Convolutional neural networks applied to multiple sclerosis lesion segmentation on 3D brain magnetic resonance images

Rodrigo Naranjo, Gustavo Ulloa, Hector Allende-Cid, Hector Allende

Research output: Contribution to conferencePaperpeer-review

Abstract

Multiple Sclerosis (MS) is a disabling disease which affects the central nervous system. The segmentation of the multiple sclerosis lesions in 3D brain Magnetic Resonance (MR) images is a fundamental task in diagnosis and tracking of this disease. The process of segmentation of the lesions is usually performed manually by experts, however, there exists interest in the automation of this task in order to speed up and standardize this process. To this end, multiple automated segmentation techniques have been proposed to effectively detect MS lesions. In this work, the performance of Convolutional Neural Networks (CNN) applied to the problem of MS lesion detection in 3D brain MR images will be compared to other state of art proposals.

Original languageEnglish
Pages7-11
Number of pages5
StatePublished - 2018
Externally publishedYes
Event9th International Conference on Pattern Recognition Systems, ICPRS 2018 - Valparaiso, Chile
Duration: 22 May 201824 May 2018

Conference

Conference9th International Conference on Pattern Recognition Systems, ICPRS 2018
Country/TerritoryChile
CityValparaiso
Period22/05/1824/05/18

Keywords

  • Convolutional Neural Networks
  • Image Segmentation
  • Magnetic Resonance Imaging
  • Multiple Sclerosis

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