Treinando Rede Neural Profunda com Divisão Proporcional de Imagens para Segmentação de Estruturas da Retina

• Pedro Victor de A. Fonseca UFMA
• Alexandre Carvalho Araújo UFMA
• João Dallyson S. de Almeida UFMA
• Geraldo Braz Júnior UFMA

DOI: https://doi.org/10.5753/sbcas.2022.222421

Abstract

With the increase in the number of pathologies related to the human eye, the segmentation of the cup and the optical disc has become the main object of study by experiments linked to Deep Learning, aiming to improve the classification of the same structures allowing a better identification. This study proposes an approach for cup and optic disc segmentation that combines the proportional image splitting technique, concerning the segmentation area, and the U-Net network architecture with ResNet-34 encoder. The proposed approach showed promising results, achieving 96% of Dice in the optic disc segmentation in the RIM-ONE and DRISHTI-GS datasets and 90% and 85% of Dice in the optic cup segmentation in the DRISHTI-GS and RIM-ONE datasets, respectively.

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