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

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

Resultado de la investigación: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

2 Citas (Scopus)

Resumen

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.

Idioma originalInglés
Título de la publicación alojadaImage Analysis and Recognition - 17th International Conference, ICIAR 2020, Proceedings
EditoresAurélio Campilho, Fakhri Karray, Zhou Wang
EditorialSpringer
Páginas182-192
Número de páginas11
ISBN (versión impresa)9783030505158
DOI
EstadoPublicada - 2020
Publicado de forma externa
Evento17th International Conference on Image Analysis and Recognition, ICIAR 2020 - Póvoa de Varzim, Portugal
Duración: 24 jun 202026 jun 2020

Serie de la publicación

NombreLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volumen12132 LNCS
ISSN (versión impresa)0302-9743
ISSN (versión digital)1611-3349

Conferencia

Conferencia17th International Conference on Image Analysis and Recognition, ICIAR 2020
País/TerritorioPortugal
CiudadPóvoa de Varzim
Período24/06/2026/06/20

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