Example based lesion segmentation

Snehashis Roy, Qing He, Aaron Carass, Amod Jog, Jennifer L. Cuzzocreo, Daniel S. Reich, Jerry Prince, Dzung Pham

Research output: Chapter in Book/Report/Conference proceedingConference contribution

19 Scopus citations


Automatic and accurate detection of white matter lesions is a significant step toward understanding the progression of many diseases, like Alzheimers disease or multiple sclerosis. Multi-modal MR images are often used to segment T2 white matter lesions that can represent regions of demyelination or ischemia. Some automated lesion segmentation methods describe the lesion intensities using generative models, and then classify the lesions with some combination of heuristics and cost minimization. In contrast, we propose a patch-based method, in which lesions are found using examples from an atlas containing multi-modal MR images and corresponding manual delineations of lesions. Patches from subject MR images are matched to patches from the atlas and lesion memberships are found based on patch similarity weights. We experiment on 43 subjects with MS, whose scans show various levels of lesion-load. We demonstrate significant improvement in Dice coefficient and total lesion volume compared to a state of the art model-based lesion segmentation method, indicating more accurate delineation of lesions.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2014
Subtitle of host publicationImage Processing
ISBN (Print)9780819498274
StatePublished - 2014
EventMedical Imaging 2014: Image Processing - San Diego, CA, United States
Duration: Feb 16 2014Feb 18 2014

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
ISSN (Print)1605-7422


OtherMedical Imaging 2014: Image Processing
Country/TerritoryUnited States
CitySan Diego, CA


  • Lesion segmentation
  • MRI
  • MS
  • Magnetic resonance imaging
  • Patches

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Biomaterials
  • Atomic and Molecular Physics, and Optics
  • Radiology Nuclear Medicine and imaging


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