MIMoSA: An Approach to automatically segment T2 hyperintense and T1 hypointense lesions in multiple sclerosis

Alessandra M. Valcarcel, Kristin A. Linn, Fariha Khalid, Simon N. Vandekar, Shahamat Tauhid, Theodore D. Satterthwaite, John Muschelli, Rohit Bakshi, Russell T. Shinohara

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

Abstract

Magnetic resonance imaging (MRI) is crucial for in vivo detection and characterization of white matter lesions (WML) in multiple sclerosis (MS). The most widely established MRI outcome measure is the volume of hyperintense lesions on T2-weighted images (T2L). Unfortunately, T2L are non-specific for the level of tissue destruction and show a weak relationship to clinical status. Interest in lesions appearing hypointense on T1-weighted images (T1L) (“black holes”), which provide more specificity for axonal loss and a closer link to neurologic disability, has thus grown. The technical difficulty of T1L segmentation has led investigators to rely on time-consuming manual assessments prone to inter- and intra-rater variability. We implement MIMoSA, a current T2L automatic segmentation approach, to delineate T1L. Using cross-validation, MIMoSA proved robust for segmenting both T2L and T1L. For T2L, a Sørensen-Dice coefficient (DSC) of 0.6 and partial AUC (pAUC) up to 1% false positive rate of 0.69 were achieved. For T1L, 0.48 DSC and 0.63 pAUC were achieved. The correlation between EDSS and manual versus automatic volumes were similar for T1L (0.32 manual vs. 0.34 MIMoSA) and T2L (0.34 vs. 0.34).

Original languageEnglish (US)
Title of host publicationBrainlesion
Subtitle of host publicationGlioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries - 4th International Workshop, BrainLes 2018, Held in Conjunction with MICCAI 2018, Revised Selected Papers
EditorsMauricio Reyes, Alessandro Crimi, Theo van Walsum, Farahani Keyvan, Spyridon Bakas, Hugo Kuijf
PublisherSpringer Verlag
Pages47-56
Number of pages10
ISBN (Print)9783030117221
DOIs
StatePublished - Jan 1 2019
Event4th International MICCAI Brainlesion Workshop, BrainLes 2018 held in conjunction with the Medical Image Computing for Computer Assisted Intervention Conference, MICCAI 2018 - Granada, Spain
Duration: Sep 16 2018Sep 20 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11383 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference4th International MICCAI Brainlesion Workshop, BrainLes 2018 held in conjunction with the Medical Image Computing for Computer Assisted Intervention Conference, MICCAI 2018
CountrySpain
CityGranada
Period9/16/189/20/18

Keywords

  • Inter-modal coupling
  • Logistic regression
  • Multiple sclerosis

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Valcarcel, A. M., Linn, K. A., Khalid, F., Vandekar, S. N., Tauhid, S., Satterthwaite, T. D., Muschelli, J., Bakshi, R., & Shinohara, R. T. (2019). MIMoSA: An Approach to automatically segment T2 hyperintense and T1 hypointense lesions in multiple sclerosis. In M. Reyes, A. Crimi, T. van Walsum, F. Keyvan, S. Bakas, & H. Kuijf (Eds.), Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries - 4th International Workshop, BrainLes 2018, Held in Conjunction with MICCAI 2018, Revised Selected Papers (pp. 47-56). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11383 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-11723-8_5