Multi-output decision trees for lesion segmentation in multiple sclerosis

Amod Jog, Aaron Carass, Dzung L. Pham, Jerry L. Prince

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

Abstract

Multiple Sclerosis (MS) is a disease of the central nervous system in which the protective myelin sheath of the neurons is damaged. MS leads to the formation of lesions, predominantly in the white matter of the brain and the spinal cord. The number and volume of lesions visible in magnetic resonance (MR) imaging (MRI) are important criteria for diagnosing and tracking the progression of MS. Locating and delineating lesions manually requires the tedious and expensive efforts of highly trained raters. In this paper, we propose an automated algorithm to segment lesions in MR images using multi-output decision trees. We evaluated our algorithm on the publicly available MICCAI 2008 MS Lesion Segmentation Challenge training dataset of 20 subjects, and showed improved results in comparison to state-of-the-art methods. We also evaluated our algorithm on an in-house dataset of 49 subjects with a true positive rate of 0.41 and a positive predictive value 0.36.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2015
Subtitle of host publicationImage Processing
EditorsMartin A. Styner, Sebastien Ourselin
PublisherSPIE
ISBN (Electronic)9781628415032
DOIs
StatePublished - 2015
EventMedical Imaging 2015: Image Processing - Orlando, United States
Duration: Feb 24 2015Feb 26 2015

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume9413
ISSN (Print)1605-7422

Other

OtherMedical Imaging 2015: Image Processing
CountryUnited States
CityOrlando
Period2/24/152/26/15

Keywords

  • Multiple Sclerosis
  • lesion
  • multi-output decision trees
  • segmentation

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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