mlsanityData to 14 Oct 2026

Unique MS Lesion Identification from MRI

arXiv 2410.09639 ↗First posted 12 Oct 2024eess.IV cs.CVcited by 3

Not on the map. Why?

Abstract (arXiv)

Unique identification of multiple sclerosis (MS) white matter lesions (WMLs) is important to help characterize MS progression. WMLs are routinely identified from magnetic resonance images (MRIs) but the resultant total lesion load does not correlate well with EDSS; whereas mean unique lesion volume has been shown to correlate with EDSS. Our approach builds on prior work by incorporating Hessian matrix computation from lesion probability maps before using the random walker algorithm to estimate the volume of each unique lesion. Synthetic images demonstrate our ability to accurately count the number of lesions present. The takeaways, are: 1) that our method correctly identifies all lesions including many that are missed by previous methods; 2) we can better separate confluent lesions; and 3) we can accurately capture the total volume of WMLs in a given probability map. This work will allow new more meaningful statistics to be computed from WMLs in brain MRIs

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 3 of its 21 references that are in the map. First posted on arXiv 12 Oct 2024.

Builds on

3 of 21 references.

References · 3

  1. 2022ModDrop++: A Dynamic Filter Network with Intra-subject Co-training for Multiple Sclerosis Lesion Segmentation with Missing Modalitiescited by 22
  2. 2023Harmonization-enriched domain adaptation with light fine-tuning for multiple sclerosis lesion segmentationcited by 6
  3. 2023Towards an accurate and generalizable multiple sclerosis lesion segmentation model using self-ensembled lesion fusioncited by 6

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Cited by year

Citing papers per year: 2025: 3.

3 papers.

  1. 2025Encoder-Only Image Registrationcited by 3
  2. 2025UNISELF: A Unified Network with Instance Normalization and Self-Ensembled Lesion Fusion for Multiple Sclerosis Lesion Segmentationcited by 2
  3. 2025MSRepaint: Multiple Sclerosis Repaint with Conditional Denoising Diffusion Implicit Model for Bidirectional Lesion Filling and Synthesiscited by 1