Análise comparativa da influência dos espaços de cores na segmentação multi-classe de Whole Slide Imaging do câncer de mama utilizando deep learning

  • Felipe Zeiser UNISINOS
  • Cristiano Costa UNISINOS
  • Gabriel Ramos UNISINOS
  • Adriana Roehe UFCSPA
  • Henrique Bohn UNISINOS
  • Ismael Santos UNISINOS
  • Rodrigo Righi UNISINOS

Resumo


O diagnóstico em estágios iniciais normalmente resulta em um prognóstico melhor nos casos de câncer. Atualmente, a análise histopatológica e o padrão ouro para o diagnóstico, estadiamento e definição do tratamento de neoplasias de mama. Contudo, a técnica possui algumas restrições que dificultam o processo de análise pelo patologista, como diferentes protocolos de coloração, variações de coloração nas lâminas, e sobreposição de tecidos. Assim, a representação computacional das cores pode exercer uma influência significativa no comportamento dos modelos convolucionais. Desta forma, este estudo propõe uma análise comparativa da influência de quatro espaços de cores (RGB, HSV, YCrCb e LAB) para a segmentação multi-classe de Whole Slide Imaging (WSI) do câncer de mama. A metodologia proposta e composta por uma etapa de pré-processamento das WSI, data augmentation e multi- segmentação utilizando a arquitetura convolucional U-Net com uma ResNet-50 pré-treinada como codificador. Os resultados obtidos demonstram que o espaço de cores HSV apresentou de maneira geral índices melhores para a segmentação das WSI utilizando a metodologia proposta neste estudo.

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Publicado
15/09/2020
ZEISER, Felipe; COSTA, Cristiano; RAMOS, Gabriel; ROEHE, Adriana; BOHN, Henrique; SANTOS, Ismael; RIGHI, Rodrigo . Análise comparativa da influência dos espaços de cores na segmentação multi-classe de Whole Slide Imaging do câncer de mama utilizando deep learning. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO APLICADA À SAÚDE (SBCAS), 20. , 2020, Evento Online. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2020 . p. 131-142. ISSN 2763-8952. DOI: https://doi.org/10.5753/sbcas.2020.11508.