Simultaneous Whole-Brain Segmentation and White Matter Lesion Detection Using Contrast-Adaptive Probabilistic Models

Oula Puonti, Koen Van Leemput

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

In this paper we propose a new generative model for simultaneous brain parcellation and white matter lesion segmentation from multi-contrast magnetic resonance images. The method combines an existing whole-brain segmentation technique with a novel spatial lesion model based on a convolutional restricted Boltzmann machine. Unlike current state-of-the-art lesion detection techniques based on discriminative modeling, the proposed method is not tuned to one specific scanner or imaging protocol, and simultaneously segments dozens of neuroanatomical structures. Experiments on a public benchmark dataset in multiple sclerosis indicate that the method’s lesion segmentation accuracy compares well to that of the current state-of-the-art in the field, while additionally providing robust whole-brain segmentations.
Original languageEnglish
Title of host publication1st International Workshop on Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries (Brainles 2015) : Revised Selected Papers
PublisherSpringer
Publication date2016
Pages9-20
ISBN (Print)978-3-319-30857-9
ISBN (Electronic)978-3-319-30858-6
DOIs
Publication statusPublished - 2016
Event1st International Workshop on Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries (Brainles 2015) - Munich, Germany
Duration: 5 Oct 20155 Oct 2015
Conference number: 1

Workshop

Workshop1st International Workshop on Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries (Brainles 2015)
Number1
CountryGermany
CityMunich
Period05/10/201505/10/2015
OtherHeld in Conjunction with MICCAI 2015
SeriesLecture Notes in Computer Science
Volume9556
ISSN0302-9743

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