareas are subsequently filled, while the edge that defines the material is removed.
Segmentation of pores according to Sobel method for a casting dataset shown

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Compute Measurements
The option to compute measurements from multi-ROIs, meshes, graphs, and vector
fields is now available in the Compute Measurements dialog. You should note that this
dialog simply replaces the Scalar Generator dialog that was available in previous versionsof Dragonfly. In addition, new metrics — ‘Radiomics with Dataset’ and ‘Longest Length’
and — are available in this software release.
Right-click a multi-ROI, mesh, graph, or vector field and then choose Compute
Measurements to open the dialog shown below.
Compute Measurements dialog

NOTE

Release Notes

The option to compute measurements and statistical properties in the Object Analysis dialog wasdeprecated in this software version. Measurements can only be computed in the Compute
Measurements dialog as of this software release.
Refer to the screenshots below for a comparison of the Object Analysis dialog in version 2022.2 (onthe left) and in version 2024.1 (on the right).

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Radiomics
A new set of measurements — Radiomics with Dataset — is available in this software
release for extracting quantitative features from images using data-characterization
algorithms. Once features are extracted, computational algorithms and statistical models
can be applied to analyze and interpret the extracted data.
The radiomic features now available in Dragonfly’s Compute Measurements dialog canbe divided into groups that include descriptors of the relationships between image
voxels, as well as local texture variations and gradients. Right-click a multi-ROI and thenchoose Compute Measurements to open the dialog and access the Radiomics withDataset set of measurements.
Compute Measurements dialog

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The various features that can be extracted using radiomics, on either an original orderived image, can be subdivided into the following classes:
Gray Level Co-occurrence Matrix (GLCM)… These 24 features provide for quantitativeand objective analyzes of image textures.
Gray Level Dependence Matrix (GLDM)… The 14 features of the GLDM provide
information about the coarser-scale texture properties of an image compared to GLCM
features. The strength and direction of gray-level dependencies captured can beparticularly useful for analyzing patterns characterized by variations in gray-level values.
Gray Level Run Length Matrix (GLRLM)… These 16 features capture information about
the spatial arrangement and distribution of pixel runs in an image. Providing insights into
the coarseness, regularity, and complexity of underlying textures, these features canidentify regions with specific texture patterns for image segmentation.
Gray Level Size Zone Matrix (GLSZM)… The 16 features of the GLSZM provide
information about the distribution and characteristics of connected regions within animage by capturing the heterogeneity, fragmentation, and spatial organization of texture
patterns. Theses features can identify regions of interest with specific size and texture
characteristics for image segmentation.
Neighbouring Gray Tone Difference Matrix (NGTDM)… The five features of the NGTDM
provide information about the local texture variations and gradients in an image by
capturing the differences in gray-level values between adjacent pixels to reveal fine-scaledetails of texture patterns. They can also identify regions of interest with specific local
texture variations for image segmentation.
Gray Level Co-occurrence Matrix (GLCM)
The Gray Level Co-occurrence Matrix (GLCM) is a widely used method in radiomics for
extracting texture-based features. GLCM quantifies the spatial relationships between
pairs of pixels based on their gray-level intensities within an image and is constructed bycounting the number of occurrences of pixel pairs with specific intensity values and their
spatial relationships at a selected offset. This process is repeated for different offsets ordirections to capture various spatial dependencies within the image, as described below.
Contrast… Measures the local intensity variations between neighboring pixels.
Energy (or Angular Second Moment)… Represents uniformity or image homogeneity.
Homogeneity… Measures the closeness of distribution of pixel pairs to the diagonal
elements of the GLCM.

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Entropy… Quantifies the randomness or uncertainty of the pixel-pair distribution.
Correlation… Measures the linear dependency between gray levels of neighboring pixels.
NOTE

Refer to https://pyradiomics.readthedocs.io/en/latest/features.html#module-radiomics.glcm for more
information about GLCM.

Gray Level Dependence Matrix (GLDM)
The Gray Level Dependence Matrix (GLDM) is another method used in radiomics toextract texture-based features. GLDM characterizes the spatial dependencies andrelationships between gray-level values in an image. The GLDM is constructed bycounting the number of occurrences of pixel pairs with specific gray-level differences andtheir corresponding spatial relationships at the selected distance. The statistical measuresto quantify the texture properties of an image include:
Contrast… Measures the local intensity differences between neighboring pixel pairs.
Dissimilarity… Quantifies the average difference in gray-level values between
neighboring pixel pairs.
Homogeneity… Measures the closeness of distribution of pixel pairs to the diagonal
elements of the GLDM.
Angular Second Moment… Represents the uniformity or homogeneity of the image.
Entropy… Measures the randomness or uncertainty of the pixel pair distribution.
Correlation… Measures the linear dependency between gray levels of neighboring pixels.
NOTE

Refer to https://pyradiomics.readthedocs.io/en/latest/features.html#module-radiomics.gldm for more
information about GLDM.

Gray Level Run Length Matrix (GLRLM) Features
The Gray Level Run Length Matrix (GLRLM) is a radiomics method used characterize thelengths and frequencies of consecutive runs of pixels with the same gray-level value inan image. The GLRLM is constructed by counting the number of occurrences ofconsecutive runs of pixels with specific gray-level values and their corresponding lengths
in a chosen direction. GLRLM features include:
Short Run Emphasis (SRE)… Measures the distribution of short runs, indicating thepresence of fine textures.
Long Run Emphasis (LRE)… Represents the distribution of long runs, indicating thepresence of coarse textures.
Gray-Level Nonuniformity (GLN)… Quantifies the variation in gray-level values within
the runs, reflecting the heterogeneity of texture.
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Run Length Nonuniformity (RLN)… Measures the variation in the lengths of the runs,
indicating the variability of texture patterns.
Run Percentage (RP)… Represents the proportion of the image covered by runs of a
specific gray-level value.
NOTE

Refer to https://pyradiomics.readthedocs.io/en/latest/features.html#module-radiomics.glrlm for more
information about GLRLM.

Gray Level Size Zone Matrix (GLSZM) Features
The Gray Level Size Zone Matrix (GLSZM) is a radiomics method used to extract texturebased features from images by characterizing the size and spatial distribution ofconnected regions with the same gray-level value. The GLSZM is constructed bycounting the number of occurrences of connected regions with specific sizes and theircorresponding gray-level values. Once the GLSZM is constructed, various statisticalmeasures can be derived from it to describe the texture properties of the image.
Small Area Emphasis (SAE)… Emphasizes the contribution of small, connected regions to
the texture analysis.
Large Area Emphasis (LAE)… Emphasizes the contribution of large, connected regions to
the texture analysis.
Gray-Level Nonuniformity (GLN)… Quantifies the variation in gray-level values within
the connected regions.
Zone Percentage (ZP)… Represents the proportion of the image covered by connected
regions of a specific gray-level value and size.
NOTE

Refer to https://pyradiomics.readthedocs.io/en/latest/features.html#module-radiomics.glszm for
more information about GSZM.

Neighbouring Gray Tone Difference Matrix (NGTDM) Features
The Neighbouring Gray Tone Difference Matrix (NGTDM) characterizes the local
variations in gray-level values between neighboring pixels in an image. To compute
NGTDM features, a specific distance or neighborhood is defined, and the gray-level
differences between the central pixel and its neighbors within this distance arecalculated. The NGTDM is constructed by counting the number of occurrences of specificgray-level differences and their corresponding frequencies.
Once the NGTDM is constructed, various statistical measures can be derived from it todescribe the texture properties of the image. Common NGTDM features include:

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Contrast… Measures the local variations in gray-level differences between the central
pixel and its neighbors.
Complexity… Quantifies the complexity or randomness of the gray-level differences.
Strength… Represents the overall magnitude or strength of the gray-level differences.
Busyness… Reflects the level of local variations or activity in the image.
NOTE

Refer to https://pyradiomics.readthedocs.io/en/latest/features.html#module-radiomics.ngtdm for
more information about NGTDM.

Longest Length
In some cases, you may want to compute the longest length of each labeled object in a
multi-ROI. For example, as part of the analysis of a fibers dataset that was segmented
with the new Open Fiber Segmentation plugin (see Open Fiber Segmentation on page35). This option is available in the Compute Measurements dialog for multi-ROIs, as
shown in the following screenshot.
Compute Measurements dialog

NOTE

Release Notes

If required, you can select the ‘Use thickness for Dijkstra weights’ option for the computation.

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When the computation is complete, select ‘Longest Length’ in Measurement drop-downmenu in the Scalar information box, as shown below, to review the measurements.

Phi and Theta Computations
This software release includes the option to compute Phi and Theta in both the world
coordinate and image coordinate systems, as well as to compute Phi and Theta of theminimum, middle, and maximum Eigen vectors.
Results of Phi computations in image and world coordinates

NOTE

The option to compute Phi and Theta in the world coordinate system lets you directly compare thePhi and Theta of datasets that are spatially oriented differently.

These new computations are available in the Basic Measurements section of theCompute Measurements dialog, as shown in the following screenshot.

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Compute Measurements dialog

NOTE

Release Notes

Computations of Phi and Theta are equivalent to computations of Phi of Minimum Eigen Vector and
Theta of Minimum Eigen.

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Analyze and Classify Measurements
This software release includes the production version of Dragonfly 3D World’s Analyze
and Classify Measurements module, which lets you import scalar data from multi-ROIs,
meshes, graphs, and vector fields for the cross-table analysis of feature vectors. You canapply arithmetic operations to the data, add cross-reference measurements, plot
histograms to gain insights into the distribution and characteristics of the data,
categorize objects according to different criteria, calculate statistics or ratios based onclassifications, and more.
Right-click a multi-ROI, mesh, graph, or vector field that contains scalar values and thenchoose Analyze and Classify Measurements in the pop-up menu to open the Analyze
and Classify Measurements dialog, shown below.
Analyze and Classify Measurements dialog

You can choose the object(s) you need to analyze in the Choose an Object to Import
Scalar Values From dialog. You can add data from additional objects to the dialog byright-clicking ‘Objects’ on the left sidebar and then choosing Add in the pop-up menu.
Tables panel… The Tables panel lists all the tables that were created by importing thescalar data of objects and all derived tables in a tree menu. In addition, you can apply
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filtering to create sub-groups that match specific criteria. For example, data can befiltered within a range, by percentile, or by logical expressions such as less than, equal to,
greater than, and so on.
Operations… The tools at the top of the dialog let you to compute new measurements,
start a histogram analysis, apply arithmetic operations, add cross-reference
measurements, compute basic statistics, and export data in the CSV file format.
Operations

Operation

Icon

New Measurement

Description
Opens the Compute Measurements dialog, shown below, in which you can compute newmeasurements for the object(s) that you are analyzing.

See Compute Measurements on page 37 for information about computing measurements
for multi-ROIs, meshes, graphs, and vector fields.
Histogram Analysis

Opens the Histogram Analysis dialog, in which you can plot and analyze the distribution ofscalar data contained in an object, compute basic statistics, as well as create classes byselecting instances in the data that match some criteria.
See Histogram Analysis on page 50 for information about updates to the dialog.

Arithmetic
Operations

Opens the Arithmetic Operations dialog, shown below, in which you can apply arithmetic
expressions to one or more inputs to modify measurements or to compute new ones.

NOTE The convention for identifying each input within an expression is simply ‘A’ for the
first input, ‘B’ for the second input, ‘C’ for the third, and so on. For example, to get the sumof the first input and the second input, the expression would be ‘A+B’. More complex
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Operation

Icon

Description
expressions can also be written, such as ‘D*[(A+B)+(2*A-1.8*B)]/C’. You can also include
NumPy functionalities in an expression, such as ‘np.sin’ and ‘np.cos’.

Cross-Reference
Measurement

Opens the Cross-Reference Measurement dialog, shown in the following screenshot. You
can add cross-reference measurements to a table in this dialog.

As shown in the following example, the measurements of 'Pores Count' and 'Pores Volume'
were added to a Solder-balls table from the pores labels that were cross-referenced to thesolder-balls.

Basic Statistics

Opens the Basic Statistics dialog, in which you can compute basic statistics, such as themean, median, minimum, maximum, and standard deviation of computed measurements.
These statistics can be added to the original table as metadata or as a new derived table.
These output options are available in the Basic Statistics dialog, as shown below.

Export as CSV

Exports the current table or selected columns in the comma-separated values (*.csv
extension) file format.

Tables… All imported, filtered, and derived scalar data is organized in tables, withcolumns representing features and rows representing components or instances within
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the scalar data, such as the labeled objects in a multi-ROI, the faces or vertices of a meshor graph, or the vectors of a vector field. Features can be length, area, volume, angle,
circularity, gray-level intensity value, or anything else that is useful for the application athand, and every row can be interpreted as a feature vector.

Histogram Analysis
Updates for Histogram Analysis include the option to plot 2D histograms using Cartesianor polar coordinates, as well as a Classification tab on which you can create classes byselecting instances in the data that match some criteria. Instances can be added toclasses on 1D histograms with the Range Selector tool and they can be selected bypainting on 2D histograms and polar plots. You should note that in the case of polarplots, painting is done on annular sectors.
Click the Histogram tool on the Data Properties and Settings panel or click theHistogram Analysis button on the Analyze and Classify Measurements dialog to open
the Histogram Analysis dialog, as shown in the following screen captures.
Histogram Analysis dialog

MeshesNew options are available in this software release for working with meshes. These
include a 3D Mesh Painter, with which you can edit a mesh’s scalar values or create a

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new scalar slot for painting, the option to smooth meshes with a constraint, adjustable
thickness for outlined meshes, and mesh capping on all faces.

3D Mesh PainterFor some 3D modeling applications, you may need to edit the scalar values of a mesh or
create a new scalar slot for painting values. These options, which are now available inDragonfly’s 3D Mesh Painter, let you:•Set boundary conditions for simulations, such as for solving heat equations with
NGSolve (see NGSolve Integration on page 33).

•

Correct any inaccurate values contained within a scalar mesh.

•Define where an operation is applied, such as mesh smoothing with a constraint (seeMesh Smoothing with Constraint on page 52).

Choose Utilities > 3D Mesh Painter to open the 3D Mesh Painter panel, shown below.
3D Mesh Painter panel and painted mesh

You can do the following with the 3D Mesh Painter:•

Paint on vertices or faces.

•

Paint on an existing scalar slot or create a new slot for painting.

•

Paint within the range of another scalar slot. For example, when constraining painting
to the boundary faces of a solid mesh. You should note that the initial value set forboundary faces is -1.

The following configured actions are available for working with the 3D Mesh Painter.
Configured actions for the 3D Mesh PainterActionRelease Notes

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ActionDefault Setting

Decrease brush size (in 3D Mesh Painter)

Mouse wheel down

Increase brush size (in 3D Mesh Painter)

Mouse wheel up

Paint vertex or face (in 3D Mesh Painter)

Left Ctrl + Left mouse

Mesh Smoothing with Constraint
This software release includes the opportunity to smooth meshes using a selected scalarslot as the per-vertices Laplacian relaxation parameter, as well as a selected degree ofsmoothing. As shown in the example below, smoothing with a constraint lets you limit
smoothing to selected areas of a mesh.
Original mesh (left) and the original mesh smoothed with constraint (right)

NOTE

The scalar slot selected for smoothing with constraint should have vertex values between 0 (norelaxation) and less than 2. You should note that a value to 1 corresponds to ‘normal’ smoothing. In
most cases, it is best to create a scalar slot specifically for smoothing with the 3D Mesh Painter (referto the topic 3D Mesh Painter on page 51 for information about mesh painting).

Select the required mesh and then click Apply with Constraint in the Smooth mesh box
on the Data Properties and Settings panel.
Smooth mesh box

You can then choose the required scalar slot in the Choose a Scalar Values Slot dialog.
Choose a Scalar Values Slot dialog

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Remeshing with Poisson Surface Reconstruction
This release includes an implementation of a Poisson surface reconstruction algorithm
for reconstructing triangle meshes from sets of oriented 3D points. This utility(vtkPoissonReconstruction) was adapted from the original implementation by Kazhdan,
Bolitho, and Hugues (2006). You should note that Poisson surface reconstruction offers
several advantages for remeshing — it is a global solution that considers all the data at
once without resorting to heuristic partitioning or blending, and it can create verysmooth surfaces that robustly approximate noisy data. An example of remeshing with
Poisson surface reconstruction is shown below.
Original mesh (on left) and remeshed with Poisson surface reconstruction (on right)

Right-click the mesh you want to reconstruct and then choose Remesh with Poisson
Surface Reconstruction to open the Mesh Smoothing dialog, shown below. You canchoose the parameters for creating the new mesh in the dialog.
Mesh Smoothing dialog

NOTE

Release Notes

Refer to the publication Michael Kazhdan, Matthew Bolitho, and Hugues Hoppe, Poisson Surface
Reconstruction, Eurographics Symposium on Geometry Processing (2006), Konrad Polthier, Alla
Sheffer (Editors) for additional information about Poisson surface reconstruction. This publication isavailable at: https://hhoppe.com/poissonrecon.pdf.

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Mesh Capping
In previous software versions, it was only possible to cap clipped meshes on the active
Clip box face. In this software version, mesh capping is applied to all clipping faces of theClip box whenever Cap mesh on clipping is selected as a 3D setting.
Cap mesh on clipping

In the example below, a thickness mesh was clipped on two faces and then capped.
Capping applied to a clipped mesh

Adjustable Thickness for Outlined Meshes
The Thickness setting in the 3D settings box on the Data Properties and Settings panel,shown below, is now applicable to meshes shown in Outlined mode. In previous
versions, this setting was only available for meshes shown in Wireframe mode.
Thickness slider

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ROI PainterThis software release introduces ‘color modes’ for labeling regions of interest and multiROIs with the ROI Painter tools. Examples of the two available color modes — Singlecolor mode and Two-color mode — are shown in the screen captures below.
ROI Painter color modes

Single-color mode… In this mode, which appears whenever one ROI or one multi-ROI
class is selected, you can use the Brush tools to label any voxel by holding down Left Ctrl
(or your configured ‘Add with key’). Holding down Left Shift (or your configured ‘Remove
with key’) lets you remove voxels from the selected ROI or multi-ROI classes. You shouldnote that in the case of multi-ROIs, this a change from previous versions. Previously,
voxels were only removed from the selected class when painting in Single-color mode. In
version 2024.1, voxels will be removed from all classes.
Two-color mode… In this mode, which appears whenever multiple ROIs or multiple
multi-ROI classes are selected, only the labeled voxels belonging to the selected ROIs (ifExclusive mode is selected) or to the selected classes of a multi-ROI will be affected when
painting. Holding down Left Ctrl (or your configured ‘Add with key’) will relabel voxels
from ‘B’ and the shown intermediary colors to ‘A’. Holding down Left Shift (or yourRelease Notes

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configured ‘Remove with key’) will relabel voxels from ‘A’ and the shown intermediarycolors to ‘B’. The identification of selected ROIs and selected multi-ROI classes as A, B,
and C is shown in the following screenshots.
Multiple ROIs selected (left) and multiple multi-ROI classes selected (right)

Two-color mode can be particularly useful whenever the results of an automatedsegmentation may need manual editing to assign labeled voxels from one or more
multi-ROI classes to another class or from one ROI to another. As shown below, only thelabeled voxels highlighted in yellow are re-assigned to those highlighted in blue when
painting in Two-color mode.
Two-color mode painting

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ROI Painting Preferences
A new set of preferences is available for controlling painting behavior in 3D views.
Choose Views > 3D Settings in the Preferences dialog to view the ‘ROI Painting in 3D’
preferences, shown below.
3D Settings preferences

Draw only after a stroke is finished… If selected, updates to the region of interest will
only appear after the brush is released.
Continuously draw while painting… If selected, updates to the region of interest willappear in real time.

Thresholding
In this software release, the selected range on the ROI Tools panel defines not only theminimum and maximum threshold values, but now also serves as a proxy for calculating
the lower and upper Otsu ranges. This means that the Otsu value will be computed
within the selected range whenever the Lower Otsu or Upper Otsu button is clicked. In
previous versions, the Otsu value was computed from the full data range, regardless ofthe currently selected range.
Range box

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3D Rendering
This software release includes multiple light sources and material presets to make your3D renderings even more sensational. In addition, optimizations implemented in version
2024.1 are sure to result in improved performance and a reduced memory footprint
when rendering multiple volumes, particularly for users who typically deploy correlative
workflows and work with large datasets.

Multiple Lights
Dragonfly 3D World includes multiple lights for achieving a higher degree of photo
realism and better shadow detail for 3D renderings. No longer limited to one-point
lighting setups, you can now setup a combination of light sources to either draw
attention to a special part of the scene or to represent the natural properties of a scene.
As shown in the example below, multiple light sources can help create a sense of depth,
contrast, and color to enhance the shape, texture, and details of 3D renderings.
Multiple lights applied to a scene with a virtual floor

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Multiple lights are available on the Lighting panel, as shown below.
Lighting panel

You should note that the lighting source position and light intensity are particular toeach light, which means that each can have its own source position and intensity setting.
However, you should also note that shadow strength and shadow opacity are universal.
Changing these settings for one light will reset the setting(s) for all lights.
NOTE

Release Notes

You can switch a light source position to ‘Back’, as shown below, to apply back lighting with one ormore of the selected lights.

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Lighting and Shadows
For this software release, the options ‘Use lighting’ and ‘Use shadows’ options wereseparated in the Lighting panel. This lets you apply lighting without any shadows for a
specific purpose and can also help with 3D rendering in cases in which you are using a
low-end GPU that might struggle to render shadows.
Dataset without lighting (left), with lighting (center), and with lighting and shadows (right)

Several additional improvements for shadows are also included in this software release.
These include:•

Applying shadows in correlative workflows, as shown below.

•

All visuals can now cast shadows, for example, volumes on meshes, graphs on
datasets, and so on.

•

Release Notes

Using shadows with reflective floors is now more accurate.

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Tone Mapping
Careful mapping of high dynamic range (HDR) values to low dynamic range (LDR) values
can be an important part of a 3D rendering pipeline. In this software release, this is now
done using ACES (Academy Color Encoding System) filmic tone mapping to avoid
saturating shadows and highlights. As shown in the example below, tone mapping canhelp you reveal the full details of your renderings and give them a hyper realistic look.
Original rendering (left) and with tone mapping applied (right)

NOTE

Tone-mapping consists of adjusting the tonal values of an image with a high dynamic range byscaling the dynamic range down while attempting to preserve the full details of the original image.

The option to apply tone mapping is available in the 3D settings box on the Data
Properties and Settings panel, as shown below.
3D settings box

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Suppression of Moiré Effects
This software release includes improved filtering to remove moiré effects from both low
resolution and high-resolution datasets. This 2D filter has no cost to apply and should
work well with most graphic cards.
Below are examples of the suppression of moiré effects on low-resolution and highresolution datasets. Images on the left are from previous versions of Dragonfly, while
images on the right are from the 2024.1 release.
Suppression of moiré effects on a low-resolution dataset

Suppression of moiré effects on a high-resolution dataset

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Material Presets
This software release includes phase functions so that you can assign a material, such asaluminum, bronze, cobalt, copper, concrete, and gold, to a dataset or region of interest.
Copper preset applied to a copper lattice (left) and multiple material presets applied to a series of ROIs (right)

Material settings for images — Aluminum, Bronze, Cobalt, Concrete, Copper, Gold,
Plaster, and Sand —are available in the Presets drop-down menu in the 3D presets box,
circled below. The option to apply material settings to regions of interest is available in
the Opacity and color box.
3D presets for images (left) and Opacity and color settings for ROIs (right)

NOTE

Release Notes

Additional Presets available in version 2024.1 include Bones ES, Lights x2, Monkey, Reptile, Sand 3,
and Sand 4. You should also note that some previously available presets have been updated.

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Settings for ROIs and Multi-ROIs
New and updated settings — ‘Smoothed (global)’ and ‘Edge contrast (global)’ — areavailable in this software release for visualizing regions of interest and multi-ROIs in 3D.
These settings are available in the Opacity and color box, as shown below.
Opacity and color box

Smoothed (global)… This global setting was updated for version 2024.1 to use reflected
light and an increased kernel size for improved real-time smoothing.
Original ROI (left) and smoothed ROI (right)

Edge contrast (global)… This global setting produces an image in which the edges areemphasized, and other features are rendered semi-transparent.
Original ROIs (on left) and with edge contrast applied (on right)

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Planes
Exploded views and additional settings are available in this software release for working
with planes.

Exploded Views
The new explosion options for visual planes lets you sectionalize 3D renderings to reveal
the interior of a sample. You should note that you can include exploded views inanimated sequences and generate high-resolution images with this visual effect. An
exploded 3D rendering is shown in the illustrations below.
Dataset exploded by ‘Translation’ on the left and by ‘Rotation (screw)’ on the right

The Explosion panel, shown below, is available for selected planes in the Data Properties
and Settings panel. You can choose the type of explosion — Translation, Rotation(hinge), or Rotation (screw) — in the drop-down menu, as well as the degree oftranslation or rotation with the Explosion slider. You can also invert rotation explosions.
Plane settings

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Show GrayscaleThis software release includes a new ‘Show grayscale’ method that can be applied toclipped planes. In some cases, this may provide better results than the ‘Show data’
method that is also available in previous and current versions of Dragonfly.
A comparison of the ‘Show data’ and ‘Show grayscale’ options is illustrated below.
Plane with Show data option applied (left) and with Show grayscale (right)

The ‘Show grayscale’ option and associated window center and width controls are circled
below.
Plane settings box

You can adjust the center and width values independently of the window leveling values
applied to the dataset.

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3D View Controls
New Axis indicator modes and controls are available in version 2024.1 for manipulating
3D views.

Automatic View Alignment
This software release includes the option to automatically align 3D views with the X, Y, or
Z axis by double-clicking on the Axis indicator, as shown below.
Axis indicator

NOTE

You can also align 3D views by double-clicking the Axis indicator whenever it is shown in Arrow,
Gyroscope World, or Gyroscope View mode.

Constrain Pan Movements
In some cases, you may want to constrain pan movements to a single axis. For example,
when creating an animated sequence. This software version includes a new set of
configured actions that let you constrain pan movements to the X, Y, or Z axis.
After changing the axis indicator to ‘Arrows’, you can then activate any of the directional
arrows and then pan within a constrained direction.
Pan movement constrained to the X axis

The following configured actions and their default settings are available for constraining
pan movements.

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Configured actions for constraining Pan movements

ActionDefault Setting

Constrain Pan movements to X axis (Infinite mode)

Left mouse

Constrain Pan movements to Y axis (Infinite mode)

Left mouse

Constrain Pan movements to Z axis (Infinite mode)

Left mouse

Gyroscope Axis Indicators
Additional Axis indicator modes — ‘Gyroscope World’ and ‘Gyroscope View’ — areavailable in this software release. You can choose an Axis indicator mode in the 3D viewcontextual menu, as shown below.
3D view contextual menu

You can constrain Track movements to a selected axis, as shown below. You should notethat when an axis is selected, it turns yellow.
Track movement constrained to rotation in the X axis

The following configured actions and their default settings are available for constraining
Track movements.
Configured actions for constraining Track movements

Release Notes

ActionDefault Setting

Constrain Track movements to X axis (Infinite mode)

Left mouse

Constrain Track movements to Y axis (Infinite mode)

Left mouse

Constrain Track movements to Z axis (Infinite mode)

Left mouse

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New preferences for the Axis Indicator
A set of new 3D preferences, shown below, are now available for selecting ‘Gyroscope(World)’ or ‘Gyroscope (View)’ as the default appearance of the Axis indicator.

CT Reconstruction
An additional pre-processing filter is available in this software release for reconstructing
cone-beam and parallel-beam projections in the CT Reconstruction module. A variant ofadaptive histogram equalization, contrast limited adaptive histogram equalization(CLAHE) is now available for improving local contrast and enhancing the definition ofedges in each region of reconstructed images.
Choose Workflows > CT Reconstruction on the menu bar to open the CT
Reconstruction dialog. The CLAHE pre-processing option is circled below.
Pre-processing options in the CT Reconstruction dialog

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Organizer
You should note that the Organizer now boasts enhanced compatibility with Dragonfly
enterprise solutions, such as VizServer, thanks to changes to the backend. The backend is
now managed by Neo4j, which is a graph database management system developed by
Neo4j, Inc. Go to https://neo4j.com/ to learn more about this administrator tool.

Image Registration
As an update for image registration, this software release provides an initial score forrigid registrations, which is computed prior to applying any registration. As shown below,
the starting point for a rigid registration task is indicated in the Registration informationbox.
Image Registration dialog

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Natural Language SearchThis software release includes an implementation of natural language search (NLS), whichlets you query the Dragonfly technical documentation using everyday language. Insteadof entering keywords in the Search bar you can pose a question using regular spoken
language that describes the information you are looking for. For example, if you want toknow how to compute a thickness mesh, a keyword-based search query might be‘thickness mesh compute’. But if you are searching using natural language, you mightphrase the query as ‘How can I compute a thickness mesh?’.
An example of a basic question entered in the Search bar is shown below.
Search bar

You should note that entered search queries get translated for the search engine using
an advanced technique called natural language processing (NLP). Results are returned ina pop-up window with links to applicable topics, as shown below.
Search results

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Importing RAW Data
To streamline workflows, you can now sequentially load RAW files without manually
re-entering settings for size, data type, and image spacing. In other words, the Image
Loader, shown below, will 'remember' your selections when you import RAW files of thesame size.
Settings for importing RAW data

File Export
This software release provides the option to override the set Miscellaneous preference
for compressing files — ‘No compression’ or ‘zstd’ — whenever an ORS Object or
Session file is exported.
This option is available in the Export to File dialog, circled on the following screenshot, as
well as in the Save Session File dialog.

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Export to File dialog

Image Filtering
You should note that some of the set parameter boundaries for image filters were
removed in this software release. This provides more freedom to select values according
to the nature of any dataset that needs to be processed.
As shown in the following example, the previous upper limit of ‘21’ for the ‘window_size’
parameter for the Sauvola adaptive thresholding filter was removed.
Sauvola thresholding filter options

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Developer Tools
A number of updates are available in this software release for the Widget Observer,which is a dedicated debugging tool for investigating the properties of objects that arepart of the Dragonfly UI. New features include tools that let you dynamically highlightselections, select highlight colors and opacity for probed objects, extract information
about spacers and layouts, as well as the option to probe elements within a graph.
Choose Developer Tools > Widget Observer to open the Graph Observer dialog. The
new Highlight/Probe tools are circled below.
Graph Observer dialog

NOTE

Release Notes

The Single Widget Observer dialog was deprecated in this software release.

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You should also note that a Stylesheet Editor, as shown below, is now available formodifying the properties of a selected object.
Stylesheet Editor (on left)

Tutorial Videos
You should note that our YouTube series of videos aimed at developers, which include
tutorials about the Object Model Observer, Reimporter, Callback Observer, Context
Observer, Docstring Analyzer, and others, was updated for this software release. You canview the full playlist of developer videos at:
www.youtube.com/playlist?list=PLbYyniU4wPOuPZmmjKrWYRT3g8wEmXEUz

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Dragonfly 3D World

Release Notes Version 2024.1

Menu Bar Changes
The following new items are available on the menu bar in Dragonfly 2024.1. Otherchanges are also listed here.

Workflows Menu
The following changes, listed below, were implemented in the Workflows menu.
Workflows menu changes

Item

Description

NGSolve

Opens the NGSolve Computation dialog, in which you can quantify fluid flow and heatdissipation in engineering and research applications (see NGSolve Integration on page 33).

Open Fiber Segmentation

Opens the Open Fiber Segmentation dialog, in which you can perform instance segmentationof the fibers in a region of interest (see Open Fiber Segmentation on page 35).

Utilities Menu
The following changes, listed below, were implemented in the Utilities menu.
Utilities menu changes

Item

Description

Analyze and Classify
Measurements

Opens the Analyze and Classify Measurements dialog, in which you can import scalar datafrom multi-ROIs, meshes, graphs, and vector fields for the cross-table analysis of feature
vectors (see Analyze and Classify Measurements on page 47).

3D Mesh PainterLets you edit the scalar values of a mesh (see 3D Mesh Painter on page 51).

Help Menu
The following changes, listed below, were implemented in the Help menu.
Help menu changes

Item

Description

Connect to Dragonfly Social As

Lets you connect to the online platform Dragonfly Social (see Dragonfly Social on page 9).

Manage Dragonfly Social
Accounts

Lets you create and manage accounts to access Dragonfly Social (see Dragonfly Social on
page 9).

Dragonfly Forums (Online)

This item and the online forums are deprecated in this software release. You can now use
Dragonfly Social to ask questions, find answers, and share your experiences with otherDragonfly users (see Dragonfly Social on page 9).

Request Write Access to Forums

This item is deprecated in this software release.

Recover Forums Write AccessThis item is deprecated in this software release.

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Changes to Contextual Menus
The following changes for the contextual menus available in the Data Properties andSettings panel are implemented in Dragonfly 2024.1.

Dataset Contextual Menu
The items listed below are now available in the Dataset pop-up menu.
Dataset pop-up menu changes

Item

Description

Automated Porosity
Segmentation

Provides automated methods — Basic Otsu, Advanced Otsu, and Sobel Threshold —to reliably segment porosity (see Automated Porosity Segmentation on page 37).

New ROI from Marked Slices

Creates a new region of interest in the same size and shape as the selected dataset andin which each marked slice is fully segmented.

Extract Slab Around Center

Extracts a specified number of rotated datasets (slabs) along the object’s central axis.
You can a select the number of slabs that will be extracted and the number of slices per
slab in the dialogs shown below.

An example of 5 extracted slabs is shown below

Generate a Multi-ROI from
Multiple Otsu Splits

Release Notes

Lets you separate the selected image into several different classes in a multi-ROI, witheach one obtained according to the intensity of the gray levels within the image. The
number of classes, which determine how many thresholds are calculated, is selectable inDragonfly 3D World, Version 2024.1
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Item

Description
the Classes dialog, as shown below.

NOTE If the number of classes is four, then three threshold values will be calculated togenerate the multi-ROI.
Create Local Maximum Map
Image

Lets you create an image that shows the coordinates of local peaks. You can enter athreshold for computing the local maximum map in the Threshold dialog, shown below.

Create Local Maximum Map ROI

Lets you create a region of interest in which only local peaks are labeled. You can entera threshold for computing the local maximum map in the Threshold dialog.

Create Ridge Image

Lets you create a ridge image.

Create Ridge ROI

Lets you create a region of interest in which detected ridges are labeled.

Open Image Quality Metrics

Now available for multi-slice datasets. Was previously only available for single-slice (2D)
datasets.

Generate Phi Theta Orientationfrom Vector Field in X, Y, Z
ImagesLets you generate images with scalar values of Phi and Theta orientations. You need to
select at least two images to access this option and then choose the input datasets in
the Choose Vector Field Component dialog, shown below.

NOTE You can use PuMA to compute the required X, Y, and Z images by selecting‘Orientation detection’ as the computation type on the Solver tab.

ROI Contextual Menu
The items listed below are now available in the Region of Interest pop-up menu.
Region of interest pop-up menu changes

Item

Description

Extract Slab Around Center

Extracts a specified number of rotated ROIs (slabs) along the object’s central axis. You cana select the number of slabs that will be extracted and the number of slices per slab.

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Multi-ROI Contextual Menu
The items listed below are now available in the Multi-ROI pop-up menu.
Multi-ROI pop-up menu changes

Item

Description

Analyze and Classify
Measurements

Opens the Analyze and Classify Measurements dialog, in which you can import scalardata from multi-ROIs and other objects for the cross-table analysis of feature vectors (seeAnalyze and Classify Measurements on page 47).

Compute Measurements

Replaces the menu item ‘Scalar Generator’ and opens the Compute Measurements
dialog, which has the same functionality of the previously named Scalar Generator dialog(see Compute Measurements on page 37).

Create an ROI from TouchingLabel Voxels

Lets you create a region of interest that only includes the labeled class voxels that toucheach other. The example below shows the ROI created from a multi-ROI with three classes
colored yellow, blue, and green.

NOTE Processing is done in the same order as the classes are listed in the Classes andscalar information box on the Data Properties and Settings panel.
Extract Slab Around Center

Extracts a set number of rotated multi-ROIs (slabs) along the object’s central axis. You cana select the number of slabs that will be extracted and the number of slices per slab.

Grow Selected Labels with
Local Otsu

Lets you grow the selected labels of a multi-ROI within the lower or upper local Otsu
threshold and by a specified amount.

Mesh Contextual Menu
The items listed below are now available in the Mesh pop-up menu.
Mesh pop-up menu changes

Item

Description

Analyze and Classify
Measurements

Opens the Analyze and Classify Measurements dialog, in which you can import scalardata from meshes and other objects for the cross-table analysis of feature vectors (seeAnalyze and Classify Measurements on page 47).

Compute Measurements

Replaces the menu item ‘Scalar Generator’ and opens the Compute Measurements
dialog, which has the same functionality of the previously named Scalar Generator dialog(see Compute Measurements on page 37).

Remesh with Poisson Surface
Reconstruction

Lets you reconstruct triangle meshes from sets of oriented 3D points (see Remeshing with
Poisson Surface Reconstruction on page 53).

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Item

Description

Merge Meshes

This item, which is available whenever two or more meshes are selected, lets you merge
the selected meshes into one mesh.

Graph Contextual Menu
The items listed below are now available in the Graph pop-up menu.
Graph pop-up menu changes

Item

Description

Analyze and Classify
Measurements

Opens the Analyze and Classify Measurements dialog, in which you can import scalardata from graphs and other objects for the cross-table analysis of feature vectors (seeAnalyze and Classify Measurements on page 47).

Compute Measurements

Replaces the menu item ‘Scalar Generator’ and opens the Compute Measurements
dialog, which has the same functionality of the previously named Scalar Generator dialog(see Compute Measurements on page 37).

Map Edge Scalar Values From

Lets you map edge scalar values contained within another object. You can choose theobject that includes the scalar values you want to map from in the Choose Scalar Value
Slots dialog, as well as a default scalar value.

Export to CSVLets you export the vertex coordinates in a comma separated values (*.CSV extension) file.

Vector Field Contextual Menu
The items listed below are now available in the Vector Field pop-up menu.
Vector field pop-up menu changes

Item

Description

Analyze and Classify
Measurements

Opens the Analyze and Classify Measurements dialog, in which you can import scalardata from vector fields and other objects for the cross-table analysis of feature vectors(see Analyze and Classify Measurements on page 47).

Compute Measurements

Replaces the menu item ‘Scalar Generator’ and opens the Compute Measurements
dialog, which has the same functionality of the previously named Scalar Generator dialog(see Compute Measurements on page 37).

Views Contextual Menu
The items listed below are now available in the Views pop-up menu.
Views pop-up menu changes

Item

Description

Export Screenshot of All Views

Lets you save a screenshot of all views in the workspace.

Release Notes

Dragonfly 3D World, Version 2024.1
02/26/2024

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Preferences Changes
New items, listed below, and other changes have been implemented for setting theapplication preferences. Click Preferences on the Status bar or choose File > Preferenceson the Menu bar to open the Preferences dialog.

Views Preferences
Changes for the Views preferences are indicated in the following table.
Views preferences changes

Item

Description

3D Settings
Axis indicator

Two new items are available for setting the Axis indicator preferences — Gyroscope(World) and Gyroscope (View). See Gyroscope Axis Indicators on page 68 for more
information about these Axis indicator styles and functions.

Default W/L range

Lets you choose the default range that will be applied for window leveling in 3D views —either computed within the upper Otsu range (Threshold at upper Otsu) or applied to
the full data range (Full data range).
In the example below of the initial window leveling range applied, the ‘Threshold at upper
Otsu’ option was selected as the default W/L range.

Use lighting by default in 3D

If selected, lighting will be applied by default to new 3D views.

ROI painting in 3D

Lets you choose when updates to regions of interest will appear — after the brush isreleased or continuously (see ROI Painting Preferences on page 57).

Deep Learning Preferences
Changes for the Deep Learning preferences are indicated in the following table.
Deep Learning preferences changes

Item

Description

Enable TensorRT modelcompilation

Lets you choose to optimize deep models for inference (see Compile for Faster Inferenceon page 25).

Execute Deep Learning tasks inan external process

If selected, TensorFlow functionality will run as an external process in Windows
installations (see External Processes for Deep Model Task Processing on page 29).

Release Notes

Dragonfly 3D World, Version 2024.1
02/26/2024

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System Requirements
As of version 2024.1, Dragonfly 3D World can be installed on systems/laptops without adedicated graphics processing unit (GPU). The minimum requirement is now an Intel
integrated graphics processor (IGP) with UHD Graphics.
NOTE

Go to https://en.wikipedia.org/wiki/Intel_Graphics_Technology for a list of Intel IGPs with UHD
Graphics.

You should note that performance will likely not be comparable to running Dragonfly 3D
World on a system with a dedicated GPU. In addition, some advanced features are notsupported on systems/laptops without a dedicated GPU. These include viewing multiple
datasets simultaneously, combining images and meshes, using back lighting, and addingreflections to virtual floors.
NOTE

Go to https://www.theobjects.com/dragonfly/system-requirements.html for system recommendations
to fully experience all the benefits and capabilities of Dragonfly 3D World.

Third-Party Packages
You should note the changes for the third-party packages listed below.
Third-party packages

Release Notes

Item

Version 2024.1

Previous version (2022.2)

TensorFlow

2.11

2.4.1

Qt

6.4.2

5.12.9Python3.10.123.8.12

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Publicly Available Source CodeUsers can freely download and examine the source code of the following.

TensorFlow
Dragonfly’s artificial intelligence applications, such as the Deep Learning Tool, theSegmentation Wizard, and Segment with AI, use TensorFlow APIs to facilitate training
and inference of deep neural networks. Created by the Google Brain team, TensorFlow isa free and open-source library widely used for deep learning applications and traditional
machine learning.
Users can freely download and examine the TensorFlow source code from:https://github.com/tensorflow.

Qt
Dragonfly is designed and developed with Qt, which is a free and open-source toolkit for
creating graphical user interfaces and cross-platform applications. Qt is developed by
The Qt Company, a publicly listed company, and is available under both commercial
licenses and open-source GPL 2.0, GPL 3.0, and LGPL 3.0 licenses.
Users can freely download and examine the Qt source code from:https://download.qt.io/official_releases/qt/.

Porous Microstructure Analysis (PuMA)The Porous Microstructure Analysis (PuMA) software integrated in Dragonfly wasdeveloped by researchers at NASA to compute effective material properties and performmaterial response simulations on digitized microstructures of porous media. PuMA cangenerate artificial microstructures that mimic real materials and to compute simple
morphological properties such as porosity, volume fraction, pore diameter, and specificsurface area. Additional capabilities include the determination of effective thermal andelectrical conductivity (both radiative and solid conduction — including the ability tosimulate local anisotropy for the latter); effective diffusivity and tortuosity from thecontinuum to the rarefied regime; techniques to determine the local material orientation,
as well as the mechanical properties (elasticity coefficient), and the permeability of amaterial.
Release Notes

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02/26/2024

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PuMA is available under the terms of the NASA Open Source Agreement (NOSA) and is a
copyright of the United States Government as represented by the Administrator of the
National Aeronautics and Space Administration.
Stable releases of PuMA can be found on the official NASA GitHub repository at:https://github.com/nasa/puma.
The latest development code can be found on the development GitLab repository at:https://gitlab.com/jcfergus/puma-dev.

Netgen/NGSolve
Netgen/NGSolve is a high-performance multi-physics finite element software. It is widelyused to analyze models from solid mechanics, fluid dynamics, and electromagnetics.
Users can freely download and examine the NGSolve source code from:https://github.com/NGSolve/ngsolve/releases.

Release Notes

Dragonfly 3D World, Version 2024.1
02/26/2024

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Learning Dragonfly
You’ll find our latest instructional videos and recorded webinars on our YouTube channel(https://www.youtube.com/channel/UCuFl2zHcyStR2RJpMXbi6ow). In addition, the new
Dragonfly Social online community platform lets Dragonfly users start discussions, chat,access material, post announcements, as well as upload and download extensions anddeep models for the application.
You can also take advantage of the course offerings listed below to further benefit fromour training team's in-depth product knowledge.

Dragonfly Workshops
Our hands-on training workshops are a great way to get up and running quickly.
Combining in-depth instruction with class exercises, our product specialists will quickly
teach you how to use key Dragonfly features and functions to optimize your image
visualization and analysis tasks.
Contact sales.dragonfly@comet.tech for more information and availability.

Online Training
Get connected with an online training session and follow along with a Dragonfly productspecialist as they demonstrate the features and benefits of Dragonfly and walk you
through specialized modules. Web-based training, which can be adapted to individual
needs and interests, offers an interactive approach that lets you practice new techniques
on numerous case studies.
Contact sales.dragonfly@comet.tech for information about availability.

Requesting New Features
Let us know if you have a suggestion for a new feature, or an idea for an improvement toan existing workflow.

Release Notes

Dragonfly 3D World, Version 2024.1
02/26/2024

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Reporting Issues
Dragonfly 3D World includes an integrated reporting module that lets you report any
issue that results in a crash. Whenever you choose to send a report, try to include as
much information as possible to help the support team to understand why the issue
occurred. You can enter details about the issue, such as how to reproduce the problem,in the Send Report to Support Team dialog.
Send Report to Support Team dialog

You should note that you can also report an issue at any time by choosing Help >Report an Issue on the menu bar. For example, when an unknown error occurs or an
unexpected result is generated.
Report an Issue dialog

Release Notes

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02/26/2024

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If you decide to report an anomaly, please try to include the following information:•

A description of the issue and when it first occurred.

•

The full error message and the steps required to reproduce the problem.

•

The installed version of Dragonfly 3D World.

•

The operating system of any affected machine.

•

If the problem affects a particular dataset or image, you may be asked to provide a
copy of the dataset.

Release Notes

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02/26/2024

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