DRAGONFLY RELEASE NOTESVERSION 2022.2

Learn all about the new features, product enhancements, and other improvements implemented in the
Dragonfly and Dragonfly Pro 2022.2 software release. Features of note include elastic registration tobetter understand dataset deformation, pre-trained deep learning models to accelerate model training,normalization of data ranges prior to model training and inference, and new measurements forcharacterizing material properties and measuring bone mineral density.

Copyright© 2022 Object Research Systems (ORS) Inc. ORS is member of the Comet Group. All rights reserved.
The present end-user documentation is confidential and proprietary information of Object Research Systems (ORS)
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Trademarks
Object Research Systems, the ORS logo, Dragonfly, and the Dragonfly logo are trademarks of ORS.

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Notices
Powered with Anaconda Distribution™ from Anaconda, Inc.
Portions 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 from a correlative workflow (courtesy of ZEISS), additive manufacturing
powder characterization, vector field mapping of a 4D deformation study looking at the interface between an additive
manufactured spacer and a human vertebrae while undergoing continuous compression (CT data courtesy
TESCAN/sample courtesy Rush University), segmented Ankylosaurus jawbone (courtesy of DigiMorph).

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Contents
New Features and Product Enhancements............................................................................................................. 7
Feature Analysis ......................................................................................................................................................................... 7
Deep Learning ............................................................................................................................................................................ 9
Pre-Trained Models ............................................................................................................................................................. 9
Normalize Data Range Prior to Training or Inference .......................................................................................... 12
Deep Model Inference on Multiple Axes................................................................................................................... 13
Deep Model Training on Multiple GPUs.................................................................................................................... 14
New Deep Learning Models ........................................................................................................................................... 15
Default Loss Function ....................................................................................................................................................... 15
Custom Loss Functions .................................................................................................................................................... 15
Custom Layers ..................................................................................................................................................................... 17
Additional Data Augmentation Setting ..................................................................................................................... 18
New Macro Blocks for Deep Learning ........................................................................................................................ 19
Segmentation Wizard ............................................................................................................................................................ 20
Pre-Trained Models ........................................................................................................................................................... 20
Deep Model Training on Multiple GPUs.................................................................................................................... 22
Auto Compute When Loading Session Groups ...................................................................................................... 22
Adding Models to a User Strategy .............................................................................................................................. 23
Exclude Image Modalities from Training................................................................................................................... 24
Image Quality Metrics ........................................................................................................................................................... 25
Intensity Scale Calibration ................................................................................................................................................... 26
Configurable Actions for Intensity Scale Calibrations .......................................................................................... 27
Preset Calibration Standards .............................................................................................................................................. 28
Elastic Registration ................................................................................................................................................................. 29
3D Data Augmentation ......................................................................................................................................................... 31
Bone Analysis ............................................................................................................................................................................ 33
New Global Measurements ............................................................................................................................................ 33
Intensity Calibration for BMD and TMD Computations ...................................................................................... 35
Bone Mineral Density Distributions ............................................................................................................................. 36
Scalar Generator ...................................................................................................................................................................... 38
Spherical Harmonics ......................................................................................................................................................... 38
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Anisotropy ............................................................................................................................................................................. 40
Basic Measurements.......................................................................................................................................................... 41
Basic Measurements with Datasets ............................................................................................................................. 41
Artificial Data Generator ....................................................................................................................................................... 42
OpenPNM ............................................................................................................................................................................. 42
PuMA ...................................................................................................................................................................................... 44
Porous Microstructure Analysis (PuMA) ......................................................................................................................... 45
Elasticity Computation Type........................................................................................................................................... 45
Limit Computations to Data Subsets .......................................................................................................................... 46
CT Reconstruction ................................................................................................................................................................... 47
Tilt Angle Finder.................................................................................................................................................................. 48
Macro Builder Blocks for CT Reconstruction ........................................................................................................... 49
Radiographic Dendrochronology ..................................................................................................................................... 50
Converting Image Data Types ............................................................................................................................................ 52
Histogram Analysis ................................................................................................................................................................. 53
Image Filtering ......................................................................................................................................................................... 55
Manual 3D RBF Filter......................................................................................................................................................... 55
Ring Artifact Removal Filter ............................................................................................................................................ 56
Configurable Actions ........................................................................................................................................................ 56
Lossless Compression with ZSTD ...................................................................................................................................... 57
3D Renderings and Settings ............................................................................................................................................... 58
Field of View Control......................................................................................................................................................... 59
Plane Settings ...................................................................................................................................................................... 60
Lighting Controls for ROIs and Multi-ROIs............................................................................................................... 61
Edge Contrast Factor ........................................................................................................................................................ 62
Interpolation......................................................................................................................................................................... 62
Render Mode ....................................................................................................................................................................... 63
Virtual Floor Size Adjustment ........................................................................................................................................ 63
Movie Maker ............................................................................................................................................................................. 64
Save and Load Templates ............................................................................................................................................... 64
Visual Effects of Shapes ................................................................................................................................................... 65
Graph Clipping .................................................................................................................................................................... 65
Overlays ................................................................................................................................................................................. 66
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Window Leveling ..................................................................................................................................................................... 67
Regions of Interest ................................................................................................................................................................. 68
Adaptive Watershed .......................................................................................................................................................... 68
Act on All Timesteps ......................................................................................................................................................... 69
Selection Text Annotation ............................................................................................................................................... 69
New Binary Image (8 bit) from ROI ............................................................................................................................. 70
Multi-ROIs .................................................................................................................................................................................. 70
Gridded Multi-ROIs ........................................................................................................................................................... 70
Grow Labels to Feret Box With Otsu ........................................................................................................................... 71
Meshes ........................................................................................................................................................................................ 72
Generate Meshes from Spherical Harmonics .......................................................................................................... 72
Generate 3D Tetrahedral Meshes ................................................................................................................................ 72
Masking with Meshes ....................................................................................................................................................... 74
Distance Between Meshes .............................................................................................................................................. 75
Vector Fields ............................................................................................................................................................................. 76
Floating Captions for Annotations ................................................................................................................................... 77
Menu Bar Changes ..................................................................................................................................................... 78
Workflows Menu ..................................................................................................................................................................... 78
Utilities Menu ........................................................................................................................................................................... 78
Changes to Contextual Menus ................................................................................................................................ 79
Dataset Contextual Menu .................................................................................................................................................... 79
ROI Contextual Menu ............................................................................................................................................................ 79
Multi-ROI Contextual Menu ................................................................................................................................................ 80
Preferences Changes ................................................................................................................................................... 81
Views Preferences ................................................................................................................................................................... 81
Miscellaneous Preferences .................................................................................................................................................. 81
Deep Learning Preferences ................................................................................................................................................. 81
System Requirements ................................................................................................................................................ 82
Third-Party Packages ............................................................................................................................................................. 82
Publicly Available Source Code ............................................................................................................................... 82
TensorFlow ................................................................................................................................................................................ 82
Qt .................................................................................................................................................................................................. 82
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Porous Microstructure Analysis (PuMA) ......................................................................................................................... 83
Extending Dragonfly .................................................................................................................................................. 84
Requesting New Features .................................................................................................................................................... 84
Learning Dragonfly ..................................................................................................................................................... 85
Dragonfly Workshops............................................................................................................................................................ 85
Online Training ........................................................................................................................................................................ 85
Reporting Issues .......................................................................................................................................................... 86

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New Features and Product Enhancements
The software release for Dragonfly 2022.2 and Dragonfly Pro 2022.2 marks the first
integration of Feature Analysis for performing cross-table analysis of feature vectors andother measurements. Other new features of note include elastic registration to better
understand how datasets were deformed, pre-trained deep learning models toaccelerate model training, normalization of data ranges prior to deep model training or
inference, and new measurements for characterizing material properties and measuringbone mineral density. Significant enhancements for the Segmentation Wizard, CT
Reconstruction, and the Movie Maker, as well as new 3D rendering features andoptimizations, are also included in Dragonfly 2022.2.

Feature Analysis
This software release includes an alpha version of Dragonfly’s Feature Analysis module,
which provides data integration for cross-table analysis of feature vectors and othermeasurements to help classify objects that share specific characteristics.
Choose Utilities > Feature Analysis (alpha) on the menu bar to open the Feature
Analysis dialog, shown below.
Feature Analysis dialog

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In the Feature Analysis dialog, you can:

Add scalar values generated from multi-ROIs, meshes, and graphs to multiple
tables.

Compute basic statistics, such as the mean, median, minimum, maximum,
standard deviation, and variance of imported scalar values, as well as modify
selected values manually or by applying arithmetic expressions.



Plot selected inputs on 1D histograms or on 2D histograms, where the X-axis
represents the values of the first input and the Y-axis the values of the second
input.



Create polar charts that use values and angles to show information as polar
coordinates. Rather than using the standard X and Y coordinates, each point on apolar plane is expressed using these two values:oRadius (r), which is the distance from the center of the plot.

oTheta (θ), which is the angle from a reference angle.

The plane itself is made up of concentric circles expanding outward from theorigin, or the pole. Polar plots are often used when the analyzing data that has acyclical nature.



Create classes by selecting instances in the data that match some criteria.
Instances can be added to classes on 1D histograms with the Range Selector tooland they can be selected by painting on 2D histograms and polar plots. You
should note that in the case of polar plots, painting is done on annular sectors.



Select an item in the table to automatically zoom to the selected item in thecurrent 2D/3D views of any data type, including multi-ROIs, graphs, and meshes.

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Dragonfly Release Notes Version 2022.2

Deep Learning
Updates for Dragonfly’s Deep Learning Tool include pre-trained models, the option tonormalize data ranges prior to training or inference, training on multiple GPUs, and deepmodel inference on multiple axes. A new deep learning model architecture, custom loss
functions and layers, and additional macro blocks for automating deep model training
are also included in this release.

Pre-Trained Models
To help you get started quickly with implementing Dragonfly’s deep learning capabilities,
you can now download a selection of deep models pre-trained by the Dragonfly Team.
You can then use transfer learning to apply the learning to a specific regression or
semantic segmentation problem. Starting with a pre-trained model often provides betterand faster results with smaller training sets than using an untrained model. In theexamples below, the progress of training with a pre-trained and an untrained deepmodel are shown as a function of increasingly larger training sets — from 2, 4, 8, 16, to
32 labeled tiles.
Validation Dice coefficients for pre-trained and untrained U-Net dl-5 ifc-64 models using increasingly larger training sets

— Pretrained— Untrained

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You should note that the objective when training a neural network is to identify thecorrect weights for the network by multiple forward and backward iterations. In the caseusing pre-trained models for semantic segmentation tasks, you take advantage oflearned feature maps to reduce laborious and time-consuming labeling of training sets.
Pre-trained models are generally more label efficient than untrained models as they also
use the unlabeled examples for learning.
The following pre-trained models are available for download:
Pre-trained models

Model

Description

Generic U-Net dl-5 ifc-64 v-1.0

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

Generic U-Net dl-6 ifc-64 v-1.0

Architecture: U-Net
Depth level: 6
Initial filter count: 64
Input dimension: 2D

Generic U-Net dl-7 ifc-32 v-1.0

Architecture: U-Net
Depth level: 7
Initial filter count: 32
Input dimension: 2D

Generic U-Net dl-6 ifc-64 slice-3 v-1.0

Architecture: U-Net
Depth level: 6
Initial filter count: 64
Input dimension: 2.5D, input slices count: 3Generic U-Net dl-7 ifc-32 slice-3 v-1.0

Architecture: U-Net
Depth level: 7
Initial filter count: 32
Input dimension: 2.5D, input slices count: 3

NOTE

The above models were pre-trained by the Dragonfly team with subsets extracted from a large
repository of diverse greyscale images, with uninformative and duplicate data removed.

NOTE

Refer to the topic and tutorial Transfer learning and fine-tuning, which is available athttps://www.tensorflow.org/tutorials/images/transfer_learning for additional information about
working with pre-trained models.

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Do the following to download and generate a pre-trained model:
1. Open the Deep Learning Tool and then click the New button.
The Model Generator dialog appears.
2. Choose Pre-trained (by the Dragonfly Team) as the Architecture.
3. Enter a Model type and Class count (for segmentation models only), as well as aName and Description, optional.
4. Double-click inside the Value cell of the pre-trained model and then choose therequired model in the drop-down menu, as shown below.

5. Click the Generate button.
The model will be downloaded to a local cache the first time that it is generated
and a copy will then be made for the current session. Subsequent models that are
generated will be copied from the local cache. You should note that Dragonfly
will check for new or updated pre-trained models each time that the applicationis started, provided that your system is connected to the Internet. You should
also note that cached models will be available even when Dragonfly runs offline.
The local cache is located at:…\AppData\Local\ORS\Dragonfly2022.2\pythonUserExtensions\Python
PluginExtensions\DeepTrainer\PretrainedWeights.

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Normalize Data Range Prior to Training or Inference
Compromised (poor quality) deep model training and inference can occur in cases inwhich datasets have a limited dynamic range. In these cases, you can now normalize
calibrated and uncalibrated datasets prior to training in the Deep Learning Tool dialog,
as well as before inference in the Segment with AI dialog and in the Filter with AI dialog.
Click the Edit Calibration button, as shown below, to open the Normalization dialog.
Deep Learning Tool dialog

Adjusting the normalization boundary can be useful in cases in which you only want toapply the model on the current input and the input histogram is not spread over thedata range. You should also note that the normalization range selected during training issaved with the model and that the saved range is loaded and set as the default at
inference. You can edit the normalization range at inference before applying the modelon an input, but this adjustment is not saved. The next time that the model is loaded, thenormalization range is reset, as shown below.
Normalization dialog

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Dragonfly Release Notes Version 2022.2

Deep Model Inference on Multiple Axes
As an additional option for deep model interference, you can choose to segment yourdatasets on multiple axes in the Segment with AI panel. In this case, the deep model will
be applied on multiple axes and then the class label will be predicted based on theselected type of vote — Majority or Soft.
The Segment with AI panel, shown below, is available by default on the left sidebar’s
Segment tab. You can also right-click the dataset you need to segment and the choose
Segment with AI in the pop-up menu to open the panel. The options for segmenting on
multiple axes are circled on the screen capture of the Segment with AI panel.
Segment with AI panel

You can select ‘Majority vote’ or ‘Soft vote’ for the multiple axes inferences as follows.
Majority vote… Sums the predictions for each class label and predicts the class labelwith the most votes.
Soft vote… Sums the predicted probabilities for each class label and predicts the classlabel with the largest probability.

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Deep Model Training on Multiple GPUs
In some cases, you may find it advantageous to train deep models on multiple GPUs
instead of a single GPU. If you have multiple GPUs available, then you can set how many
GPUs to use for deep model training at the beginning of training. It can be any number
from 1 to the total number of available GPUs. You should note that you can select yourpreferred GPUs in the Deep Learning preference ‘GPU card for Deep Learning’ (see Deep
Learning Preferences on page 81).
The option for selecting the number of GPU cards to use for model training is available
on the Training Parameters tab in the Deep Learning Tool dialog, as shown below.
Training Parameters tab

NOTE

Selecting this option will add some overhead to model training as models will be replicated for eachGPU. In addition, if not all GPUs are the same, then the slowest card may create a bottleneck as
patches are split evenly, not proportionally to each GPU.

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Dragonfly Release Notes Version 2022.2

New Deep Learning Models
A new deep learning architecture — Multi-Level Wavelet U-Net — is available in thissoftware release for training regression models. This model is a U-Net style encoderdecoder network with discrete wavelet transform (DWT) encoding and inverse wavelet
transform (IWT) decoding. In this modified U-Net architecture, DWT reduces the size offeature maps in the contracting subnetwork, while an additional convolutional layer isused to decrease the channels of feature maps. In the expanding subnetwork, IWT isthen deployed to reconstruct high-resolution feature maps.
NOTE

Refer to the publication Pengju Liu, Hongzhi Zhang, Wei Lian, Wangmeng Zuo, Multi-Level Wavelet
Convolutional Neural Networks, IEEE Access, Volume 7, 2019 for more information about this U-Net
style encoder. Available at: (https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8732332).

NOTE

Information about the implementation of the Multi-Level Wavelet U-Net model, as well as
experimental test results, are available at: https://github.com/lpj0/MWCNN.

Default Loss Function
You should note that Categorical Crossentropy is now the default loss function forsemantic segmentation, instead of OrsDiceLoss.

Custom Loss Functions
For training regression tasks, this software release includes a number of new custom loss
functions. Double-click the editable Loss function field on the Training Parameters tab of
the Deep Learning Tool to view the available loss functions, as shown below.
Custom loss functions

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The following custom loss functions are available in Dragonfly 2022.2.
Custom loss functions

Loss Function

Description

OrsGradientLoss

A simple loss function that helps preserve image structures for super-resolution and is thesquared error loss between the gradients in X and Y of true and predicted batch. Loss isnormalized on twice the batch size to consider the two directions of gradient.
NOTE The input and output must be well registered or the translation may be learned. In
addition, noise has a high gradient and blotchy artifacts can be created in empty areas.
REFERENCE Abrahamyan et al. Gradient Variance Loss for Structure-Enhanced Image SuperResolution, arXiv, February 2, 2022 (https://arxiv.org/pdf/2202.00997.pdf).

OrsMixGradientLoss

This loss function is similar to OrsGradientLoss but is more forgiving than gradient loss fordataset registration. Includes Mean Square Error in the loss calculation and Sobel edges areused instead of gradient.
NOTE Uses fixed weight for the gradient and mean square portion of total loss. Noise has ahigh gradient.
REFERENCE Zhengyang Lu and Ying Chen, Single Image Super Resolution Based on a Modified
U-Net with Mixed Gradient Loss, arXiv, November 21, 2019(https://arxiv.org/pdf/1911.09428.pdf).

OrsTotalVarianceLoss

Total variance defined by sum of integral of absolute value. This loss function tries tominimize the variance in an image while maintaining a fit to the original. Essentially tries toreproduce the image in piecewise constant functions.
NOTE Weight between variance and image fit automatically adapted based on scale ofimage features. May be less effective for complicated structures.
REFERENCE David Strong and Tony Chan, Edge-Preserving and Scale-Dependent Properties of
Total Variation Regularization, Inverse Problems, Volume 19, Number 6(https://iopscience.iop.org/article/10.1088/0266-5611/19/6/059).

OrsVGGFeatureLoss

This loss function compares the activation map at a layer of a pretrained VGG16 model
between true and predicted batches. VGG style networks have shown good discriminative
performance and tend to preserve structures well with less blur in output image.
NOTE Uses memory to load VGG model.
REFERENCE Johnson et al. Perceptual Losses for Real-Time Style Transfer and Super-Resolution,
arXiv, March 27, 2016 (https://arxiv.org/pdf/1603.08155.pdf).

OrsPerceptualLoss

Designed for use with Gan-style models, this loss function includes style loss (4 activation
layers of VGG), feature loss (1 activation layer of VGG), TV loss, and MSE loss. This loss
function tends to preserve structures well with less blur in output images.
NOTE Uses memory to load VGG model, weights of losses in total loss is fixed.
REFERENCE Johnson et al. Perceptual Losses for Real-Time Style Transfer and Super-Resolution,
arXiv, March 27, 2016 (https://arxiv.org/pdf/1603.08155.pdf).

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Dragonfly Release Notes Version 2022.2

Custom Layers
In some cases, you may want to edit the network structure of a deep learning model bymodifying the combination of pre-defined components (functions) called layers. In
addition to all the layers previously defined for the Deep Learning Tool, this software
release includes three additional custom layers — OrsDiscreteWaveletTransform,OrsInverseWaveletTransform, and OrsConvolveBatches.
Click the Go to Editing button at the bottom of the Deep Learning Tool dialog to open
the Model Editing panel. You can view the available layers in the Select New Layer Typedrop-down menu, as shown below.
Model Editing panel

The parameters of each layer can be set by selecting the layer and then setting thevalues in the side panel on the right. The model can be edited by dragging these layers
on the workspace and then defining the connection graph between the layers.
Custom layers

Layer

Description

OrsDiscreteWaveletTransform

This custom layer performs a discrete wavelet transform using Haar wavelets on input
images. The output is four quarter-sized images. This layer can be used to replace poolinglayers as it enlarges the perceptual field, but unlike pooling is perfectly inversible.
NOTE Outputs quadruple channel size and may complicate models.
REFERENCE Liu et al. Multi-Level Wavelet Convolutional Neural Networks, IEEE Access, Volume
7, 2019 (https://ieeexplore.ieee.org/abstract/document/8732332).

OrsInverseWaveletTransform

17This layer performs an inverse wavelet transform using Haar wavelets on input images. The
layer takes four channels as input and outputs one channel with a size four times larger
than the inputs. Used as the up-sampling scheme for discrete wavelet transform.
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Layer

Description
REFERENCE Liu et al. Multi-Level Wavelet Convolutional Neural Networks, IEEE Access, Volume
7, 2019 (https://ieeexplore.ieee.org/abstract/document/8732332).

OrsConvolveBatches

This layer performs a convolution between images and kernels batch-wise and can be used
to handle large ranges of motion during sparse-view interpolation. Kernel size must be
specified and the layer does not perform any padding.
NOTE Equivalent operation to iterating a kernel over a batch but is very memory intensive.
REFERENCE Niklaus et al. Video Frame Interpolation via Adaptive Separable Convolution, arXiv,
August 5, 2017 (https://arxiv.org/pdf/1708.01692.pdf).

Additional Data Augmentation Setting
Data augmentation is the process of increasing the size of a dataset by transforming it inways that a neural network is unlikely to learn by itself. This software release provides anadditional data augmentation setting called ‘Jitter’. If selected, a jitter effect will beapplied randomly to input and output patches within the set range. You should note thatin many cases jitter can help prevent overfitting during training.
Data augmentation settings are available on the Training screen’s Inputs tab, as shown
below.
Data augmentation settings

NOTE

This additional data augmentation setting is also available in the Segmentation Wizard on the Data
Augmentation tab in the Training Parameters dialog and for custom model architectures on the Traintab.

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Dragonfly Release Notes Version 2022.2

New Macro Blocks for Deep Learning
A number of new Macro Builder blocks, shown below, are available in Dragonfly 2022.2
for automating deep learning model training with Dragonfly’s Macro Builder.
Choose Utilities > Macro Builder on the menu bar to open Dragonfly’s Macro Builder.
New Macro Builder blocks for deep learning model training

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Segmentation Wizard
Pre-trained models, the option to train deep models on multiple GPUs, and excluding
image modalities from training are available for Dragonfly’s Segmentation Wizard in thissoftware release. Other updates include new auto compute settings and the option to
add trained deep models to user-defined strategies.

Pre-Trained Models
You can now download a selection of deep models pre-trained by the Dragonfly Team.
In many cases, starting with a pre-trained model provides better and faster results withsmaller training sets than using an untrained model. In the case of using pre-trainedmodels for semantic segmentation tasks, you take advantage of learned feature maps toreduce laborious and time-consuming labeling of training sets. Refer to the topic PreTrained Models on page 9 for descriptions of the available pre-trained models andadditional information about working with pre-trained models.
You can add pre-trained models to a user-defined strategy, as described below. Selectedmodels will be downloaded to a local cache the first time that they are imported andthen a copy will be made for the current Segmentation Wizard session. Subsequentmodels will be copied from the local cache. You should note that Dragonfly will check for
new or updated pre-trained models each time that the application is started, provided
that your system is connected to the Internet. You should also note that cached models
will be available even when Dragonfly runs offline.
Do the following to add a pre-trained model to a user-defined strategy:
1. Click the Model Generation Strategy icon on the Models tab.
2. Select or create a user-defined strategy in the Model Generation Strategy dialog.
3. Click the Add New Model button.
4. Scroll to and select Pre-Trained (by the Dragonfly Team) in the Select dropdown menu, as shown in the following screen capture.

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Dragonfly Release Notes Version 2022.2

5. Double-click inside the editable Pre-trained model field and then choose therequired model in the drop-down menu, as shown below.

6. Click the Apply button to add the selected model to the strategy.
The selected model will be downloaded, if required, and then copied to thecurrent Segmentation Wizard session.

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Deep Model Training on Multiple GPUs
In some cases, you may find it advantageous to train deep models on multiple GPUs
instead of a single GPU (see Deep Model Training on Multiple GPUs on page 14).
The option for selecting the number of GPU cards to use for model training is available
on the Settings tab, as shown below.
Settings tab

Auto Compute When Loading Session Groups
The option to automatically compute and show model predictions by default whenloading a Segmentation Wizard Session Group is available in this software release. This
option is available on the Settings tab, as shown below.
Settings tab

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Dragonfly Release Notes Version 2022.2

Adding Models to a User Strategy
You can now add trained deep models to a user-defined strategy from the current
Segmentation Wizard session. This can be particularly useful if you often generate thesame model(s) with edited training parameters.
Right-click a trained deep model on the Models tab and then choose Add to Strategy to
add the model to either a new strategy or to an existing user-defined strategy.
Models tab

Create a New Strategy… Select this option to add the selected model to a new strategy.
Current strategies list… Select this option to add the selected model to a listed userdefined strategy.
You can also add trained deep models to a user-defined strategy in the Model
Generation Strategy dialog. To do so, click the Add New Model button on the dialog
and then choose a trained model in the Pretrained models list.
NOTE

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You can edit the name and/or description of the model that was added to a user-defined model
generation strategy, as shown below.

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Exclude Image Modalities from Training
In some cases, you might want to exclude an image modality from model training. For
example, in cases in which a modality in a multi-modality training set will only be used
for labeling voxels. The new option to include or exclude an input modality from modeltraining is available in the Image modalities list, as shown below.
Image modalities

Checked… If checked, the dataset will be included with input modalities during modeltraining.
Unchecked… If not checked, the dataset will be excluded from model training and will
only used for labeling. Check to include the image in the input modalities.

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Dragonfly Release Notes Version 2022.2

Image Quality Metrics
You can now evaluate and compare the image quality of previews and other single-slice
datasets with a number of different metrics, such as Sharpness, Shannon Entropy, SNR,
and FactorQ, in the newly implemented Image Quality Metrics dialog.
Right-click the data that you want to include in your evaluation and then choose Open
Image Quality Metrics in the pop-up menu to open the dialog. You can then choose theobjects to include in the comparison, apply a mask to limit computations, and select the
metrics for comparison. You can also export the results in the comma-separated values
file (*.csv extension) format.
Image Quality Metrics dialog

Image quality metrics

MetricDescription

Sharpness

Is a measure of how accurately a sample is represented and is related to the edge contrastof an image.
NOTE This metric is calculated using the function numpy.gradient(https://numpy.org/doc/stable/reference/generated/numpy.gradient.html).

Shannon Entropy

Is a measure of the uncertainty in predicting voxel values. Decreasing values would meanless uncertainty and better image quality.
NOTE This metric is calculated using the function skimage.measure.shannon_entropy(https://scikit-image.org/docs/0.13.x/api/skimage.measure.html#skimage.measure.
shannon_entropy).

SNR

Is the signal-to-noise ratio, which is defined as the ratio of average and standard deviationof a signal or measurement. The higher the ratio, the better the image quality.
NOTE This definition is only useful for variables that are always non-negative.

FactorQ

Is a measure of the degree of separation of two material classes in the analyzed image andis calculated on the base of a grey value histogram.
NOTE FactorQ is only meaningful for datasets with two classes.

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Intensity Scale Calibration
This software release provides several updates for calibrating the intensity scale ofimages to a set of calibration standards. By setting-up a standard calibration, theintensity values of different materials or tissue will be consistent, regardless of thesample acquired. In most cases, you can re-use the same deep model for the inference ofdifferent datasets that are calibrated to the same standard.
Right-click the data you need to calibrate and then choose Calibrate Intensity Scale in
the pop-up menu to open the Intensity Calibration dialog, shown below.
Intensity Calibration dialog

NOTE

An intensity scale calibration does not change the data distribution; it only computes a slope/offset
transformation so that the selected standard intensity values will be obtained from the raw intensity
values. This is the same as manually modifying the slope/offset values in the Image Properties panel.

The following updates are available in the Intensity Calibration dialog:You can use the integrated Probe tools to extract the 'mode' of the raw intensity
values of each selection, which is extracted from the histogram and the size ofthe spherical brush. Mode is the intensity value that corresponds to the center ofthe bin having the maximum element count. You should note that the histogramis built with 1024 bins spread over the range of data of the dataset.
You should also note that it is normal to have a different value for mode than forthe mean. The advantage of using mode instead of the mean is to help identify

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the intensity of a given material that is mixed with other materials, without being
influenced by those other materials.


You can adjust your selections directly on the histogram.

For calibrated datasets, you can toggle between Raw intensity and Calibrated
intensity by selecting a presentation scale.

You can remove calibrations from raw data by clicking the Reset Calibration
button. When reset, the offset and slope values will be set to 0 and 1 respectively.
A reset button is also available on the Image Properties panel, as shown below.

Configurable Actions for Intensity Scale Calibrations
The following actions can be configured for intensity scale calibrations.
Configurable actions for intensity scale calibrations

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ActionDefault Key

Decrease probe size (in Intensity Calibration)

Mouse wheel down

Increase probe size (in Intensity Calibration)

Mouse wheel up

Probe pre-calibrated value in Intensity Calibration

Left mouse

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Preset Calibration Standards
Preset calibration standards for Hounsfield units (HU) and Bone Mineral Density are
now available for calibrating raw data. You can review the set labels and intensity values
for these presets in the Dimension Unit Manager.
Choose Utilities > Dimension Unit Manager on the menu bar to open the Dimension
Unit Manager dialog, shown below.

NOTE

Refer to the topic Intensity Scale Calibration on page 26 for information about the updates available
for calibrating the intensity scale of images. Refer to the topic Updates for Bone Analysis on page 29
for information about bone mineral density measurements.

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Dragonfly Release Notes Version 2022.2

Elastic Registration
To better understand how a dataset was deformed, this software release provides nonrigid or elastic registration for mapping points from one image to the corresponding
points in a reference image.
Right-click the fixed image that you want to use as the reference for the registration andthen choose Image Registration in the pop-up menu to open the Image Registration
dialog, shown below. You can then choose Non-rigid as the registration type, as well as
the mobile image for which the deformable registration is computed and the resulting
transform is used to resample it. The output settings include the registered image,displacement modulus, displacement vector fields and generated X, Y, and Z
displacements.
Image Registration dialog

NOTE

Rigid and non-rigid registration of images in Dragonfly is based on elastix and SimpleITK, which are
open-source software packages based on the well-known Insight Segmentation and Registration
Toolkit (ITK)You can also apply multiple transforms, assign weights to similarity and penalties
metrics, as well as choose settings for the selected optimizer and interpolator, byselecting the Use advanced settings option.

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The advanced options for Simple Elastix – Elastic Registration are shown in the following
screen capture.
Advanced settings

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3D Data Augmentation
For some processing and analysis purposes, you may want to create deformed or warped
datasets. For example, to test invert problems such a non-rigid registration or to create
additional data for deep model training. For these purposes, Dragonfly includes a 3D
data augmentation generator that lets you create warped datasets from an original
dataset.
Right-click the original dataset, region of interest, or multi-ROI and then choose 3D Data
Augmentation in the pop-up menu to open the 3D Data Augmentation dialog, shown
below.
3D Data Augmentation dialog

In the 3D Data Augmentation dialog, you can:

Select the number of outputs required.



Set the seed positions and vector magnitude distribution, as well as an image
intensity interpolation.



Choose to generate deformation vector fields, in addition to the generated
datasets.

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An example of a generated warped dataset with its associated deformation vector field isshown below.
Warped dataset with deformation vector fieldObject Research Systems (ORS) Inc.

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Bone Analysis
This software release includes a number of additional measurements for quantifying
bone micro-architectures and measuring bone mineral density. These measurements areavailable in the Global Measurements dialog whenever you perform a detailed analysis
with Dragonfly’s Bone Analysis wizard.
You should note that you can now filter the available measurements by input. For
example, only measurements related to trabecular bone will appear if you only input a
trabecular bone region of interest. Likewise, if you add the filled bone ROI, then themeasurements related to those inputs will appear in the dialog, as shown below.
Global Measurements dialog

New Global Measurements
The following new measurements are available for providing quantitative descriptions ofbone micro-architecture and bone mineral density from segmented bone regions.
NOTE

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Bone mineral density measurements are only available in Dragonfly Pro. Contact Object Research
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New global measurements for Bone Analysis

Abbr.

Measurement

Description

Unit

BMD.Me1

Bone mineral density(trabecular bone +medullary cavity)

Is the mean intensity of bone mineral density (BMD) computed forcalibrated bone data that corresponds to trabecular bone and the
medullary cavity.

mg.cm-3

NOTE This parameter relates to a mixed bone-soft tissue region.
Refer to TMD.BV and TMD.Co for measurements of the mineraldensity of bone itself.
NOTE See Intensity Calibration for BMD and TMD Computations on
page 35 for information about calibrating bone data.
BS

Bone surface

Is the surface of the trabecular bone and is computed from the
input trabecular bone ROI.

(default unit)2

NOTE This parameter can be computed with the marching cube or
weighted voxel estimation methods.
BS/BV

Specific bone surface

Is the ratio of the bone surface (trabecular) to the bone volume(trabecular + cortical) and is computed from the input bone ROIs.

(default unit)-1

NOTE Surface to volume ratio or “specific bone surface” can be auseful parameter for characterising the complexity of structures.
BS/TV

Bone surface density

Is the ratio of the trabecular bone surface to the total volume of themixed bone-soft tissue region (total volume).

(default unit)-1

Conn.D

Connectivity density

Is a measure of the degree of connectivity of trabeculae normalized
by the volume of the trabecular bone region of interest.

(default unit)-3

Ct.Po

Cortical porosity

Is the ratio of pores to the total volume in the input cortical bone
region and is computed as:none[total pore volume (Po.V)/total volume of cortical bone (Ct.V)].
SMI2

Structure model index

Is an indicator of the relative structure of the trabeculae andinvolves measurements of surface convex curvature. Ideal plates,cylinders, and spheres have SMI values of 0, 3 and 4 respectively.
Conversely, cylindrical and spherical cavities have SMI values of -3
and -4 respectively.

none

NOTE This parameter is of importance in osteoporotic degradationof trabecular bone, which is characterised by a transition from aplate-like to rod-like architecture.
Tb.N

Trabecular number

Is a measure of the average number of trabeculae per unit length.

(default unit)-1

TMD.BV3

Tissue mineral density(Bone volume)

Is the mean intensity of tissue mineral density (TMD) computed forcalibrated bone data that corresponds to the input cortical andtrabecular bone regions.

mg.cm-3

NOTE See Intensity Calibration for BMD and TMD Computations on
page 35 for information about calibrating bone data.
TMD.Co3

Tissue mineral density(Cortical bone)

Is the mean intensity of tissue mineral density (TMD) computed forcalibrated bone data that corresponds to the cortical bone ROI.

mg.cm-3

NOTE See Intensity Calibration for BMD and TMD Computations on
page 35 for information about calibrating bone data.
1

Bone mineral density (BMD) is defined as the volumetric density of calcium hydroxyapatite (CaHA) in a biological tissue in terms of mg.cm-3.
It is calibrated by means of phantoms with known density values of CaHA.

2

Refer to Hildebrand T, Ruegsegger P. Quantification of Bone Microarchitecture with the Structure Model Index, Comput Methods Biomech
Biomed Engin. 1997; 1: 15–23 for more information about SMI.

3

In contrast to BMD, tissue mineral density (TMD) provides information about the material density of the bone itself and ignores surrounding
soft tissue.

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Dragonfly Release Notes Version 2022.2

Intensity Calibration for BMD and TMD Computations
Measurements of bone mineral density (BMD) and tissue mineral density (TMD) requirecalibrated bone data, which is calibrated by means of phantoms with a known density ofcalcium hydroxyapatite (CaHA). Calibration can be done beforehand and is also madeavailable on demand in the Bone Analysis wizard by clicking the Calibrate Intensity Now
button in the Global Measurements dialog, as shown below.
Global Measurements dialog

You can then calibrate the data to standard intensity values in the Intensity Calibration
dialog, which is shown below.
Intensity Calibration dialog

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In the Intensity Calibration dialog, you can:

Select the required calibration points. For example, water and your CaHA
phantom.

Probe the bone data to extract the mode of the raw intensity values.



Adjust the probed values with the indicators on the histogram.



Toggle between raw intensity values and calibrated intensity values.

NOTE

Refer to the topic Intensity Scale Calibration on page 26 for more information about intensity scalecalibrations.

Bone Mineral Density Distributions
This software release includes the option to add histograms of bone mineral density(BMD.Me) and tissue mineral density (TMD.BV and TMD.Co) distributions to Bone
Analysis reports. Plotted histograms include labels for the 5th percentile, 95th percentile,mode, mean, and full width half maximum (FWHM) computed for calibrated bone data,
as shown below.
Histogram of bone mineral density distribution

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Dragonfly Release Notes Version 2022.2

To add a histogram to a report, select and compute the required BMD and/or TMD
measurements and then check the BMD and/or TMD distribution options, as circled on
the example shown below.
Global Measurements dialog

NOTE

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Bone mineral density measurements and distributions are only available in Dragonfly Pro. Contact
Object Research Systems for information about the availability of Dragonfly Pro.

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Scalar Generator
A number of new measurements are available in the Scalar Generator for characterizing
and measuring object properties, as well as for computing locations as discrete pixel
indices.

Spherical Harmonics
In mathematics and physical science, spherical harmonics (SH) are special functions thatare defined on the surface of spheres. In Dragonfly 2022.2, you can add scalar values ofspherical harmonics to the classes of a multi-ROI to help classify clusters of spherical andquasi-spherical objects and to identify outliers. In some cases, spherical harmonics may
provide a more robust measurement than ‘roughness’ for particle analysis and can beused as a feature vector for machine learning classification tasks.
Right-click the multi-ROI to which you want to add scalar values of spherical harmonics
and then choose Scalar Generator in the pop-up menu. Spherical harmonics areavailable in the Scalar Generator dialog, as shown below.
Scalar Generator

In the Scalar Generator dialog, you can:

Add a user prefix to scalar values of spherical harmonics, optional.

Select the minimum and maximum power spectrum that will be computed.



Select a 'grid size', which determines the number of rays projected to make theprojection map.

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Dragonfly Release Notes Version 2022.2
2D histogram of plotted SH powers in the Object Analysis dialog

NOTE

For simulation and experimentation purposes, you can generate meshes from spherical harmonics(see Generate Meshes from Spherical Harmonics on page 72).

References
Refer to the following for more information about spherical harmonics and theimplemented Python software package used for spherical harmonic transforms.
Overview of spherical harmonics:(https://mathworld.wolfram.com/SphericalHarmonic.html).

Integration package of Spherical Harmonic Tools (pyshtools 4.10):(https://shtools.github.io/SHTOOLS/index.html).



Tutorial that demonstrates how to analyse global data on the sphere using
spherical harmonic functions:(https://nbviewer.org/github/SHTOOLS/SHTOOLS/blob/master/examples/notebo
oks/low-level-spherical-harmonic-analyses.ipynb).

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Anisotropy
This software release provides the opportunity compute anisotropy for the classes within
a multi-ROI using the mean intercept length (MIL) or star volume distribution (SVD)
method. The settings for computing the degree of anisotropy are available in the Scalar
Generator dialog, as shown below.
Scalar Generator dialog

NOTE

Anisotropy is a measure of how highly oriented substructures are within a volume. For an isotropic(perfectly oriented) system, the degree of anisotropy (DA) is equal to 0. As the system becomes moreanisotropic (less well-oriented), the DA increases to some value less than 1.

Refer to the table below for a description of the settings applicable to anisotropy.
Settings for computing anisotropy

Parameter

Description

Sampling

Is the resolution, or distance between subsequent samples along each vector. The enteredvalue can be equal to the voxel size of the input multi-ROI.

RadiusIs the radius of the sampling sphere, which determines the length of each sampling vector.

Tolerance

Is the coefficient of variation. Sampling new random points will continue until either acoefficient of variation equal to the tolerance is reached or the maximum number of
iterations is completed.

Orientations

Is the number of lines to analyze per sampling sphere.

Min iterations

Is the minimum number of random points in the sample that will be analyzed.

Max iterations

Is the maximum number of random points in the sample that will be analyzed. Fitting willstop automatically after this number is completed or if the coefficient of variation(tolerance) is reached.

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Basic Measurements
The following basic measurements are now available for multi-ROIs in this software
release.
Basic measurements

Measurement

Description

Center of Mass Index X/Y/Z

Indicates the X, Y, or Z voxel index of the center of mass, whichis calculated from the centroid of the object.

Min Index X/Y/Z

Indicates the minimum X, Y, or Z voxel index of an object.

Max Index X/Y/Z

Indicates the maximum X, Y, or Z voxel index of an object.

Basic Measurements with Datasets
The following new basic measurements with datasets are available for multi-ROIs in thissoftware release.
Basic measurements with datasets

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Measurement

Description

Weighted Center of Mass X/Y/Z Index

Indicates the X, Y, or Z voxel index of the weighted center ofmass.

Position of Minimum Value X/Y/Z

Indicates the X, Y, or Z position (in default units) of theminimum value within each object.

Position of Maximum Value X/Y/Z

Indicates the X, Y, or Z position (in default units) of the
maximum value within each object.

Index of Minimum Value X/Y/Z

Indicates the X, Y, or Z voxel index of the minimum value withineach object.

Index of Maximum Value X/Y/Z

Indicates the X, Y, or Z voxel index of the maximum value withineach object.

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Artificial Data Generator
This software release features an integration of the algorithms used in Pore Network
Modeling (OpenPNM) and Porous Microstructure Analysis (PuMA) for generating
artificial image data and graphs.
Choose Utilities > Artificial Data Generator on the menu bar to open the Create an
Object dialog, shown below.
Create an Object dialog

OpenPNM
The options for generating artificial image data for pore network simulations, which
include blobs, cylinders, lattice spheres, overlapping spheres, and Voronoi edges, areavailable in the Create an Object dialog whenever OpenPNM is selected as the algorithm
for image data. These structure types are available in the Structure type drop-down
menu.

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Dragonfly Release Notes Version 2022.2
Generated artificial pore network

You also generate graphs for pore network simulations with selectable geometry andconnectivity by selecting Graph as the Object type.
Generated graph

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PuMA
You can now generate random fiber and random sphere specimens with PuMA’s Sample
Generator whenever PuMA is selected as the algorithm. You can select a size for the
specimen and effective porosity, as well as specify a number of additional parameterssuch as radius, length, orientation, and intersection for fiber structure types. The
parameters for sphere structure types include diameter and intersection.
Random fiber specimen

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Dragonfly Release Notes Version 2022.2

Porous Microstructure Analysis (PuMA)
A number of updates for performing material response simulations on microstructures ofporous media with Dragonfly’s Porous Microstructure Analysis (PuMA) module areavailable in this software release. These updates are described below.

Elasticity Computation Type
This software release includes the addition of Elasticity as a computation type forinvestigating effective material properties and for performing material response
simulations on microstructures of porous media. You should note that the required
inputs for elasticity computations is a multi-ROI, the parameters for the labels, and a
direction or directions for solving.
PuMA dialog with results for elasticity

You can also generate vector fields to visualize published data of the displacement,
direct stress, and shear stress coefficients computed for effective elasticity, as shown
below.

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Vector field of direct stress

Limit Computations to Data Subsets
Computations of continuum tortuosity, elasticity, orientation detection, permeability, andthermal conductivity can now be limited to a subset of the data contained within a userdefined box or within a clip box applied to the workspace. These options are available inthe computation settings box, as circled below.
PuMA dialog

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Dragonfly Release Notes Version 2022.2

CT Reconstruction
This software release features a refactoring of the CT Reconstruction dialog, as well as
the integration of RTK for the reconstruction of parallel-beam projections and the
addition of SART for the reconstruction of cone-beam projections. You should note that
RTK is an open-source software package based on the Insight Toolkit (ITK).
Choose Workflows > CT Reconstruction on the menu bar to open the CT
Reconstruction dialog, shown below.
CT Reconstruction dialog

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Tilt Angle Finder
In cases in which projection data was acquired at a tilt angle, reconstructions may
include artifacts and other anomalies. To help optimize the quality of yourreconstructions, this software release includes a CT Tilt Angle Finder.
Click the Find Title Angle button in the CT Reconstruction dialog to open the CT Tilt
Angle Finder dialog, shown below.
CT Tilt Angle Finder

NOTE

The CT Tilt Angle Finder dialog also includes an alternate method to find the rotation center offset. In
this case, it is best to find the tilt angle first and then the rotation center offset.

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Dragonfly Release Notes Version 2022.2

Macro Builder Blocks for CT Reconstruction
With new Macro Builder blocks for CT Reconstruction, you can now fully configure andautomate the reconstruction of cone beam and parallel beam projections in Dragonfly’s
Macro Builder.
Choose Utilities > Macro Builder on the menu bar to open the Macro Builder.
Fully configured macro for reconstructing cone beam projections

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Radiographic Dendrochronology
Dragonfly’s new Radiographic Dendrochronology wizard provides a guided workflow
for analyzing wood samples or planks from furniture or works of art, such as old panel
paintings, that were acquired with non-destructive testing techniques. The results froman analysis, which include the width of each tree ring ordered in the direction of growth,
can visually inspected and then exported in the Heidelberg format.
Dendrochronology (or tree-ring dating) is the scientific method of dating tree rings (alsocalled growth rings) to the year they were formed and derives from Ancient Greek
dendron meaning "tree", and khronos, meaning "time".
Choose Workflows > RAD-DENDRO on the menu bar to open the Radiographic
Dendrochronology Wizard, shown below.
Radiographic Dendrochronology wizard

NOTE

The Radiographic Dendrochronology wizard was developed by Blake C. Sharp, Arlen Heginbotham,
Pascale Fraiture, Andrea Seim from the J. Paul Getty Museum in Los Angeles in conjunction with the
Dragonfly team.

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Dragonfly Release Notes Version 2022.2

The Radiographic Dendrochronology wizard provides the following workflow steps:Import and register images that were acquired with high resolution X-ray film orthat were reconstructed from CT tomography.

Crop images to a subset of the input dataset.

Apply filtering to remove artifacts, such as dust on digitized X-ray films.

Automatically extract the direction of growth.

Define a path to automatically analyze and labelize each ring from a chosen
starting point.



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Visually inspect the results and export outputs in the Heidelberg format.

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New Features and Product Enhancements

Converting Image Data Types
In some cases, you may need to convert the data type of your images. For example, fromunsigned short to unsigned char. In this software release, you can now choose to preserve
the original data calibration, in which case the slope and offset values will be modified tomatch the original data, or to set the values within the usual data range for the selected
data type.
Right-click the image data you want to convert and then choose Modify and Transform> Convert in the pop-up menu to open the Image Converter panel. The option topreserve the calibration or to set the intensity values within the normalized range iscircled below.
Output options for converting image data

Preserve calibration by rescaling… If checked, the original data calibration will bepreserved by transforming the offset and slope values. For example, if you convert animage saved as unsigned short to unsigned char, the intensity values will still range from0 to 65,000. If unchecked, the offset will be set to 0 and the slope will be set to 1. In thiscase, intensity values will be within the usual data range of the selected data type. For
example, if you convert an image saved as unsigned short to unsigned char, the intensity
values will range from 0 to 255.
Normalize… If selected, the data spread will be normalized within the selected Min andMax range values.

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Histogram Analysis
The Histogram Analysis dialog now includes the option to compute additional
measurements, such as the 5th and 95th percentile, full width half maximum, mode, andsum. The dialog also includes tick marks to indicate measurement locations, as well as alegend that can be superimposed on the plotted values.
Click the Histogram tool on the Data Properties and Settings panel to plot a dataset’s
intensity distribution, as shown in the following screen capture. You can also use theHistogram tool to plot a dataset’s intensity distribution within a region of interest andthe scalar values within a multi-ROI, mesh, or other object.
Histogram Analysis dialog

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The following measurements are available in the Histogram Analysis dialog.
Histogram Analysis measurements

Measurement

Description

5th Percentile

Is the value below which 5% of the values in the distribution may be found.

95th Percentile

Is the value below which 95% of the values in the distribution may be found.

Full Width Half Maximum

Is the width of the curve as measured between points on the Y-axis which areat the peak half maximum level.

Maximum

Indicates the maximum intensity value.

Mean

Indicates the mean intensity value.

MedianIndicates the median intensity value.

Minimum

Indicates the minimum intensity value.

Mode

Is the most commonly occurring value.

Standard Deviation

Indicates the standard deviation of intensity values. Shown as the range fromthe mean value on the histogram.

Sum

Is the integral of all intensities of all of the pixels.

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Dragonfly Release Notes Version 2022.2

Image Filtering
The following new and updated filters are available in Dragonfly 2022.2 to improveimage quality and yield information that otherwise would have been missed.

Manual 3D RBF Filter
This software release includes a Manual 3D RBF filter that uses a radial basis function tocorrect 3D non-uniform illumination and beam hardening effects. You should note that
the Manual 3D RBF filter requires at least three(3) seed points to be placed in
representative regions on image slices. In contrast to the Manual RBF filter, you can place
the seed points on different image slices with the Points tool, which is circled below.
Options for Manual 3D RBF Filter

A good indication that a dataset requires shading correction can be provided by theintensity profile of rulers drawn across the image, as shown below.
Intensity profile in Z axis before (in yellow) and after correction (in blue)

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Ring Artifact Removal Filter
In cases in which ring artifacts do not originate at the center of a dataset, you can now
pick the center of the rings for filtering. You can pick the center of the ring with the
Points tool, which is circled below.
Options for the ring artifact removal filter

Configurable Actions
The following configurable actions are available for working with the Ring Artifact
Removal filter and the Manual 3D RBF filter.
Configurable actions for filtering

ActionDefault Key

Create point for ring artifact removal filter

Left mouse

Edit point for ring artifact removal filter

Left mouse

Create points for 3D RBF filter

Left mouse

Edit points for 3D RBF filter

Left mouse

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Dragonfly Release Notes Version 2022.2

Lossless Compression with ZSTDThis software release features the implementation of zstd for saving objects, such asimage data, ROIs, meshes, and so on, in the ORS Object file format (*.ORSObject
extension), as well as session files (*.ORSSession extension), with lossless compression.
Lossless compression is a class of data compression algorithms that allows the original
data to be perfectly reconstructed from the compressed data.
NOTE

The Zstandard, commonly known by the name of its reference implementation zstd, is a lossless datacompression algorithm developed by Yann Collet at Facebook and was released as open-source
software in 2016.

The option for saving ORSObject and ORSSession files with lossless compression isavailable in the Preferences dialog, as shown below.
Miscellaneous Preferences

NOTE

57

Compressed object files are NOT compatible with Dragonfly 2022.1 or prior versions. This means that
objects saved with compression cannot be loaded in earlier versions of Dragonfly.

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3D Renderings and Settings
This software release includes new rendering options and optimizations that should
result in improved performance and interactivity, particularly for users who typically
deploy correlative workflows and work with large datasets.
Additional improvements for 3D visualizations include automatic detection of theappropriate focal length by collision detection with what is in the scene for the
Autofocus option, the suppression of moiré effects, and the elimination of darkening incases in which air surrounds a scanned object.
Multi-volume rendering in a correlative workflow

Autofocus feature

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Dragonfly Release Notes Version 2022.2
Suppression of moiré effects on the boundaries of a rendered dataset

NOTE

With improved sampling, and the exclusion of voxel fragments greater than an accumulated
threshold at low cost, the Maximize quality option has been removed from the Scene’s Views
Properties panel.

NOTE

The option Flip ROI lighting was also deprecated in this software release. This is now applied
automatically whenever a region of interest or multi-ROI is not facing the camera.

Field of View Control
A new slider on the Scene Views Properties panel, shown below, lets you zoom in andout on objects in a 3D view without moving the camera. In this case, clipping does notoccur when zooming in very closely and lighting does not change.
Field of View slider

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New Features and Product Enhancements
Far and close field of view settings

Plane Settings
In some cases, you may want to make a visual plane solid in order to view the full area
covered by the plane. This option is now available in this software release.
Visual plane shown as solid

The option to view a visual plane as solid is available in the Plane settings box, as shown
below.
Plane settings

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Lighting Controls for ROIs and Multi-ROIs
A number of additional lighting controls – Diffuse and Specular – are now available forfine-tuning the visualization of regions of interest and multi-ROIs when they are shown
without a dataset.
Lighting applied to an ROI without a dataset

These controls are available on the Data Properties and Settings panel in the Opacity andcolor box, as shown below.
Opacity and color box

Diffuse slider… Lets you vary the strength of diffuse light reflected from surfaces, which
is a characteristic of light absorbed by surfaces. Increasing the diffuse lighting with the
Diffuse slider will result in surfaces being more defined.
Specular slider… Lets you vary the strength of specular reflections originating from thesurfaces of an object and is a characteristic of light reflected from shiny surfaces.
Increasing this property with the Specular slider will result in increased reflections.
NOTE

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These new options can be better than converting an ROI or multi-ROI to a mesh for visualization
purposes.

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Edge Contrast Factor
In cases in which you want to highlight a segmented feature of interest within an object
while still showing the object’s structure, a new slider lets you precisely control the edge
contrast of images.
Image with edge contrast applied

Edge Contrast slider

NOTE

This control, which can be used in animated sequences, was previously available on the Windowleveling panel.

Interpolation
There is now a drop-down menu for the 3D filtering, with options for Nearest, Linear,and Cubic interpolation. These Interpolation options replace the deprecated Cubic
checkbox. The addition of the Nearest filter mode in 3D lets you examine datasets withno interpolation. This can particularly useful when viewing datasets exported from PuMA,
so that the physical values conveyed are not altered by interpolation.

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The additional interpolation options are available in the 3D settings box, as shown below.
3D settings

Render ModeYou should note that the Render Mode settings, which can be applied to images in 3D
views, has been moved to the Data Properties and Settings panel on the right sidebar
from the Scene Views Properties panel on the left sidebar.
3D settings

With this change, each dataset in the 3D view can be rendering in a different mode —Default, MIP, or ISO.

Virtual Floor Size Adjustment
The Virtual Floor panel now includes the option to adjust the size of a virtual floor.
Virtual Floor panel

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Movie Maker
The following enhancements, described below, were implemented for the Movie Maker
in version 2022.2. You should also note that rebuilding movies is now much faster andmore robust, particularly in cases in which objects were created and added to timeline
after the Movie Maker was launched.

Save and Load TemplatesThis Dragonfly release includes the option to save and load Movie Maker timelines –either in the Movie File (*.mvf extension) format or in the generic Template File (*.tpl
extension) format. In either case, only the camera manipulations, clipping, and otherproperty changes will be saved in the file, which is more efficient that saving all objects
and the timeline in a session file.
The options to save and load movie templates are available on the Movie Maker panel,as shown below.
Save Movie Timeline to file

Load Movie Timeline from file

NOTE

After loading a Movie Template, you can click the Update Thumbnails button to refresh theappearance of the thumbnails on the timeline.

Movie File format… This format is recommended in cases in which you want to save aparticular animated sequence and then reproduce the sequence with the same orsimilarly sized objects of the same data type. You should note that the number ofavailable objects and the name of each object on the Data Properties and Settings panel
must be same to load a Movie File template.
Template File format… This format is recommended in cases in which you to save a‘generic’ animated sequence that could be re-used for other objects. In this case, thesize, position, and spacing of the objects in the animated sequence will be adapted to fit
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the movie template stage and properties. You should note that the number of available
objects on the Data Properties and Settings panel must be same to load an animatedsequence saved as a Template File.

Visual Effects of Shapes
You can now interpolate the visual effects — such as clipping, window leveling, opacity,and others — that are applied between keyframes to shapes, such as boxes, capsules,
and cylinders.
Clipping and visual effects applied to a keyframe in an animated sequence

Graph Clipping
You should note that clipping of graphs is now supported in the Movie Maker, as shownin the timeline below.
Movie Maker timeline

NOTE

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When clipping graphs, nodes are considered to be either in or out, but edges will be clipped by theboundary box.

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Overlays
You can now change the position and opacity of text and image overlays between
keyframes, also well as smooth transitions by applying interpolation. These options areavailable in the Advanced Animations Options dialog, as shown below.
Advanced Animations Options dialog

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Dragonfly Release Notes Version 2022.2

Window Leveling
For fine-tuning the tonal range of 3D renderings, a new Gain and Bias Window Levelingmode is available in version 2022.2.
3D renderings of Gila monster head

The Gain and Bias window leveling mode, as well as the Gain slider, is available on the
Window Leveling panel, as shown below.
Window Leveling panel

You should note that an image’s tonality is initially represented as a straight diagonalline on the histogram. You can then adjust the shape of the curve with the Gain slider toadjust its tonal range.

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Regions of Interest
A number of new options available in this software release for working with regions ofinterest. These new options are described below.

Adaptive Watershed
For cases in which you cannot get good segmentation results using a global threshold,this software release provides an adaptive watershed that separates single components
in a manner consistent with the integrity of the connected particles.
Right-click the required ROI and then choose Adaptive Watershed in the pop-up menu
to compute an adaptive watershed on an initial segmentation. The output is a multi-ROI
with all particles indexed and identified.
Input ROI and related dataset (left) and the resulting multi-ROI (right)

The adaptive watershed can segment a particle image into different regions by treating
its inverse distance map as a landscape and the local minima as markers. By labeling
each segmented region with a unique index, different particles can be separated,
identified, and subsequently analyzed. However, you should note that the performanceof the algorithm may be compromised if the particles are irregularly shaped, overlapped,or are overly connected.
NOTE

Refer to Sun, H.Q., and Y.J. Luo, Adaptive watershed segmentation of binary particle image, Journal of
microscopy 233.2(2009): 326 – 330 for more information about the implemented method. Available
at: https://onlinelibrary.wiley.com/doi/10.1111/j.1365-2818.2009.03125.x.

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Act on All Timesteps
In cases in which you create regions of interest on 4D image data, you can now choose
to have applicable operations — such as clear, invert, and undo, as well as defined
ranges, morphological operations, and image operations — applied to all timesteps
automatically. The option Act on all timesteps, which is circled below, is available
whenever you create a new ROI.
ROI Tools panel

Selection Text Annotation
Whenever you define a range for a threshold segmentation, a new text annotation willappear in the 2D views of the current scene. This annotation, shown below, lets you
adjust the range by dragging.
Selection text annotation

Do the following to adjust the defined range with the Selection text annotation:

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

Drag left and right to decrease or increase the selected range (window width)Drag up and down to decrease or increase the center value.

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New Binary Image (8 bit) from ROIThis item, which is available in the pop-up menu for regions of interest, lets you create a
new binary image from an ROI in which all labeled voxels are assigned a selected value
and all unlabeled voxels are assigned a value of 0. You can choose a value for the labeledvoxels in the Value for Labeled Voxels dialog, as shown below.
Value for Labeled Voxels dialog

Multi-ROIs
A number of additional options for working with multi-ROIs are available in this software
release. These new options are described below.

Gridded Multi-ROIs
In some cases, you may want to make a ‘gridded’ multi-ROI from a region of interest or
another object. For example, to analyze a subsection of a large region of interest or
multi-ROI, as shown below.
Gridded multi-ROI of a large crack

Gridded multi-ROIs can be created from image data, regions of interest, and multi-ROIs.
Select the required option, as described below, in the pop-up menu to open the Grid
Cell Size dialog, and then select the required sampling size for the generated grid.
Make as a Gridded Multi-ROI Intersection… Creates a gridded multi-ROI that includes
only voxels that intersect with the selected region of interest.

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Make as a Gridded Multi-ROI of Same Shape… Creates a gridded multi-ROI in the same
shape as the selected image data, region of interest, or multi-ROI.

Grow Labels to Feret Box With Otsu
This software release includes the option to grow labels to their Feret box within thelocal lower or upper Otsu of a selected dataset. Right-click the required multi-ROI andthen choose Grow Labels to Feret Box with Otsu in the pop-up to open the dialog
shown below.
Grow Labels to Feret Box with Otsu dialog

In the dialog, you can:

Select the object to extract to the local lower or upper Otsu values.



Grow the Feret box by a specified amount.

Select local lower or upper Otsu.

NOTE

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All labels will be processed in order and voxels that intersect with the initial labels within the Feretbox of each label will be added.

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Meshes
A number of new options for generating meshes and using meshes as a mask, as well as
optimizations for computing mesh deviation maps, are available in this software releasefor 3D modeling.

Generate Meshes from Spherical Harmonics
For simulation and experimentation purposes, this software release provides the option
to generate meshes from spherical harmonics.
Choose Utilities > Generate a Mesh from SH on the menu bar to open a series of
dialogs that let you enter the order and degree for the harmonic function. Meshes canbe generated with a number of different settings, as shown below.
Meshes generated from spherical harmonics

Settings for the above meshes were as follows: L=4, M=2 (left), L=6, M=4 (middle), L=8, M=6 (right).

Spherical harmonic functions are defined by an order (L), which specifies the number ofpolynomial terms that a harmonic function contains, and a degree (M), which describes
the number of basis functions to be computed for each order. Relatively smoothfunctions can be obtained using low order spherical harmonics, with increasing ordercorresponding to a greater number of frequencies captured by the spherical harmonics.

Generate 3D Tetrahedral Meshes
This software release includes the option to generate 3D tetrahedral meshes fromselected classes of a multi-ROI. 3D tetrahedral meshes, which are boundary meshes filledwith tetrahedrals that can fit arbitrary shaped geometries, are often used forcomputational fluid dynamics and other simulations.

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Dragonfly Release Notes Version 2022.2
Clipped 3D tetrahedral mesh (orange) inside a clipped surface mesh (blue)

Do the following to generate a 3D tetrahedral mesh:

Select the classes in a multi-ROI for which you want to generate a 3D tetrahedral
mesh.



Right-click the multi-ROI and then choose Generate 3D Tetrahedral Mesh in the
pop-up menu.

Enter the required values in the Generate 3D Tetrahedral Mesh dialog, shown
below.

NOTE

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Tetrahedral elements are commonly constructed as equilateral, such as in systems with circularcurvature, or they could be constructed as isosceles tetrahedra when a system has asymmetry.
Tetrahedral elements could also be totally unstructured and adapted to arbitrary geometries withhigh accuracy. The accuracy can be much higher than a simple cubic grid applied in 3D or a squaregrid applied in 2D.

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Masking with Meshes
This software release provides the option to use meshes as a mask. For example, to maska dataset, as shown below.
3D rendering with mesh (left) and 3D rendering masked with mesh (right)

The option to use a mesh as a mask is available in the 3D settings box, as shown below.
3D settings for meshes

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Distance Between Meshes
For optimizing computations of true distances between meshes, this software releaseprovides a Search Distance dialog for specifying the maximum search distance when
computing distance maps.
Signed distance map

Right-click the required mesh and then choose Signed Distance Map or Deviation Map
in the pop-up menu. You can then choose a reference mesh in the Choose a Mesh as aReference dialog and a search distance in the Search Distance dialog, shown below.
Search Distance dialog

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Vector Fields
A new setting for vector fields — Normalized modulus — is available in this software
release for visualizing vector fields. If applied, the length of each directed line segment
will be equal, regardless its magnitude.
Close-up of vector without normalized modulus (left) and with normalized modulus (right)

The option to apply the Normalized modulus option is available in the Vector fieldsettings box, as shown below.
Vector field settings

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Floating Captions for Annotations
Captions for rulers, paths, angles, and regions can now be floated and then repositioned
manually anywhere within a view to avoid overlapping. The option to float a caption isavailable in the Properties box, as circled below.
Properties for annotations

Below is an example of using floating captions for a series of rulers.
Floating captions

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Selected annotation properties are now recorded by Dragonfly’s Macro Player, which can include
position, font size, control point appearance, and others. However, the setting ‘Automatically update
captions’ for Points set is not recorded.

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Menu Bar Changes
The following new items are available on the menu bar in Dragonfly 2022.2. Otherchanges are also listed here.

Workflows Menu
A number of changes, listed below, were implemented in the Workflows menu.
Workflows menu changes

Measurement

Description

RAD-DENDRO

Opens the Radiographic Dendrochronology Wizard, which provides a guided workflow
for analyzing wood samples or planks (see Radiographic Dendrochronology on page
50).

Utilities Menu
A number of changes, listed below, were implemented in the Utilities menu.
Utilities menu changes

Measurement

Description

Feature Analysis (alpha)

Opens the Feature Analysis dialog, in which you can perform cross-table analysis offeature vectors and other measurements (see Feature Analysis on page 7).

Generate a Mesh from SH

Lets you generate meshes from spherical harmonics for simulation and experimentation
purposes (see Generate Meshes from Spherical Harmonics on page 72).

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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 2022.2.

Dataset Contextual Menu
A number of new items, listed below, are available in the Dataset pop-up menu.
Dataset pop-up menu changes

Item

Description

Derive New from Current View,
with Isotropic Spacing

Lets you create a new 3D volume with isotropic spacing that is aligned to the selectedviewing angle of the current 2D view.

Make as a Gridded Multi-ROI ofSame Shape

Creates a gridded multi-ROI in the same shape as the selected dataset (see Gridded
Multi-ROIs on page 70).

3D Data Augmentation

Lets you generate a series of warped datasets (see 3D Data Augmentation on page 31).

Open Image Quality Metrics

Lets you evaluate and compare the image quality of previews and other single-slice
datasets with a number of different metrics (seeImage Quality Metrics on page 25).

ROI Contextual Menu
A number of new items, listed below, are available in the Region of Interest pop-up
menu.
ROI pop-up menu changes

Item

Description

Adaptive Watershed

Applies an adaptive watershed that separates single components in a mannerconsistent with the integrity of the connected particles (see Adaptive Watershed on
page 68).

Derive New from Current View,
with Isotropic Spacing

Lets you create a new 3D volume with isotropic spacing that is aligned to the selectedviewing angle of the current 2D view.

Make as a Gridded Multi-ROI
Intersection

Creates a gridded multi-ROI that includes only voxels that intersect with the selected
region of interest (see Gridded Multi-ROIs on page 70).

Make as a Gridded Multi-ROI ofSame Shape

Creates a gridded multi-ROI in the same shape as the selected region of interest (seeGridded Multi-ROIs on page 70).

New Binary Image (8 bit) from
ROI

Lets you create a new binary from an ROI in which all labeled voxels are assigned aselected value and all unlabeled voxels are assigned a value of 0 (seeNew Binary Image (8 bit) from ROI on page 70).

3D Data Augmentation

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Lets you generate a series of warped datasets (see 3D Data Augmentation on page 31).

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Multi-ROI Contextual Menu
A number of new items, listed below, are available in the Multi-ROI pop-up menu.
Multi-ROI pop-up menu changes

Item

Description

Derive New from Current View,
with Isotropic Spacing

Lets you create a new 3D volume with isotropic spacing that is aligned to the selectedviewing angle of the current 2D view.

Fill Multi-ROI with New Class

Automatically adds a new complimentary class to the multi-ROI in which all previously
unlabeled voxels are labeled.

Make as a Gridded Multi-ROI ofSame Shape

Creates a gridded multi-ROI in the same shape as the selected region of interest (seeGridded Multi-ROIs on page 70).

3D Tetrahedral Mesh Generator

Lets you generate 3D tetrahedral meshes from selected classes of a multi-ROI (seeGenerate 3D Tetrahedral Meshes on page 72).

3D Data Augmentation

Lets you generate a series of warped datasets (see 3D Data Augmentation on page 31).

Grow Labels to Feret box with
Otsu

Lets you grow labels to their Feret box within the local lower or upper Otsu of a
selected dataset (see Grow Labels to Feret Box With Otsu on page 71).

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Preferences Changes
A number of new items, listed below, and other changes have been implemented forsetting the application preferences.
Click Preferences on the Status bar or choose File > Preferences on the Menu bar toopen the Preferences dialog.

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

Item

Description

3D Settings
Show 2D view plane(s) by default

If selected, 2D view planes will appear in the 3D view by default.

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

Item

Description

Default compression for object
and session files

Lets you choose to either compress object and session files with zstd when saving or to
not apply compression (see Lossless Compression with ZSTD on page 57).

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

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Item

Description

GPU card for Deep Learning

If you have multiple GPUs available, you can select your preferred GPU(s) for training in
the Deep Learning Tool and Segmentation Wizard, as shown below (see Deep Model
Training on Multiple GPUs on page 14).

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Publicly Available Source Code

System Requirements
This release supports the base system requirements for Windows and Linux.
NOTE

As of Dragonfly version 2022.2, Windows 10 or 11 is required for Deep Learning.

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

Item

Version 2022.2

Previous version (2022.1)

TensorFlow

2.4.1

2.4.1

Qt

5.12.95.12.9Python3.8.12

3.8.6

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

TensorFlow
A number of Dragonfly’s artificial intelligence applications, such as the Deep Learning
Tool, the Segmentation Wizard, and Segment with AI, use TensorFlow APIs to facilitate
training and inference of deep neural networks. Created by the Google Brain team,TensorFlow is a 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/tensorflow/releases/tag/v2.4.1.

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/5.12/5.12.9/.
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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 isable to generate artificial microstructures that mimic real materials and to computesimple morphological properties such as porosity, volume fractions, pore diameter, andspecific surface area. Additional capabilities include the determination of effective
thermal and electrical conductivity (both radiative and solid conduction - including theability to simulate local anisotropy for the latter); effective diffusivity and tortuosity fromthe continuum to the rarefied regime; techniques to determine the local material
orientation, as well as the mechanical properties (elasticity coefficient), and thepermeability of a material.
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.

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Extending Dragonfly

Extending Dragonfly
Dragonfly’s Infinite Toolbox, an open exchange platform for the Dragonfly user
community, lets you submit, browse, and download extensions that implement newfeatures and workflows for all your image processing or analysis needs.
Choose Utilities > Infinite Toolbox on the menu bar to open Dragonfly’s Infinite
Toolbox.

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

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Learning Dragonfly
You’ll find some great free resources, such as our latest instructional videos, recorded
webinars, and user forums, at https://www.theobjects.com/dragonfly/. You can also takeadvantage of the course offerings listed below to further benefit from our 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.
You can check for updates at https://www.theobjects.com/dragonfly/workshops.html.

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.
Online training is available upon request. Contact support@theobjects.com for
information about availability.

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Reporting Issues

Reporting Issues
Dragonfly includes an integrated reporting module that lets you include comments andother information along with generated DMP and log files whenever you encounter anissue that results in a crash or application freeze.
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

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