D RAGONFLY 2021.1
R ELEASE N OTESCOPYRIGHT© 2021 Object Research Systems (ORS) Inc. All rights reserved.
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COVER IMAGES

Training of deep neural networks in Dragonfly’s Segmentation Wizard for the segmentation of hardened block (bottom) and 3D
visualization of fully segmented dataset (top). Data courtesy of the Digital Rocks Portal (www.digitalrocksportal.org/).

Title
Dragonfly 2021.1 Release Notes
Release date2021-01-26
Reference number TM021-A-09

Object Research Systems (ORS) Inc. • 760 Saint‐Paul West, Suite 101 • Montreal (QC) H3C 1M4 • www.theobjects.com

Contents
This document describes the new features, product enhancements, and otherimprovements implemented in the Dragonfly and Dragonfly Pro 2021.1 software
release. You should read these release notes before you install this new version.

New Features and Product Enhancements . . . . . . . . . . . . . . . . . . . . . . .5
Segmentation Wizard. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

Model Generation Strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
Default Strategies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
Updates for the Input Tab . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
Updates for the Models Tab. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
Updates for the Settings Tab . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
Training Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
Model Parameters. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
Rename and Save Multiple Models. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
Data Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12Open Wizard Session from the Menu Bar . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

New Deep Learning Architectures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
Attention U‐Net . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
LinkNet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
U‐Net++ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

Updated Deep Learning Architectures . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
PSPNet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

CT Reconstruction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

Stripe Removal Filters for Pre‐Processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
Updates for the CT Rotation Finder . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
Support for YXLON Micro‐CT Scans. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
Metadata Parser for SKYSCAN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

Distributed Tasks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

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Distributed Tasks Observer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
Distributed Task Dialog . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
Updates for the Deep Learning Tool . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

ROI Painter Panel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
New ROI Painter Tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
Local Otsu Brush . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
Adaptive Gaussian Brush . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
Point & Click Brush. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
Point & Click Outline Brush Preferences . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
Path Brush . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

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New Features and Product Enhancements

Export Label Histograms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
Morphological Operations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
ROI and Mesh Opacity Settings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
Window Leveling Modes for 3D Views. . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
W/L Mode Preferences. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

Image Filtering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

Thresholding for Unimodal Images . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
Updates for Image Filters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

Three‐Point Circle Tool . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
Thickness Meshes. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
Organizer Updates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
Search Criteria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
Organizer Settings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

Plug Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
Movie Maker. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
2D Animations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
Support for H5 Files . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
Additional Support for CZI Images. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
Export Images to VTK . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

Menu Bar Changes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .37

File Menu. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
Utilities Menu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

Changes to Contextual Menus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .38

Dataset Pop‐Up Menu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
Multi‐ROI Pop‐Up Menu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

Preferences Changes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .39

Views Preferences . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
Configurable Actions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

Resolved Issues . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .40

System Requirements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
TensorFlow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

Extending Dragonfly . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
Requesting New Features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
Learning Dragonfly . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .42

Dragonfly Workshops. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
Online Training . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

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Reporting Issues. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .43

Dragonfly Release Notes Version 2021.1

New Features and Product Enhancements
Segmentation Wizard

New Features and Product Enhancements

The 2021.1 software release for Dragonfly and Dragonfly Pro provides new features,product enhancements for the Segmentation Wizard and CT Reconstruction, new deeplearning architectures, as well as support for additional file formats.

Segmentation Wizard
This Dragonfly release features a number of significant advancements for the
Segmentation Wizard, which provides an easy-to-use, guided workflow for
implementing powerful deep learning and machine learning segmentation of multidimensional images.

Model Generation Strategy
The reformatted Model Generation Strategy dialog provides new options for creatingand editing model generation strategies, as well as for quickly reviewing and editing
Deep Learning model settings and feature presets for Machine Learning (Classical)
models.
The Model Generation Strategy dialog, shown below, appears when you click Train andthe models list is empty. You can also click the Open button the Models tab to open the

dialog (see Updates for the Models Tab on page 9).
Model Generation Strategy dialog

The Model Generation Strategy dialog includes a list of the ORS default strategies anduser-defined strategies, as well as a list of each model included in the selected strategy.
Each model in the selected strategy can also be expanded to review and edit its settings.
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You can do the following in the Model Generation Strategy dialog:• Add a new strategy, import and export strategies, duplicate a strategy, and delete a
strategy.
• Set a strategy as the default.
• Edit user-defined strategies, including renaming, adding, and removing models.

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6 Contents

New Features and Product Enhancements• Edit the model parameters for Deep Learning models, as shown below.

• Add and remove feature presets for Machine Learning (Classical) models.

N OTE ORS defined strategies are read-only and all editing functionalities are disabled for these strategies.

The controls and options that are now available in the dialog are described below:
Controls and options for the Model Generation Strategy dialog
Icon

Description

Expand all models

Expands all models in the list so that you can review and edit the parameters of Deep Learningmodels and the feature presets of Machine Learning (Classical) models.

Collapse all models

Collapses the list of all models.

Select all models

Selects all models in the list.

Deselect all models

Deselects all models in the list.

Add new model

Adds a new model to the selected strategy. You can choose the type of model in the drop-down
menu, as shown below.

Remove selected model

Removes the selected model from the selected strategy.

N OTE ORS defined strategies are read-only. You cannot add models to these strategies.
N OTE ORS defined strategies are read-only. You cannot remove models from these strategies.

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N OTE Selected models will be generated whenever you click Continue or Apply.

New Features and Product Enhancements
Segmentation Wizard

Icon
Add new features preset

Description
Lets you add a new features preset to the selected Machine Learning (Classical) model. You
can choose features presets in the drop-down menu, as shown below.

N OTE ORS defined strategies are read-only. You cannot add feature presets to any Machine
Learning (Classical) model in these strategies.
Remove selected features
presetRemoves the selected features preset from the selected Machine Learning (Classical) model.

Save

Saves any changes you made to the model generation strategy or to the settings of a model
within a user-defined strategy.

ReloadReloads the current strategy.

Continue/ApplyContinue… Is available when the Model Generation Strategy dialog appears after clicking

N OTE ORS defined strategies are read-only. You cannot remove feature presets from any
Machine Learning (Classical) model in these strategies.

‘Train’. In this case, all selected models in the current strategy will be generated when training
begins.

Apply… Appears when the Model Generation Strategy dialog is opened from the Models tab.
In this case, all selected models will only be generated.

N OTE You can choose to train or not train generated models on the Models tab on the
Segmentation Wizard panel.
Close

Closes the dialog.

Default Strategies
Two ORS-defined default strategies — Quick Start and High Accuracy — are available

for the Segmentation Wizard in this software release.

Quick Start… The models in this strategy, which are listed below, generally provide
good results without excessive processing times.

• Random Forest with features presets Morphological, Gaussian_MS, and Neighbors.
• Random Forest with features preset Activation Map 2.
• U-Net (depth = 3, initial filters count = 64).

High Accuracy… The models in this strategy, which are listed below, are moredemanding than those in ‘Quick Start’, but can provide a higher level of accuracy.

TM021-A-09• Random Forest with features preset ‘Activation Maps 2’.
• U-Net (depth = 4, initial filters count = 64, multi-slice input with 3 slices).
• Sensor 3D (depth = 3, initial filters count = 64).

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8 New Features and Product Enhancements
Segmentation Wizard

Updates for the Input Tab
The following options are now available on the Input tab, shown below. These options

let you change the image modality shown in the views, control the visibility of frames
and label highlights, as well as save, import, and fill frames.
Input tabImage modalities… This new addition to the Input tab appears only when multiple

datasets were selected for multi-modality training. Selecting an image in the list willautomatically change the modality displayed in the workspace views and update theimage data selected for defining a range in the ROI Tools panel.

Visibility controls… This new control, which appears on the left-side of the frames list,
lets you show or hide a frame and the highlight applied to labeled voxels within theframe.

Import and Save… These new options for Dragonfly 2021.1 let you save frames as alabeled multi-ROI in the same shape as the input dataset(s), as well as to import saved

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frames to resume or duplicate a workflow.

Dragonfly Release Notes Version 2021.1

New Features and Product Enhancements
Segmentation Wizard

F ILL… This modified item, which was previously ‘Fill from Object’, now lets you also fill

all new frames from the currently selected model prediction.

Updates for the Models Tab
The Models tab now includes the option to open the Model Generation Strategy dialog,
in which you can choose a model generation strategy, create new strategies, edit

strategies, and generate models (see Model Generation Strategy on page 5). This new
option is circled below.
Models tab

Updates for the Settings Tab
The Settings tab now includes the option to allow duplicate models. This option, which

is circled below, should be enabled if you plan to generate and train similar models thatare set with different parameters.

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Settings tab

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10 New Features and Product Enhancements
Segmentation Wizard

Training Parameters
A number of new options — Save as Defaults, Load Defaults, and Reset Defaults — areavailable for the edited training parameters of Deep Models. Right-click the Deep Model
you need to edit on the Models tab and then choose Update Training Parameters in the
pop-up menu to open the Training Parameters dialog, shown below.
Training Parameters dialog

Options for saving and applying edited training parameters
Description
Save as Defaults

Saves the current settings as the default values, which will be applied to newmodels.

Load Defaults

Restores the settings to the last saved default values.

Reset Defaults

Resets all settings to their original values.

Apply toThe ‘Apply to’ options have been streamlined to include the following:

Current deep model… If selected, all changes will be applied to the selected
Deep Learning model only.

All current deep models… If selected, all changes will be applied to all Deep

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Learning models in the current Segmentation Wizard session.

Dragonfly Release Notes Version 2021.1

New Features and Product Enhancements
Segmentation Wizard

Model Parameters
The model parameters for Deep Learning models selected in the Model Generator dialog
have been renamed and also include informative tooltips, as shown in the following
screen capture.
Model Generator dialog

Rename and Save Multiple Models
As an additional change for the Segmentation Wizard in Dragonfly 2021.1, whenever
you exit the Segmentation Wizard, or click the Save button on the Models tab, a new
Save dialog now appears onscreen.
Save dialog

You can do the following in the reformatted Save dialog:• Choose the model or models that you want to save.

TM021-A-09• Rename a model before it is saved.

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12 New Features and Product Enhancements
Segmentation Wizard

Data Selection
The Data Selection dialog, shown below, now appears whenever you open the
Segmentation Wizard from data selected in the Data Properties and Settings panel.
Data Selection dialog

This change lets you easily choose the order of your inputs. You should also note thatmodalities order is important for training models and must be consistent for inference.

Open Wizard Session from the Menu Bar
Whenever you now choose to open the Segmentation Wizard from the Artificial
Intelligence menu, you can reload an available Segmentation Wizard session. This new
option is shown in the screen capture below.
Dragonfly menu bar

N OTE All frames, trained models, and training parameters are saved within Segmentation

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Wizard group objects. This lets you reproduce previous results, as well as share data withothers.

Dragonfly Release Notes Version 2021.1

New Features and Product Enhancements
New Deep Learning Architectures

New Deep Learning Architectures
The following deep learning architectures — Attention U-Net, LinkNet, and U-Net++ —are now available for the Deep Learning Tool and the Segmentation Wizard for training
semantic segmentation models.

Attention U‐Net
This novel attention gate (AG) model, which was originally designed for medical
imaging segmentation, automatically learns to focus on target structures of varying
shapes and sizes while suppressing irrelevant regions in input images. By highlighting
salient features only, the necessity of using explicit external tissue/organ localization
modules of cascaded convolutional neural networks (CNNs) is eliminated. Integrated
into the standard U-Net architecture, AGs can increase model sensitivity and prediction
accuracy.

R EFERENCE… Oktay et al. Attention U-Net: Learning Where to Look for the Pancreas,
arXiv, May 20, 2018 (https://arxiv.org/pdf/1804.03999.pdf).

LinkNetThis deep neural network architecture focuses on speed and efficiency for semantic
segmentation tasks. Compared to other algorithms, LinkNet can learn with a more
limited number of parameters and operations and still deliver accurate results.

R EFERENCE… Abhishek Chaurasia and Eugenio Culurciello, LinkNet: Exploiting Encoder
Representations for Efficient Semantic Segmentation, arXiv, June 14, 2017(https://arxiv.org/pdf/1707.03718.pdf).

U‐Net++
U-Net++ is a powerful architecture for medical image and semantic segmentation. Thisarchitecture is a deeply-supervised encoder-decoder network in which the encoder anddecoder sub-networks are connected through a series of nested, dense skip pathways.
The skip pathways help reduce the semantic gap between the feature maps of theencoder and decoder sub-networks.

R EFERENCE… Zhou et al. UNet++: A Nested U-Net Architecture for Medical Image

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Segmentation, arXiv, July 18, 2018 (https://arxiv.org/pdf/1807.10165.pdf).

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14 New Features and Product Enhancements
Updated Deep Learning Architectures

Updated Deep Learning Architectures
A number of updates and improvements for the PSPNet deep learning architecture areavailable in this software release.

PSPNet
The following changes were implemented for PSPNet, which can be trained for semantic
segmentation.

Parameter to control filters count… Although this architecture has high memory

demands, the newly implemented filters count controls should allow users with more
limited GPUs to now train models with this updated version of PSPNet.

Default Patch size… Although PSPNet requires a Patch size of at least 256, the default
has been reset to 512 for optimal performance.

In addition, a number of minor fixes were implemented for this architecture to improve

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data flow.

Dragonfly Release Notes Version 2021.1

New Features and Product Enhancements
CT Reconstruction

CT Reconstruction
The following updates are available for the Beta version of the CT Reconstruction
module in Dragonfly 2021.1.

Stripe Removal Filters for Pre‐Processing
The following stripe and ring artifact removal filters are now available for pre-processing
acquisition data prior to reconstruction.

Wavelet‐Fourier… Removes horizontal stripes and ring artifacts from the sinogramusing the Fourier-Wavelet (FW) based method.
Options for Wavelet‐Fourier stripe removal

The following adjustable parameters are available for the Wavelet-Fourier stripe
removal filter. You should note that you can generate multiple previews to test yoursettings prior to reconstructing data and then import the inputs from the best preview.
Adjustable parameters for Wavelet‐Fourier stripe removal filter
Parameter

Description

Level

Lets you set the number of discrete wavelet transform levels.

Sigma

Lets you set the sampling parameter in Fourier space.

WaveletLets you select the type of the wavelet filter — ‘db5’, ‘haar’, or ‘sym5’.

N OTE Refer to Münch et al. Stripe and ring artifact removal with combined wavelet–fourier
filtering, Optics Express, 17(10):8567-8591, 2009 for more information.

Titarenko… Removes horizontal stripes and ring artifacts from the sinogram using

Titarenko’s approach.

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Options for Titarenko stripe removal

The adjustable parameters available for the Titarenko stripe removal filter include‘Number of blocks’ and ‘Alpha (damping factor)’. You should note that you can generate
any number of previews to test your settings prior to reconstructing data and thenimport the inputs from the best preview.

N OTE Refer to Miqueles et al. Generalized Titarenko’s algorithm for ring artefacts reduction,
Journal of Synchrotron Radiation, 21(6):1333-1346, 2014 for more information.

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16 New Features and Product Enhancements
CT Reconstruction

Updates for the CT Rotation Finder
A number of updates are available in this software release for finding the center ofrotation for reconstructing cone beam projections.
Choose Workflows > CT Reconstruction to start the CT Reconstruction module. You canthen open the CT Rotation Finder dialog, shown below, by clicking the Find Rotation

Center button in the Reconstruction engine box.

CT Rotation Finder dialog

Do the following to change the source offset and optimize the algorithm for computing
the center of rotation for cone beam projections:
1. Enter or import the acquisition parameters for the projection that you want toreconstruct in the CT Reconstruction dialog.
2. Click the Find Rotation Center button.

3. Select Image metric as the method in the CT Rotation Finder dialog.
4. Select a metric type — Shannon entropy or Sharpness — in the Metric type dropdown menu.

5. Check the Change source offset option.

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The CT Rotation Finder dialog appears.

New Features and Product Enhancements
CT Reconstruction

6. Click the Refresh button to compute a metric cost function vs. rotation center offset
curve, recommended.

N OTE If required, you can adjust the minimum and/or maximum offset ranges, aswell as the number of points, before you compute the curve.

7. Set the Minimum and Maximum offset values (pixelwise) to an optimal range,
recommended. Adjustments can be made on the curve by dragging the thresholds,
as shown below, or by entering new values in the edit boxes, as circled below.

N OTE You can modify the Number of optimizations and Precision (pixelwise), if
required.

8. Click the Update Preview button to compute the optimal center of rotation.

After the computation is complete, a preview of the result appears in the dialog.

9. Examine the preview with the Preview tools, recommended, and then do one or

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more of the following:• Click the OK button at the bottom right of the dialog to apply the computed

rotation center offset value and update the Source offset and Detector offset
values in the CT Reconstruction dialog.

• Click the Export Preview button to export the preview and its settings. You canimport the inputs from the best preview in the CT Reconstruction dialog.

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18 New Features and Product Enhancements
CT Reconstruction• Click the Shannon Entropy Curve or Sharpness Curve button to plot the curve of
the selected Image metric vs. the offset within the computed range, as shown
below.

• Select Manual range as the method and then generate additional previews
centered on the found value.

Support for YXLON Micro‐CT Scans
This software release provides support for reconstructing projection data acquired withYXLON micro-CT scanning systems. As shown in the screen capture below, acquisition
parameters can be imported from accompanying XML files (*.xml extension).
Acquisition parameters

N OTE You can use Dragonfly’s CT Rotation Finder to accurately find the center of rotation (seeUpdates for the CT Rotation Finder on page 16.

Metadata Parser for SKYSCAN
A geometry reader is available in this software release for parsing the metadata of cone

Select SkyScan in the CT scanner manufacturer drop-down menu and then click theImport button. You can choose the log file (*.log extension) with the scan metadata toload the acquisition parameters.

N OTE SKYSCAN is a registered trademark of the Bruker Corporation.

Dragonfly Release Notes Version 2021.1

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beam projections acquired with SKYSCAN micro-CT systems.

New Features and Product Enhancements
Distributed Tasks

Distributed Tasks
Dragonfly’s high-performance compute (HPC) framework lets you distribute computeheavy tasks to other Dragonfly workstations, dedicated HPC clusters, or to elasticallyinstantiated compute nodes on Amazon’s EC2 platform. Applicable tasks include training
deep models with the Deep Learning Tool and the Segmentation Wizard, reconstructing
projection data with the CT Reconstruction module, and generating simulations with
MC X-Ray. Support for the inference of trained deep models, numerical modeling for
LMB simulations, and image filtering will be available in future releases.

N OTE Distributed Tasks uses Python's distributed task queue Huey to support multi-threaded
task execution, scheduled execution, periodic execution, resubmitting failedtaskslkr444e, and task result storage.

N OTE Distributed Tasks is available as beta only and is a separately licensed feature forDragonfly. Contact sales@theobjects.com for more information about licensing forDistributed Tasks.

Updates in this software release for Distributed Tasks include the addition of a
distributed tasks observer and an updated dialog for monitoring the progress of queued
tasks for the Segmentation Wizard.

Distributed Tasks Observer
The newly implemented Distributed Tasks Observer dialog lets you monitor the progressof all tasks within a selected queue. Additional options let you:
• Clear completed tasks, tasks with error, pending tasks, or selected tasks.
• Resubmit processing tasks, as well as tasks with errors.
• Clear temporary data.
• Add and manage cloud computers.
Choose Utilities > Start Distributed Tasks Queue Observer on the menu bar to open theDistributed Tasks Observer dialog, as shown below.

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Distributed Tasks Observer dialog

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20 New Features and Product Enhancements
Distributed Tasks

Distributed Task Dialog
After launching a distributed task from the Segmentation Wizard for training a deepmodel, you can monitor training progress in Distributed Task dialog, shown below.
Distributed tasks can be launched by clicking the Train Distributed button.
Distributed Task dialog

You can do the following in the Distributed Task dialog:• Monitor the status — Pending, Executing, and Completed — and progress of alllaunched tasks.
• Cancel or stop a task.
• Display the details of the training task in progress. Click the Display Progress button
to open the Training dialog, shown below.

Updates for the Deep Learning Tool when using Distributed Tasks include additional
controls to resubmit a training task, stop training, and to display the details of training
progress. These are available by right-clicking a model on the Model Overview panel.
You should also note that the Model Status was updated for Distributed Tasks.

Dragonfly Release Notes Version 2021.1

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Updates for the Deep Learning Tool

New Features and Product Enhancements
ROI Painter Panel

ROI Painter Panel
The reformatted ROI Painter panel not only includes a more intuitive layout, but also
provides new tools, such as a Local Otsu brush and Adaptive Gaussian brush, fortackling challenging segmentation tasks. Updates for the Path tool and the Point & Click
tool are also included in this software release.
The reformatted panel for working in 2D and 3D views are shown below.
ROI Painter tools for 2D views (left) and 3D views (right)

N OTE Activation of the Round and Square Brushes now requires only a single click instead of

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two. Macros previously created with these brushes will only be supported for a limited
time.

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22 New Features and Product Enhancements
New ROI Painter Tools

New ROI Painter Tools
A number of new ROI Painter tools, including a set of local adaptive threshold brushes,
are now available for tackling challenging segmentation problems. You should note that
adaptive thresholding changes the threshold dynamically over an image by determining
the threshold for a pixel based on a small region around it. This can provide better
results for images with strong illumination gradients or shadows when compared toapplying a constant threshold. The assumption is that smaller image regions are more
likely to have approximately uniform illumination.
In addition to these new ROI Painter tools, this software release also provides an outline
mode for the Point & Click tool and a selectable thickness for the Path tool whenworking in Multi-slice mode.

Local Otsu Brush
The threshold value of the new Local Otsu brush is determined by maximizing thevariance between two classes of pixels of the local neighborhood defined by the area orvolume of the Round or Square brush. The dataset that provides the values forcomputing the threshold can be selected in the Image drop-down menu and you canchoose to paint within the Lower or Upper range.
Local Otsu brush settings

An example of painting with the Local Otsu brush set to the Upper class is shown in thescreen capture below.

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Painting with the Local Otsu brushDragonfly Release Notes Version 2021.1

New Features and Product Enhancements
New ROI Painter Tools

Adaptive Gaussian Brush
The threshold value of the new Adaptive Gaussian brush is the weighted sum ofneighbourhood values subtracted by a constant, where the weights are a Gaussian
window. The dataset that provides the values for computing the threshold can beselected in the Image drop-down menu and you can choose to paint within the Lower orUpper range. The neighbourhood is defined by the area or volume of the Round or
Square brush.

Adaptive Gaussian brush settings

Sigma… Is the amount of variation or dispersion of the set of values within the definedneighborhood.

Offset… Is the constant subtracted from the weighted sum of neighborhood to calculate
the local threshold value.

An example of painting with the Adaptive Gaussian brush set to the upper range isshown in the screen capture below.

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Painting with the Adaptive Gaussian brush

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24 New Features and Product Enhancements
New ROI Painter Tools

Point & Click BrushYou can now choose the level of connectivity — 4-connected and 8-connected forSingle-slice mode and 6-connected and 26-connected for Multi-slice and 3D modes —for working with the Point & Click brushes.
Other options for the Point & Click brush now include an Outline mode, which isactivated in the screen capture shown below.
Point & Click brushes and settings

An example of painting with the Point & Click brush in Outline mode is shown below.
Painting with Point & Click brush in Outline mode

You should note that in image processing, connectivity is the way in which pixels in 2D
images and voxels in 3D images relate to their neighbors.
When working in Single-slice mode in 2D views, connectivity is selectable as follows:

4‐connected… Propagation is done by strictly using the 4 edges adjacent to the current

seed and will result in the minimum number of connected pixels.

8‐connected… Propagation is done by strictly using the 4 edges and 4 corners adjacentto the current seed and will result in the maximum number of connected pixels.
When working in Multi-slice mode in 2D views and in 3D views, connectivity is

6‐connected… Propagation is done by strictly using the 6 faces adjacent to the current
seed and will result in the minimum number of connected voxels.

26‐connected… Propagation is done by strictly using the 6 faces, 8 corners, and 12

edges adjacent to the current seed and will result in the maximum number of connectedvoxels.

Dragonfly Release Notes Version 2021.1

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selectable as follows:New Features and Product Enhancements
Export Label Histograms

Point & Click Outline Brush Preferences
The following configured actions are available for the new Point & Click Outline brush.
Configured actions for the Point & Click Outline brush
ActionActivate point & click outline add

Defaults
LEFT CTRL

Activate point & click outline remove

LEFT SHIFT

Add to Point & Click Outline

LEFT CTRL + Left mouse

Decrease point & click outline brush size in add mode

LEFT CTRL + Mouse wheel down

Decrease point & click outline brush size in remove mode

LEFT SHIFT + Mouse wheel down

Increase point & click outline brush size in add mode

LEFT CTRL + Mouse wheel up

Increase point & click outline brush size in remove mode

LEFT SHIFT + Mouse wheel up

Remove from Point & Click Outline

LEFT SHIFT + Left mouse

Path BrushThe Path brush now has a selectable thickness when working in Multi-slice mode.
Thickness can be selected with the slider, as shown below.
Thickness slider for the Path brush

Export Label Histograms
This software release provides the option to export histograms of labeled data in thecomma-separated values (*.csv extension) file format. In this case, the data associatedwith each label will appear in a separate row and the number of pixels in each bin willappear in the columns.
Right-click the required multi-ROI labels in the Classes and scalar information box andthen choose Export Label Histograms to CSV in the pop-up menu. You can then choose

the image to extract the values for the histograms in the Choose an image to create

histograms dialog, shown below.

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Choose an image to create histograms dialog

N OTE You can select up to a maximum of 1000 labels for exporting label histograms to CSV.

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26 New Features and Product Enhancements
Morphological Operations

Morphological Operations
A number of new options are available on the ROI Tools panel for applying
morphological operations to regions of interest and the selected class(es) of multi-ROIs.
These are available in the Morphological operations box, which is shown below.
Morphological operations

Dimensionality… Lets you apply the selected morphological operation in 3D, in whichcase the operation will be applied to all axes, or in 2D, in which the case the operation
can be applied along the X, Y, or Z axis.

Range… You can choose selected slices, a range of image slices and an increment, or a
combination of both, to which the selected morphological operation will be applied. For
example, if you enter 1,2,3-5,6-15/3, then the operation will be applied to slices 1, 2,
3, 4, 5, 6, 9, 12, and 15.

ROI and Mesh Opacity Settings
You should note that for regions of interest and meshes, the applied color box is now
split diagonally between the original color on the left, while the color on the rightcorresponds to the original color at the applied opacity setting.
Examples of the reformatted color boxes are circled in the screen capture below.

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Data Properties and Settings panel

Dragonfly Release Notes Version 2021.1

New Features and Product Enhancements
Window Leveling Modes for 3D Views

Window Leveling Modes for 3D Views
Available in the 3D settings box on the Data Properties and Settings panel and circled
below, the additional Window Leveling modes that are available in Dragonfly 2021.1 canhelp you fine-tune renderings to optimize visualizations.
WL modes for 3D views

The following Window Leveling modes are available for 3D views in this software
release.
Description of the WL modes for 3D views
Mode

Icon

Description

Left-gated ramp

In this mode, the opacity of the values to the left of the lower threshold will be set to 0, while theupper threshold is disabled. The opacity of the values within the window are set within a linear
ramp, which can be modified by adjusting the local gamma on the Window Leveling panel.

Bi-gated ramp

In this mode, the opacity of the values to the left of the lower threshold will be set to 0, while theopacity of the values to the right of the upper threshold will be set as per the highest value
within the window. The opacity of the values within the window are set within a linear ramp,
which can be modified by adjusting the local gamma on the Window Leveling panel.

N OTE This mode was previously the default behavior for the next generation rendering engine.

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N OTE This mode was previously the default behavior for the legacy rendering engine.
Band pass ramp

In this mode, the opacity of the values to the left of the lower threshold and to the right of theupper threshold will be set to 0. The opacity of the values within the window are set within alinear ramp, which can be modified by adjusting the local gamma on the Window Levelingpanel.

Band pass Gaussian

In this mode, the opacity of the values to the left of the lower threshold and to the right of theupper threshold will be set to 0. The opacity of the values within the window are set within aGaussian window, which can be modified by adjusting the local gamma on the Window
Leveling panel.

Band pass constantIn this mode, the opacity of the values to the left of the lower threshold and to the right of theupper threshold will be set to 0. The opacity of the values within the window are set to a
constant 50%.

N OTE The local gamma cannot be adjusted in this mode.

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28 New Features and Product Enhancements
Window Leveling Modes for 3D Views

The following illustrations demonstrate some of the differences between the Window
Leveling modes for 3D views.
Left‐gated mode (left) vs. Bi‐gated mode (right)

In this case, gating the upper values with the Bi-gated mode results in a much moredetailed rendering.
Left‐gated mode (left) versus Band pass ramp

In this case, setting the highest values to 0 with the Band pass ramp results in much
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more detail in the mid-level values.

Dragonfly Release Notes Version 2021.1

New Features and Product Enhancements
Window Leveling Modes for 3D Views

Left‐gated mode (left) versus Band pass constant (middle) and Band pass Gaussian (right) modes

In this case, voxels within the middle and upper values appear with greater detail andsolidity in the Band pass constant and Band pass Gaussian modes.

W/L Mode Preferences
A new set of preferences are available for setting a default WL mode for 3D views. Thissetting — Default WL Mode — is available in the Views | 3D Settings preferences, as

shown below.

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Default WL Mode preference

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30 New Features and Product Enhancements
Image Filtering

Image Filtering
The following updates are available for filtering image data in Dragonfly 2021.1.

Thresholding for Unimodal Images
There are many methods that provide appropriate threshold values for an image with abimodal gray-level histogram. However, most of these methods cannot accurately andefficiently segment an image with a unimodal gray-level distribution. To address thislimitation, the Liu‐Song‐S threshold filter has been added to Dragonfly for image

filtering operations. By analyzing the morphology of the gray gradient distribution
histogram, an optimal threshold for image segmentation can be obtained for images
with unimodal gray-level distributions, as shown in the example below.
Original image (left) and the result of Liu‐Song‐S thresholding (right)

N OTE Refer to Song et al, A new automatic thresholding algorithm for unimodal gray-level
distribution images by using the gray gradient information, Volume 190, July 2020
(https://www.sciencedirect.com/science/article/abs/pii/S0920410520301674) for
additional information about this filtering technique.

Updates for Image Filters
A number of updates are available for the following filters in Dragonfly 2021.1.

DoG… The Difference of Gaussian (DoG) edge detection filter now supports 3D
structuring elements, as shown in the following screen capture.

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New kernel options for DoG filter

N OTE In some cases, it can be more useful to interpret the input data of an image processing
algorithm as a 3D volume instead of a sequence of 2D planes.

Dragonfly Release Notes Version 2021.1

New Features and Product Enhancements
Image Filtering

Morphological gradient… This filter now includes the option to compute the external
and internal gradients of the input image, circled below.
Gradient options for the Morphological Gradient filter

You should note that a morphological gradient is the difference between the dilation andthe erosion of an image. It is an image in which each pixel value indicates the contrast
intensity in the close neighborhood of that pixel. It is useful for edge detection andsegmentation applications.
Mathematically, the morphological gradient (G) of f is given by:
G(f) = D(f) - E(f), where D and E denote dilation and erosion with the selectedstructuring element, respectively.
The internal gradient is given by: Gi(f) = f - E(f).
The external gradient is given by: Ge(f) = D(f) - f.

N OTE The internal and external gradients are “ thinner” than the gradient. Gradient peaks are

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located on the edges, whereas the internal and external ones are located at each side ofthe edges. You should also note that Gi + Ge = G.

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32 New Features and Product Enhancements
Three‐Point Circle Tool

Three‐Point Circle Tool
The new Three-Point Circle tool for annotations, circled below, lets you create a circularregion by simply clicking on a succession of three points in a 2D view. The smallest
circle that can pass through the three points is computed automatically.
Three‐Point Circle tool (circled)

Measurements available for this region tool include the area and perimeter of the circle.
You can also adjust the thickness of the annotation, select a control point size and style,as well as plot a histogram of the data values within the region.

Configured Actions for the Three‐Point Circle Tool
The following configured actions are available for the Three-Point Circle tool.
Configured actions for the Three‐Point Circle tool
ActionCreate circle from 3 points

Default key
Left mouse

Thickness Meshes
You can now choose to export the X-Y-Z coordinates of thickness meshes along with thescalar values when you export data in the comma-separated values (*.csv extension)
file format.
Right-click the thickness mesh, choose Export Scalar Values in the pop-up menu, andthen select Export with coordinates in the Choose Scalar Values Slots to Export dialog,
as show below.

The exported comma-separated values (*.csv extension) file will include columns for
the X-axis coordinate, the Y-axis coordinate, and the Z-axis coordinate for each vertex,
along with the corresponding scalar value(s).

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Choose Scalar Value Slots to Export dialog

New Features and Product Enhancements
Organizer Updates

Organizer Updates
Updates for Dragonfly’s Organizer include the option to find projects that containsession groups, such as those created by the Bone Analysis, Plug Analysis, andSegmentation Wizards, as well as a reformatted Organizer Settings dialog.

Search Criteria
The selectable objects for finding projects now includes Groups on the Objects tab in
the Search Project dialog. This option is circled on the screen capture below.
Search criteria

Organizer Settings
The Organizer Settings are now arranged with a tree view on the left side and theparameters related to the current selection on the right. Click the Manage Settings

button in the Organizer dialog to open the Organizer Settings dialog shown below.
Organizer Settings dialog

The Organizer Settings are available as described below.

Network… Lets you designate a server port and also lists the current IP address.
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Roots… Lists the configured roots and lets you add and edit roots.
Project Tags… Lets you setup tags that are mandatory or optional when new projects

are created.

Sharing… Lets you enable sharing and to add authentication.
Maintenance… Provides the option to build the database. Rebuilding may be required if
you manually delete objects in a project or remove projects from a root folder outsideof Dragonfly.

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34 New Features and Product Enhancements
Plug Analysis

Plug Analysis
As an update for Dragonfly’s Plug Analysis module, which provides a dedicated workflow
for analyzing core plugs acquired from rock samples and other porous media, you can
now control the opacity of the selected range highlight when using the Manual Range
option to apply a threshold segmentation.
The option to adjust the opacity of the range highlight with the Range opacity slider,circled below, is available in the Preview box.
Range opacity slider

Movie Maker
This new release also features a number of improvements and enhancements for
Dragonfly’s Movie Maker.

Clip sweeps… The option to choose the direction of clip sweeps is now available. To add

a clip sweep, select the required keyframe and then choose a clip sweep in the Clip
sweep drop-down menu, as shown below.
Clip sweep options

2D Animations(*.png extension) from the 2D Animation panel.

N OTE Animated PNG files will play automatically in most internet browsers, such as Google

Chrome and Windows Edge. You should also note that animated PNGs generally havebetter quality than other file formats, such as GIF.

Dragonfly Release Notes Version 2021.1

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This new release provides the option to record 2D animations in the APNG file format

New Features and Product Enhancements
Support for H5 Files

Support for H5 FilesFor Dragonfly Pro users, this software release includes a reader for importing files
created with Xnovo’s GrainMapper3D software (*.h5 extension) from processed TXM
files. GrainMapper3D provides non-destructive 3D crystallographic imaging through
LabDCT on ZEISS Xradia 520/620 Versa X-ray microscopes made for laboratory use.
Choose File > Import GrainMapper File (h5) on the menu bar to automatically import a
selected GrainMapper file.

Visualization of a GrainMapper H5 file in 3D

N OTE Subsequent analysis of grain maps in Dragonfly Pro lets you filter, analyze, and visualize
data with parameters such as orientation, volume, sphericity, and others. Color coding
can be chosen according to the selected scalar value.

Dragonfly Pro’s .h5 reader imports:

AbsorptionCT/Data… As a 3D dataset of the CT reconstruction, which is usuallyregistered with the TXM reconstruction from the same experiment.

LabDCT/GrainID… As a 3D multi-ROI in which each GrainID appears in the Namecolumn, along with the number of corresponding labeled voxels and scalar slots forcompleteness, Eigen values, and Rodrigues vectors.
The items available for an imported H5 file are shown in the following screen capture of

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the Data Properties and Settings panel.

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36 New Features and Product Enhancements
Additional Support for CZI Images

Imported .h5 file data in the Data Properties and Settings panel

N OTE GrainMapper3D is a registered trademark of Xnovo Technology ApS.
N OTE This option is available for Dragonfly Pro only. Contact ORS for information about theavailability of this version of Dragonfly.

Additional Support for CZI Images
The additional support for importing CZI files in Dragonfly Pro now includes
automatically reading stage positions from the metadata in order to orient dataappropriately. You should also note that a known issue with scaling has also been
resolved.

N OTE The option to import CZI files is available for Dragonfly Pro only. Contact ORS for
information about the availability of this version of Dragonfly.

Export Images to VTK
The option to export image data in the VTK (*.vtk extension) file format is available in

Right-click the data you need to export in the Data Properties and Settings panel andthen choose Export > Export Image to VTK in the pop-up menu. In this case, a channel
as a vtkStructuredPoints with the channel data as a scalar slot will be exported.

Dragonfly Release Notes Version 2021.1

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this software release.

Menu Bar Changes
File Menu

Menu Bar Changes

The following new items are available on the menu bar in Dragonfly 2021.1.

File Menu
A number of new items, listed below, are available in the File menu.
File menu changes
Description
Import GrainMapper File(h5)

Lets you import files created with Xnovo’s GrainMapper3D software (*.h5 extension) from processed TXM
files (see Support for H5 Files on page 35).

Utilities Menu
A number of new items, listed below, are available in the Utilities menu.
Utilities menu changes
Description
Opens the Distributed Tasks Consumer dialog, in which you can monitor the activity of consumers within aselected queue, as well as de-register synched consumers.

Start Distributed Tasks
Queue Observer

Opens the Distributed Tasks Observer dialog, in which you can clear completed tasks, tasks with errors,
pending tasks, or selected tasks, as well as resubmit processing tasks and clear temporary data (seeDistributed Tasks Observer on page 19).

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Start Distributed Tasks
Consumer

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38 Changes to Contextual Menus
Dataset Pop‐Up Menu

Changes to Contextual Menus

The following changes for the contextual menus available in the Data Properties andSettings panel are implemented in Dragonfly 2021.1.

N OTE The item ‘Extract Action Log from Object’ was renamed as ‘Extract Object History as

Macro’ for this software release. Extracted macros not only provide an audit trail to help
troubleshoot processing issues, but can be edited and replayed to create new objects.

Dataset Pop‐Up Menu
A number of new and modified items, listed below, are available in the Dataset pop-up

menu.

Dataset pop‐up menu changes
Description
ExportTwo additional items now appear in the Export submenu.

As RBG… This option, which lets you export selected RGB channels in the BMP, JPEG, and PNG fileformats, was previously available as a stand-alone item in Dataset pop-up menu.

To VTK… Lets you export image data in the VTK (*.vtk extension) file format (see Export Images to VTK
on page 36).

Multi‐ROI Pop‐Up Menu
A number of new items, listed below, are available in the Multi‐ROI pop-up menu in the
Data Properties and Settings panel.
Multi‐ROI pop‐up menu changes
Description
Lets you export label histograms in the comma-separated values (*.csv extension) file format (see Export
Label Histograms on page 25).

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Label Histograms to CSV

Dragonfly Release Notes Version 2021.1

Preferences Changes
Views Preferences

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
Description
3D Settings
Default WL Mode

Lets you select a default window leveling mode for 3D renderings (see Window Leveling Modes for 3D
Views on page 27).

Configurable Actions
New configurable actions are available for annotations and the Point & Click Outline
brush. Chose File > Preferences > Configurable Actions to access the configurable

actions for Dragonfly.

Three‐Point Circle tool… See Three-Point Circle Tool on page 32 for the configured

actions that let you create a circular region by simply clicking on a succession of threepoints in a 2D view.

Create a point in Point mode… The default action for adding points in Point mode waschanged to LEFT CTRL + LEFT MOUSE in this software release. This should facilitate adding

points to 3D views.

Point & Click Outline brush… See Point & Click Outline Brush Preferences on page 25

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for the actions related to the new Point & Click Outline brush.

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40 Resolved Issues

Configurable Actions

Resolved Issues

A number of issues reported by users have been resolved in this software release.

Labeling Multi‐ROIs
A number of updates and fixes for labeling multi-ROIs are included in Dragonfly 2021.1.
These include a reformatted ROI Painter panel (see ROI Painter Panel on page 21), new
ROI Painter tools (see New ROI Painter Tools on page 22), and new patterns for working
in ‘Remove’ mode. You should note the following when working in ‘Remove’ mode.
• When a single label of a multi-ROI is selected in the Classes and scalar information
box and you are working in ‘Remove’ mode, only voxels within the selected class will
be removed. All other labels will remain as is.
• If no label of a multi-ROI is selected, then all labeled voxels will be removed when you
are working in ‘Remove’ mode.

Working in 3D Views
Issues related to working with visual planes and clipping in cases in which a mesh is
visible in the 3D view have been resolved in this software release.

3D Settings for Loaded Sessions
Settings for 3D visualizations, such as the applied LUT and the selected opacity, solidity,
and other 3D settings, are now retained in saved sessions and will be reinstated when asession is loaded.

Issues with AMD Graphic Cards
Issues related to rendering data in 3D on systems with AMD cards have been fixed.

Movies Rendered in 4K
Reported issues related to rendering movies in 4K with Dragonfly’s MovieMaker havebeen fixed.

Exported Screenshots
Some users have reported that the values on legends in exported screenshots are notalways well positioned. For example, numbers sometimes overlap with each other orthe bar when exporting at different resolutions. This has been fixed.

Smoothing Meshes
Issues related to smoothing meshes with the Laplacian method should no longer occur

Interpolating ROIs
The issue reported for interpolating the slice intervals of regions of interest in the ZY
plane has been resolved in Dragonfly 2021.1.

Dragonfly Release Notes Version 2021.1

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in this software release.

System Requirements
TensorFlow

System Requirements

This release supports the base system requirements (see http://www.theobjects.com/
dragonfly/system-requirements.html).

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. You should note that the version of TensorFlow
implemented in Dragonfly 2021.1 and in previous versions is 1.14.0.
Users can freely download and examine the TensorFlow source code from:https://github.com/tensorflow/tensorflow/releases/tag/v1.14.0.

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/5.9/5.9.7/.

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 of your image processing or analysis needs.

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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
to an existing workflow.

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42 Learning Dragonfly
Dragonfly Workshops

Learning Dragonfly

You’ll find some great free resources, such as our latest instructional videos, recorded
webinars, and user forums just by taking a quick look around www.theobjects.com/

dragonfly/. You can also take advantage 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.

N OTE All scheduled Dragonfly workshops have been postponed or cancelled due to worries
about the coronavirus pandemic. We will resume our workshop schedule as soon as
possible. You can check for updates at http://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 newtechniques on numerous case studies.

N OTE Online training is available upon request. Contact support@theobjects.com for

TM021-A-09

information about availability.

Dragonfly Release Notes Version 2021.1

Reporting Issues
Online Training

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.
Send Report to ORS dialog

You should note that you can also report an issue at any time by choosing Help >TM021-A-09

Report an Issue on the menu bar.

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