contrast-agnostic-spinal-cord-v3.0 (r20250402)

Overview of the dataset characteristics

**List of datasets used **

• basel-mp2rage
• canproco
• data-multi-subject
• dcm-brno
• dcm-zurich-lesions-20231115
• dcm-zurich-lesions
• dcm-zurich
• lumbar-epfl
• lumbar-vanderbilt
• nih-ms-mp2rage
• sci-colorado
• sci-paris
• sci-zurich
• sct-testing-large (T2star and MTon contrasts)
• site_006 (praxis - montreal site)
• site_007 (praxis - vancouver site)

** Dataset stats I (SUBJECT-WISE PATHOLOGY SPLIT) **

Pathology	Number of Subjects
ALS	13
AcuteSCI	95
DCM	359
HC	428
MS	164
NMO	10
PPMS	60
RIS	61
RRMS	249
SCI	191
SYR	1
TOTAL	1631

** Dataset stats II (CONTRAST-WISE PATHOLOGY SPLIT) **

	MS	HC	RRMS	RIS	PPMS	DCM	SCI	ALS	NMO	SYR	AcuteSCI	#total_per_contrast
dwi	0	184	0	0	0	59	0	0	0	0	0	243
mt-off	0	184	0	0	0	59	0	0	0	0	0	243
mt-on	0	184	0	0	0	64	0	0	0	0	0	248
psir	0	42	193	54	44	0	0	0	0	0	0	333
stir	0	10	56	7	16	0	0	0	0	0	0	89
t1w	0	249	0	0	0	59	0	0	10	0	0	318
t2star	121	237	0	0	0	127	0	13	0	1	0	499
t2w	0	252	229	61	57	426	257	0	0	0	95	1377
unit1	50	53	0	0	0	0	0	0	0	0	0	103
TOTAL	171	1395	478	122	117	794	257	13	10	1	95	3453

NOTE: All the info presented above can also be found in the release assets

What’s Changed

• Change in Training Framework: Replacing monai-based model with a 3D nnUNet model trained from scratch. Tested on a wide variety of pathologies and contrasts. Works especially well on compressed spinal cords, fixes issues with shifted predictions (consistent in monai-based models)
• Introducing Lifelong Learning Framework: Adds a GitHub Actions-based workflow to automatically generate plots measuring CSA variability once a release has been published. This addition makes it easy to monitor morphometric drift between different model releases. Currently, the plots are generated only for v2.0 (original model) and v3.0 (the current release)
• Other:
  • Update model with new datasets, pathologies and contrasts by @naga-karthik in #125
  • Lifelong learning strategy for adding new contrasts and pathologies by @naga-karthik in #128
  • Add instructions to use contrast agnostic model with nnUnet by @naga-karthik in #129
  • Update dataset info in README by @sandrinebedard in #135
  • Release new nnunet model replacing the current monai-based model by @naga-karthik in #136
  • Add standalone script to compute morphometrics after training by @naga-karthik in #139
  • Clean repo and provide instructions for future reproducibility and retraining by @naga-karthik in #138
  • update readme by @naga-karthik in #144

Full Changelog: v2.5...v3.0