Longitudinal Multiple Sclerosis lesion segmentation using multi-view convolutional neural networks

Ariel Birenbaum, Hayit Greenspan

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Automatic segmentation of Multiple Sclerosis (MS) lesions is a challenging task due to their variability in shape, size, location and texture in Magnetic Resonance (MR) images. A reliable, automatic segmentation method can help diagnosis and patient follow-up while reducing the time consuming need of manual segmentation. In this paper, we present a fully automated method for MS lesion segmentation. The proposed method uses MR intensities and White Matter (WM) priors for extraction of candidate lesion voxels and uses Convolutional Neural Networks for false positive reduction. Our networks process longitudinal data, a novel contribution in the domain of MS lesion analysis. The method was tested on the ISBI 2015 dataset and obtained state-of-theart Dice results with the performance level of a trained human rater.

Original languageEnglish
Title of host publicationDeep Learning and Data Labeling for Medical Applications - 1st International Workshop, LABELS 2016, and 2nd International Workshop, DLMIA 2016 Held in Conjunction with MICCAI 2016, Proceedings
EditorsZhi Lu, Vasileios Belagiannis, Joao Manuel R.S. Tavares, Jaime S. Cardoso, Andrew Bradley, Joao Paulo Papa, Jacinto C. Nascimento, Marco Loog, Julien Cornebise, Gustavo Carneiro, Diana Mateus, Loic Peter
Pages58-67
Number of pages10
DOIs
StatePublished - 2016
Event1st International Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, LABELS 2016 and 2nd International Workshop on Deep Learning in Medical Image Analysis, DLMIA 2016 held in conjunction with 19th International Conference ... - Athens, Greece
Duration: 21 Oct 201621 Oct 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10008 LNCS

Conference

Conference1st International Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, LABELS 2016 and 2nd International Workshop on Deep Learning in Medical Image Analysis, DLMIA 2016 held in conjunction with 19th International Conference ...
Country/TerritoryGreece
CityAthens
Period21/10/1621/10/16

Keywords

  • CNN
  • Longitudinal data
  • Multiple sclerosis
  • Segmentation

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science

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