Improving multiple sclerosis lesion boundaries segmentation by convolutional neural networks with focal learning

Gustavo Ulloa, Alejandro Veloz, HÉCTOR GABRIEL ALLENDE CID, Héctor Allende

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

Abstract

Multiple sclerosis lesions segmentation is an important step in the diagnosis and tracking in the evolution of the disease. Convolutional Neural Networks (CNN) have been obtaining successful results in the task of lesion segmentation in recent years, but still present problem segmenting boundaries of the lesions. In this work we focus the learning process on hard voxels close to the boundaries of the lesions by means of a stratified sampling and the use of focal loss function that dynamically increase the penalization on this kind of voxels. This approach was applied on the 2015 Longitudinal MS Lesion Segmentation Challenge dataset (ISBI2015 (https://smart-stats-tools.org/lesion-challenge)), obtaining better results than approaches using binary cross entropy loss and focal loss functions with uniform sampling.

Original languageEnglish
Title of host publicationImage Analysis and Recognition - 17th International Conference, ICIAR 2020, Proceedings
EditorsAurélio Campilho, Fakhri Karray, Zhou Wang
PublisherSpringer
Pages182-192
Number of pages11
ISBN (Print)9783030505158
DOIs
StatePublished - 2020
Externally publishedYes
Event17th International Conference on Image Analysis and Recognition, ICIAR 2020 - Póvoa de Varzim, Portugal
Duration: 24 Jun 202026 Jun 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12132 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference17th International Conference on Image Analysis and Recognition, ICIAR 2020
CountryPortugal
CityPóvoa de Varzim
Period24/06/2026/06/20

Keywords

  • Convolutional Neural Networks
  • Focal loss
  • Image segmentation
  • Magnetic Resonance Imaging
  • Multiple sclerosis lesions
  • Stratified sampling

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