Skip to content

r20240417
Compare
Choose a tag to compare
@naga-karthik naga-karthik released this 16 Apr 22:14
v2.3
d851223

Improved version of contrast-agnostic spinal cord segmentation model trained on healthy subjects and pathologies in the cervical cord:

  • Contrasts: T1w, T2w, T2star, MTon-MTS, MToff-MTS, DWI (averaged), mp2rage UNIT1
  • Pathologies: multiple sclerosis (MS) patients, compressed spinal cords in degenerative cervical myelopathy (DCM) patients.

Works well on:

  • Spinal cord injury (SCI) lesions
  • GRE-EPI images
  • B0 Field Map images
  • Lumbar cord

Main difference from earlier versions of contrast-agnostic is the addition of pathological data (MS, DCM) to the training set.

The train/val/test splits from all the datasets used to train this model can be found in the datasplits folder in the source code. Further details on the number of training samples across all datasets and samples per contrast can be found in dataset_stats.md.

Full Changelog: v2.2...v2.3

Assets 3
Loading