Efficient Methodology Based on Convolutional Neural Networks with Augmented Penalization on Hard-to-Classify Boundary Voxels on the Task of Brain Lesion Segmentation

Gustavo Ulloa, Alejandro Veloz, Héctor Allende-Cid, Raúl Monge, Héctor Allende

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

Abstract

Medical images segmentation has become a fundamental tool for making more precise the assessment of complex diagnosis and surgical tasks. In particular, this work focuses on multiple sclerosis (MS) disease in which lesion segmentation is useful for getting an accurate diagnosis and for tracking its progression. In recent years, Convolutional Neural Networks (CNNs) have been successfully employed for segmenting MS lesions. However, these methods often fail in defining the boundaries of the MS lesions accurately. This work focuses on segmenting hard-to-classify voxels close to MS lesions boundaries in MRI, where it was determined that the application of a loss function that focuses the penalty on difficult voxels generates an increase in the results with respect to the Dice similarity metric (DSC), where the latter occurs as long as the sufficient representation of these voxels as well as an adequate preprocessing of the images of each patient. The methodology was tested in the public data set ISBI2015 and was compared with alternative methods that are trained using the binary cross entropy loss function and the focal loss function with uniform and stratified sampling, obtaining better results in DSC, reaching a DSC> 0.7, a threshold that is considered comparable to that obtained by another human expert.

Original languageEnglish
Title of host publicationPattern Recognition - 14th Mexican Conference, MCPR 2022, Proceedings
EditorsOsslan Osiris Vergara-Villegas, Vianey Guadalupe Cruz-Sánchez, Juan Humberto Sossa-Azuela, Jesús Ariel Carrasco-Ochoa, José Francisco Martínez-Trinidad, José Arturo Olvera-López
PublisherSpringer Science and Business Media Deutschland GmbH
Pages338-347
Number of pages10
ISBN (Print)9783031077494
DOIs
StatePublished - 2022
Externally publishedYes
Event14th Mexican Conference on Pattern Recognition, MCPR 2022 - Ciudad Juárez, Mexico
Duration: 22 Jun 202225 Jun 2022

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13264 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference14th Mexican Conference on Pattern Recognition, MCPR 2022
Country/TerritoryMexico
CityCiudad Juárez
Period22/06/2225/06/22

Keywords

  • Convolutional Neural Networks
  • Lesions segmentation
  • Loss function
  • Magnetic Resonance Imaging
  • Multiple sclerosis

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