Releases · spinalcordtoolbox/spinalcordtoolbox

26 Apr 12:17

github-actions

6.3

6e75cd8

6.3 Latest

Latest

Notable changes include:

Feature: Add QC report for sct_deepseg. View pull request
Feature: Add CanProCo-based MS lesion segmentation model to sct_deepseg. View pull request
Feature: Add EPI-BOLD fMRI spinal cord segmentation model to sct_deepseg. View pull request
Enhancement: Update contrast-agnostic SC segmentation model (sct_deepseg) to the latest version (v2.3). View pull request
Enhancement: Switch to using mean magnitude for output moco_params.tsv file used for QC. View pull request
Documentation: Add links to new 2024 SCT review paper to prominent locations in documentation. View pull request

Full release notes and Changelog

Results of batch_processing.sh on Ubuntu 20.04

~~~
Version:         git-HEAD-f0e3766f4d663d28fbb6b718cd0f76bd203a0971
Ran on:          Linux fv-az1533-44 5.15.0-1061-azure
Duration:        0hrs 16min 46sec
---
t2/csa_c2c3.csv:     73.87680055661444  [Row 1, MEAN(area)]
t2/csa_pmj.csv:      73.89183952519573  [Row 1, MEAN(area)]
t2/csa_pam50.csv:    34.23530312082996  [Row 39, MEAN(area)]
t2s/csa_gm.csv:      12.515230379832953 [Row 4, MEAN(area)]
t2s/csa_wm.csv:      64.93314479093739  [Row 4, MEAN(area)]
mt/mtr_in_wm.csv:    54.417007086785446 [Row 1, MAP()]
dmri/fa_in_cst.csv:  0.7758118462499376 [Row 1, WA()]
dmri/fa_in_cst.csv:  0.773312472697186  [Row 2, WA()]
~~~

Results of batch_processing.sh on macOS 12 (Monterey)

~~~
 Version:         git-HEAD-f0e3766f4d663d28fbb6b718cd0f76bd203a0971
Ran on:          Darwin Mac-1714066641279.local 21.6.0
Duration:        0hrs 21min 32sec
---
t2/csa_c2c3.csv:     73.87095978136215  [Row 1, MEAN(area)]
t2/csa_pmj.csv:      73.89183952519573  [Row 1, MEAN(area)]
t2/csa_pam50.csv:    34.23530312082996  [Row 39, MEAN(area)]
t2s/csa_gm.csv:      12.515230379832953 [Row 4, MEAN(area)]
t2s/csa_wm.csv:      64.93314479093739  [Row 4, MEAN(area)]
mt/mtr_in_wm.csv:    54.41827867818828  [Row 1, MAP()]
dmri/fa_in_cst.csv:  0.7761900744878227 [Row 1, WA()]
dmri/fa_in_cst.csv:  0.7765149211365178 [Row 2, WA()]
~~~

Results of batch_processing.sh on Windows 2022

~~~
 Version:         git-HEAD-f0e3766f4d663d28fbb6b718cd0f76bd203a0971
Ran on:          MINGW64_NT-10.0-20348 fv-az1105-632 3.4.10-87d57229.x86_64
Duration:        0hrs 22min 13sec
---
t2/csa_c2c3.csv:     73.87680055661444  [Row 1, MEAN(area)]
t2/csa_pmj.csv:      73.89183952519573  [Row 1, MEAN(area)]
t2/csa_pam50.csv:    34.23530312082996  [Row 39, MEAN(area)]
t2s/csa_gm.csv:      12.515230379832953 [Row 4, MEAN(area)]
t2s/csa_wm.csv:      64.93314479093739  [Row 4, MEAN(area)]
mt/mtr_in_wm.csv:    54.417007086785446 [Row 1, MAP()]
dmri/fa_in_cst.csv:  0.7758118462499376 [Row 1, WA()]
dmri/fa_in_cst.csv:  0.773312472697186  [Row 2, WA()]
~~~

Assets 5

15 Feb 21:24

github-actions

6.2

9ee15da

6.2

Notable changes include:

Feature: Integrate 4 new nnUNet/MONAI models into sct_deepseg (contrast-agnostic softseg, SCI lesion/SC seg, spinal nerve rootlet seg, mouse GM/WM seg). View pull request
Feature: Save QC records to browser local storage to avoid losing data on refresh. View pull request
Feature: Update PAM50 template to include new PAM50_rootlets.nii.gz file. View pull request
Bugfix: Fix straightening error during registration if 3+ labels are supplied and topmost disc label is not C1. View pull request
Bugfix: Mitigate scaling issues (1.0 -> 0.999) due to float/int datatype mismatches between header and array. View pull request
Installation: Specify Rosetta 2 as a requirement for installation on Apple silicon (M1, M2, M3). View pull request
Documentation: Port changes from SCT Course 2023 Google Slides to the web tutorials. View pull request

Full release notes and Changelog

Results of batch_processing.sh on Ubuntu 20.04

~~~
Version:         git-master-6962e03b5906ab3466e6e330438dbea58d949407
Ran on:          Linux fv-az1018-974 5.15.0-1054-azure
Duration:        0hrs 17min 15sec
---
t2/csa_c2c3.csv:     73.8768043493825   [Row 1, MEAN(area)]
t2/csa_pmj.csv:      73.89184331879929  [Row 1, MEAN(area)]
t2/csa_pam50.csv:    34.23530487782326  [Row 39, MEAN(area)]
t2s/csa_gm.csv:      12.487533885581323 [Row 4, MEAN(area)]
t2s/csa_wm.csv:      64.93464311280606  [Row 4, MEAN(area)]
mt/mtr_in_wm.csv:    54.417007018368906 [Row 1, MAP()]
dmri/fa_in_cst.csv:  0.7758118559608612 [Row 1, WA()]
dmri/fa_in_cst.csv:  0.7733124731836604 [Row 2, WA()]
~~~

Results of batch_processing.sh on macOS 11 (Big Sur)

~~~
Version:         git-master-6962e03b5906ab3466e6e330438dbea58d949407
Ran on:          Darwin Mac-1708024085684.local 20.6.0
Duration:        0hrs 29min 31sec
---
t2/csa_c2c3.csv:     73.8709635738436   [Row 1, MEAN(area)]
t2/csa_pmj.csv:      73.89184331879929  [Row 1, MEAN(area)]
t2/csa_pam50.csv:    34.23530487782326  [Row 39, MEAN(area)]
t2s/csa_gm.csv:      12.487533885581323 [Row 4, MEAN(area)]
t2s/csa_wm.csv:      64.93464311280606  [Row 4, MEAN(area)]
mt/mtr_in_wm.csv:    54.41827830455262  [Row 1, MAP()]
dmri/fa_in_cst.csv:  0.7761900951571409 [Row 1, WA()]
dmri/fa_in_cst.csv:  0.7765149187171309 [Row 2, WA()]
~~~

Results of batch_processing.sh on Windows 2019

~~~
Version:         git-master-6962e03b5906ab3466e6e330438dbea58d949407
Ran on:          MINGW64_NT-10.0-17763 fv-az1488-920 3.4.9-be826601.x86_64
Duration:        0hrs 23min 38sec
---
batch_processing.sh: line 270: D:\a\spinalcordtoolbox\spinalcordtoolbox/python/envs/venv_sct/bin/python: No such file or directory
t2/csa_c2c3.csv:     73.8768043493825   [Row 1, MEAN(area)]
t2/csa_pmj.csv:      73.89184331879929  [Row 1, MEAN(area)]
t2/csa_pam50.csv:    34.23530487782326  [Row 39, MEAN(area)]
t2s/csa_gm.csv:      12.487533885581323 [Row 4, MEAN(area)]
t2s/csa_wm.csv:      64.93464311280606  [Row 4, MEAN(area)]
mt/mtr_in_wm.csv:    54.417007018368906 [Row 1, MAP()]
dmri/fa_in_cst.csv:  0.7758118559608612 [Row 1, WA()]
dmri/fa_in_cst.csv:  0.7733124731836604 [Row 2, WA()]
~~~

Assets 5

05 Nov 18:20

github-actions

6.1

3e7dc3d

6.1

This minor release of SCT has been developed in preparation for the 2023-10-20 SCT Course. It contains a significant update to the PAM50 template, important documentation improvements, and many other bugfixes and tweaks.

Notable changes include:

Feature: Update PAM50 template link to include cord and lumbar label changes. View pull request
Feature: Add function to output the axial damage ratio for sct_analyze_lesion. View pull request
Documentation: Add tutorial for sct_compute_compression View pull request
Documentation: Add tutorial for lumbar segmentation and registration. View pull request
Documentation: Update Docker installation instructions for Linux/macOS/Windows. View pull request
Maintenance: Remove -s functionality from sct_warp_template and add a deprecation warning. View pull request
Bugfix: Fix distorted registration due to straightening bug in get_closest_to_absolute. View pull request
Bugfix Use pandas for .csv saving in sct_compute_compression to correctly merge existing output metric columns. View pull request

Full release notes and Changelog

Results of batch_processing.sh on Ubuntu 20.04

~~~
Version:         git-master-f5a46f328fe797b3d7c0e3844e17ad3f8add5ee1
Ran on:          Linux fv-az619-734 5.15.0-1050-azure
Duration:        0hrs 26min 56sec
---
t2/csa_c2c3.csv:     73.87711295363036  [Row 1, MEAN(area)]
t2/csa_pmj.csv:      73.89298021190447  [Row 1, MEAN(area)]
t2/csa_pam50.csv:    34.25734847835911  [Row 39, MEAN(area)]
t2s/csa_gm.csv:      12.48783482885619  [Row 4, MEAN(area)]
t2s/csa_wm.csv:      64.93830702088245  [Row 4, MEAN(area)]
mt/mtr_in_wm.csv:    54.39463563966487  [Row 1, MAP()]
dmri/fa_in_cst.csv:  0.7793857114020448 [Row 1, WA()]
dmri/fa_in_cst.csv:  0.775688329824498  [Row 2, WA()]
~~~

Results of batch_processing.sh on macOS 11 (Big Sur)

~~~
Version:         git-master-f5a46f328fe797b3d7c0e3844e17ad3f8add5ee1
Ran on:          Darwin Mac-1699196155540.local 20.6.0
Duration:        0hrs 28min 56sec
---
t2/csa_c2c3.csv:     73.87711295363036  [Row 1, MEAN(area)]
t2/csa_pmj.csv:      73.89298021190447  [Row 1, MEAN(area)]
t2/csa_pam50.csv:    34.25734847835911  [Row 39, MEAN(area)]
t2s/csa_gm.csv:      12.487834828856178 [Row 4, MEAN(area)]
t2s/csa_wm.csv:      64.93830702088249  [Row 4, MEAN(area)]
mt/mtr_in_wm.csv:    54.41599937353151  [Row 1, MAP()]
dmri/fa_in_cst.csv:  0.7807687336793107 [Row 1, WA()]
dmri/fa_in_cst.csv:  0.774647306583997  [Row 2, WA()]
~~~

Results of batch_processing.sh on Windows 2019

~~~
Version:         git-master-f5a46f328fe797b3d7c0e3844e17ad3f8add5ee1
Ran on:          MINGW64_NT-10.0-17763 fv-az981-219 3.4.7-25de8b84.x86_64
Duration:        0hrs 22min 58sec
---
t2/csa_c2c3.csv:     73.87711295363036  [Row 1, MEAN(area)]
t2/csa_pmj.csv:      73.89298021190447  [Row 1, MEAN(area)]
t2/csa_pam50.csv:    34.25734847835911  [Row 39, MEAN(area)]
t2s/csa_gm.csv:      12.487834828856178 [Row 4, MEAN(area)]
t2s/csa_wm.csv:      64.93830702088248  [Row 4, MEAN(area)]
mt/mtr_in_wm.csv:    54.39462068731342  [Row 1, MAP()]
dmri/fa_in_cst.csv:  0.7793857115174943 [Row 1, WA()]
dmri/fa_in_cst.csv:  0.7756883298906552 [Row 2, WA()]
~~~

Assets 5

14 Jul 19:56

github-actions

6.0

99b1c07

SCT v6.0

This major release provides significant improvements for how SCT is installed on all platforms, as well as many new features and bugfixes.

Notable changes include:

Installation: Allow install_sct to be run standalone (without downloading "Source code" archive). View pull request
Installation: Use Miniconda instead of built-in Python for Windows installations. View pull request
Feature: Add new CLI script to compute normalized metric ratios (MSCC, etc.) for compressed levels. View pull request
Feature: Add new -histo option to sct_warp_template to warp the newly-added PAM50 histology files. View pull request
Feature: Add new sagittal mosaic option for sct_deepseg_lesion QC report. View pull request
Feature: Add support for model ensembles to sct_deepseg and use it for mp2rage_lesion model. View pull request
Feature: Add new -project-centerline option to sct_label_utils to project an image on the spinal cord centerline. View pull request
Feature: Add new -centerline-soft option to sct_get_centerline to output a non-binary "soft" centerline. View pull request
Bugfix: Ensure that qform/sform codes are preserved when generating sct_deepseg_sc segmentation. View pull request

Full release notes and Changelog

Results of batch_processing.sh on Ubuntu 20.04

~~~
Version:         git-master-908998829a2a3694fa96363b358c9b662da4ae43
Ran on:          Linux fv-az205-332 5.15.0-1041-azure
Duration:        0hrs 23min 3sec
---
t2/csa_c2c3.csv:     73.87711295363036  [Row 1, MEAN(area)]
t2/csa_pmj.csv:      73.89298021190447  [Row 1, MEAN(area)]
t2/csa_pam50.csv:    34.25734847835911  [Row 39, MEAN(area)]
t2s/csa_gm.csv:      12.487834828856178 [Row 4, MEAN(area)]
t2s/csa_wm.csv:      64.93830702088249  [Row 4, MEAN(area)]
mt/mtr_in_wm.csv:    54.38856944000351  [Row 1, MAP()]
dmri/fa_in_cst.csv:  0.7793405440192693 [Row 1, WA()]
dmri/fa_in_cst.csv:  0.7755611783165834 [Row 2, WA()]
~~~

Results of batch_processing.sh on macOS 11 (Big Sur)

 ~~~
Version:         git-master-908998829a2a3694fa96363b358c9b662da4ae43
Ran on:          Darwin Mac-1689358819888.local 20.6.0
Duration:        0hrs 39min 15sec
---
t2/csa_c2c3.csv:     73.87711295363036  [Row 1, MEAN(area)]
t2/csa_pmj.csv:      73.89298021190447  [Row 1, MEAN(area)]
t2/csa_pam50.csv:    34.25734847835911  [Row 39, MEAN(area)]
t2s/csa_gm.csv:      12.487834828856178 [Row 4, MEAN(area)]
t2s/csa_wm.csv:      64.93830702088249  [Row 4, MEAN(area)]
mt/mtr_in_wm.csv:    54.43230073944698  [Row 1, MAP()]
dmri/fa_in_cst.csv:  0.7805537210812612 [Row 1, WA()]
dmri/fa_in_cst.csv:  0.7746193990233381 [Row 2, WA()]
~~~

Results of batch_processing.sh on Windows 2019

~~~
Version:         git-master-908998829a2a3694fa96363b358c9b662da4ae43
Ran on:          MINGW64_NT-10.0-17763 fv-az34-210 3.4.7-ea781829.x86_64
Duration:        0hrs 26min 44sec
---
t2/csa_c2c3.csv:     73.87711295363036  [Row 1, MEAN(area)]
t2/csa_pmj.csv:      73.89298021190447  [Row 1, MEAN(area)]
t2/csa_pam50.csv:    34.25734847835911  [Row 39, MEAN(area)]
t2s/csa_gm.csv:      12.487834828856178 [Row 4, MEAN(area)]
t2s/csa_wm.csv:      64.93830702088248  [Row 4, MEAN(area)]
mt/mtr_in_wm.csv:    54.38856944000351  [Row 1, MAP()]
dmri/fa_in_cst.csv:  0.7793405440192693 [Row 1, WA()]
dmri/fa_in_cst.csv:  0.7755611783165834 [Row 2, WA()]
~~~

Assets 5

19 Jul 15:30

github-actions

5.8

b8be5c4

SCT v5.8

Note: Version 5.8 of SCT was originally released on Nov 30, 2022. However, the release was then updated on Feb 17, 2023 to backport a fix for an installation related bug (24f602b), and on Jul 19, 2023 to backport two fixes for bugs causing some SCT commands to crash (b8be5c4).

Notable changes include:

Feature: sct_image: Add new -stitch option for combining sequential image scans. View pull request
Feature: sct_run_batch: Add new -ignore-ses option to prioritize sub- directories over ses- subdirectories. View pull request
Feature: sct_process_segmentation: For the -perslice option, begin outputting the DistancePMJ metric. View pull request
Enhancement: Add readability fixes for QC reports (sagittal view scaling, label text, label colormaps). View pull request
Enhancement: image.py: Update header dtype property on save/load to match the datatype of the data array. View pull request
Bugfix: sct_run_batch: Modify -include-list and -exclude-list to check against parts of a directory, too. View pull request
Bugfix: sct_run_batch: Allow path_output parameter to start with ~. View pull request
Installation: Upgrade SCT from Python 3.7 to Python 3.8. View pull request
Documentation: Emphasize references to PMJ method by Bédard and Cohen-Adad. View pull request

Full release notes and Changelog

Results of batch_processing.sh on Ubuntu 20.04

 ~~~
Version:         git-master-71e199dc14156e895880bc8e74de8409a2d238c8
Ran on:          Linux fv-az259-426 5.15.0-1023-azure
Duration:        0hrs 23min 13sec
---
t2/csa_c2c3.csv:     73.87711295363036  [Row 1, MEAN(area)]
t2/csa_pmj.csv:      73.89298021190447  [Row 1, MEAN(area)]
t2s/csa_gm.csv:      12.487834828856178 [Row 4, MEAN(area)]
t2s/csa_wm.csv:      64.93830702088249  [Row 4, MEAN(area)]
mt/mtr_in_wm.csv:    54.37973940532884  [Row 1, MAP()]
dmri/fa_in_cst.csv:  0.7793405440192693 [Row 1, WA()]
dmri/fa_in_cst.csv:  0.7755611783165834 [Row 2, WA()]
~~~

Results of batch_processing.sh on macOS 11 (Big Sur)

 ~~~
Version:         git-master-71e199dc14156e895880bc8e74de8409a2d238c8
Ran on:          Darwin Mac-1669763228410.local 20.6.0
Duration:        0hrs 28min 36sec
---
t2/csa_c2c3.csv:     73.87711295363036  [Row 1, MEAN(area)]
t2/csa_pmj.csv:      73.89298021190447  [Row 1, MEAN(area)]
t2s/csa_gm.csv:      12.487834828856178 [Row 4, MEAN(area)]
t2s/csa_wm.csv:      64.93830702088249  [Row 4, MEAN(area)]
mt/mtr_in_wm.csv:    54.424258247179175 [Row 1, MAP()]
dmri/fa_in_cst.csv:  0.7805537210812612 [Row 1, WA()]
dmri/fa_in_cst.csv:  0.7746193990233381 [Row 2, WA()]
~~~

Results of batch_processing.sh on Windows 2019

 ~~~
Version:         git-master-71e199dc14156e895880bc8e74de8409a2d238c8
Ran on:          MINGW64_NT-10.0-17763 fv-az30-853 3.3.6-341.x86_64
Duration:        0hrs 24min 52sec
---
batch_processing.sh: line 267: ./python/envs/venv_sct/bin/python: No such file or directory
t2/csa_c2c3.csv:     73.87711295363036  [Row 1, MEAN(area)]
t2/csa_pmj.csv:      73.89298021190447  [Row 1, MEAN(area)]
t2s/csa_gm.csv:      12.487834828856178 [Row 4, MEAN(area)]
t2s/csa_wm.csv:      64.93830702088248  [Row 4, MEAN(area)]
mt/mtr_in_wm.csv:    54.37973940532884  [Row 1, MAP()]
dmri/fa_in_cst.csv:  0.7793405440192693 [Row 1, WA()]
dmri/fa_in_cst.csv:  0.7755611783165834 [Row 2, WA()]
~~~

Assets 3

28 Jul 20:29

github-actions

5.7

d2e38af

SCT v5.7

🔔 Reminder for Windows users 🔔

Since version 5.6, WSL and Docker are no longer required to install SCT on Windows! 🎉
Please visit the updated Windows installation page of SCT's documentation to try out this new installation method.

Notable changes include:

Feature: Multimodal registration using Deep Learning methods are now integrated in SCT (sct_register_to_template, sct_register_multimodal). View pull request
Tutorial: New tutorial for contrast agnostic registration. View pull request
Bugfix: sct_process_segmentation, sct_extract_metric: Combine conditions when slice number and vertebral levels are both specified. View pull request
Bugfix: sct_propseg: Prevent from overwriting files. View pull request
Bugfix: Correctly handle output files on different drives on Windows. View pull request
Bugfix: sct_maths: Don't treat a single 4D image as a sequence of 3D images in -add/-sub/-mul/-div. View pull request
Maintenance: sct_deepseg_sc, sct_deepseg_gm, sct_deepseg_lesion: Replace Tensorflow/Keras-based inference (.h5) with onnxruntime (.ONNX). View pull request
Testing: The batch_processing.sh tests now support macOS and Windows. View pull request

Full release notes and Changelog

Results of batch_processing.sh on Ubuntu 20.04

~~~
Version:         git-master-ff99c2cc6364c2241da3203ead46026e24908657
Ran on:          Linux fv-az453-981 5.15.0-1014-azure
Duration:        0hrs 22min 54sec
---
t2/csa_c2c3.csv:     73.87711295363036  [Row 1, MEAN(area)]
t2/csa_pmj.csv:      73.89298021190447  [Row 1, MEAN(area)]
t2s/csa_gm.csv:      12.487834828856178 [Row 4, MEAN(area)]
t2s/csa_wm.csv:      64.93830702088249  [Row 4, MEAN(area)]
mt/mtr_in_wm.csv:    54.37973940532884  [Row 1, MAP()]
dmri/fa_in_cst.csv:  0.7793999418865862 [Row 1, WA()]
dmri/fa_in_cst.csv:  0.7755359512006275 [Row 2, WA()]
~~~

Results of batch_processing.sh on macOS 11 (Big Sur)

~~~
Version:         git-master-ff99c2cc6364c2241da3203ead46026e24908657
Ran on:          Darwin Mac-1659036569665.local 20.6.0
Duration:        0hrs 27min 49sec
---
t2/csa_c2c3.csv:     73.87711295363036  [Row 1, MEAN(area)]
t2/csa_pmj.csv:      73.89298021190447  [Row 1, MEAN(area)]
t2s/csa_gm.csv:      12.487834828856178 [Row 4, MEAN(area)]
t2s/csa_wm.csv:      64.93830702088249  [Row 4, MEAN(area)]
mt/mtr_in_wm.csv:    54.424258247179175 [Row 1, MAP()]
dmri/fa_in_cst.csv:  0.7806055878397453 [Row 1, WA()]
dmri/fa_in_cst.csv:  0.7746118274273426 [Row 2, WA()]
~~~

Results of batch_processing.sh on Windows 2019

~~~
Version:         git-master-ff99c2cc6364c2241da3203ead46026e24908657
Ran on:          MINGW64_NT-10.0-17763 fv-az158-506 3.3.5-341.x86_64
Duration:        0hrs 26min 1sec
---
batch_processing.sh: line 267: ./python/envs/venv_sct/bin/python: No such file or directory
t2/csa_c2c3.csv:     73.87711295363036  [Row 1, MEAN(area)]
t2/csa_pmj.csv:      73.89298021190447  [Row 1, MEAN(area)]
t2s/csa_gm.csv:      12.487834828856178 [Row 4, MEAN(area)]
t2s/csa_wm.csv:      64.93830702088248  [Row 4, MEAN(area)]
mt/mtr_in_wm.csv:    54.37973940532884  [Row 1, MAP()]
dmri/fa_in_cst.csv:  0.7793999418865862 [Row 1, WA()]
dmri/fa_in_cst.csv:  0.7755359512006275 [Row 2, WA()]
~~~

Assets 3

29 Apr 17:33

github-actions

5.6

45b1c17

SCT v5.6

🔔 Notice for Windows users 🔔

Starting from version 5.6, WSL and Docker are no longer required to install SCT on Windows! 🎉
Please visit the updated Windows installation page of SCT's documentation to try out this new installation method.

Other notable changes include:

sct_deepseg: Add model for T2w lumbar SC segmentation. View pull request
sct_deepseg: Add new option -list-tasks-long to print in-depth descriptions of deepseg tasks. View pull request
sct_label_vertebrae: Update the default label cleaning behavior. View pull request
Add support for ITK-Snap + multiple viewers to display_viewer_syntax. View pull request
BUGFIX: sct_analyze_lesion: Fix computation of estimated lesion length and diameter. View pull request
BUGFIX: Set a more permissive threshold for reading the qform. View pull request

Full release notes and Changelog

Results of batch_processing.sh

~~~
Version:         git-master-c642c1e3285efbd5fdb88e19a5c30da341444927
Ran on:          Linux fv-az81-867 5.4.0-1077-azure
Duration:        0hrs 18min 5sec
---
t2/csa_c2c3.csv:     73.87711295363036  [Row 1, MEAN(area)]
t2/csa_pmj.csv:      73.89298021190447  [Row 1, MEAN(area)]
t2s/csa_gm.csv:      12.487834828856178 [Row 4, MEAN(area)]
t2s/csa_wm.csv:      64.93830702088249  [Row 4, MEAN(area)]
mt/mtr_in_wm.csv:    54.37973940532884  [Row 1, MAP()]
dmri/fa_in_cst.csv:  0.7793999418865862 [Row 1, WA()]
dmri/fa_in_cst.csv:  0.7755359512006275 [Row 2, WA()]
~~~

Assets 3

27 Jan 20:26

github-actions

5.5

b918050

SCT v5.5

Notable changes include:

Added new sct_deepseg models for MP2RAGE spinal cord and MS lesion segmentation. View pull request
Added new sct_deepseg model for 7T spinal cord/gray matter multiclass segmentation. View pull request
Added new Patch2Self CLI script for dMRI denoising. View pull request
Brought back previously-removed sct_testing command as a light wrapper for pytest. View pull request
Fixed bug in sct_compute_mtr when run on high-valued int16 data.

Full release notes and Changelog

Results of batch_processing.sh

~~~
Version:         git-HEAD-daf4ce949b79e686a3ded53601e5b5f12c4ef9fd
Ran on:          Linux fv-az41-165 5.4.0-1067-azure
Duration:        0hrs 20min 23sec
---
t2/csa_c2c3.csv:     73.87711295363036  [Row 1, MEAN(area)]
t2/csa_pmj.csv:      73.89298021190447  [Row 1, MEAN(area)]
t2s/csa_gm.csv:      12.487834828856178 [Row 4, MEAN(area)]
t2s/csa_wm.csv:      64.93830702088249  [Row 4, MEAN(area)]
mt/mtr_in_wm.csv:    54.37973940532884  [Row 1, MAP()]
dmri/fa_in_cst.csv:  0.7793999143910345 [Row 1, WA()]
dmri/fa_in_cst.csv:  0.7755359385435815 [Row 2, WA()]
~~~

Assets 2

27 Sep 02:31

github-actions

5.4

55fbd43

SCT v5.4

Notable changes include:

Feature: Measure CSA based on distance from pontomedullary junction (PMJ) using sct_process_segmentation -pmj.
Feature: Normalize CSA based on sex, brain volume, and thalamus volume using sct_process_segmentaiton -normalize.
Feature: Visualize b-vectors for multi-shell acquisitions using sct_dmri_display_bvecs -bval.
Feature: Compute SNR within a single 3D volume using sct_compute_snr -method single.
Feature: It is now possible to loop across "ses-" entities in sct_run_batch.
Enhancement: In sct_detect_pmj, the method used to determine the R-L placement of the PMJ label has been improved.
Bug: The SCT FSLeyes script is now compatible with FSLeyes v1.X.

Full release notes and Changelog

Results of batch_processing.sh:

~~~
Version:         git-master-5e54502852b4212dba2bedf3aa5de740b5660516
Ran on:          Linux fv-az76-747 5.4.0-1056-azure
Duration:        0hrs 25min 53sec
---
t2/csa_c2c3.csv:     73.87711295363036  [Row 1, MEAN(area)]
t2/csa_pmj.csv:      73.89298021190447  [Row 1, MEAN(area)]
t2s/csa_gm.csv:      12.487834828856176 [Row 4, MEAN(area)]
t2s/csa_wm.csv:      64.93830702088249  [Row 4, MEAN(area)]
mt/mtr_in_wm.csv:    54.379739375142336 [Row 1, MAP()]
dmri/fa_in_cst.csv:  0.7793999143910345 [Row 1, WA()]
dmri/fa_in_cst.csv:  0.7755359385435815 [Row 2, WA()]
~~~

Assets 2

25 Apr 21:03

joshuacwnewton

5.3.0

e2e19bd

SCT v5.3.0

Notable changes for v5.3.0 include:

Added flag -s-template-id to use another segmentation (e.g. white matter) for sct_register_to_template
Interactive QC assessment: Add Pass/Fail/Artifact and download YAML file
QC reports for sct_dmri_moco and sct_fmri_moco
Implemented weighted median for sct_extract_metric
Several bugfixes for issues encountered by users on the SCT forum

Release notes and Changelog

Results of batch_processing.sh:

Version:         git-release-e2e19bd02e2a99eab252ad3cca86eee5c5d63e92
Ran on:          Linux fv-az139-388 5.4.0-1046-azure
Duration:        0hrs 23min 37sec
---
t2/CSA:          73.87711295363036
mt/MTR(WM):      54.379739375142336
t2s/CSA_GM:      12.48783482885618
t2s/CSA_WM:      64.93830702088246
dmri/FA(CST_r):  0.7755359385435815
dmri/FA(CST_l):  0.7793999143910345

Assets 2

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Releases: spinalcordtoolbox/spinalcordtoolbox

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