Análise comparativa da influência dos espaços de cores na segmentação multi-classe de Whole Slide Imaging do câncer de mama utilizando deep learning
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.
Referências
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Sabeena Beevi, K., Nair, M. S., and Bindu, G. R. (2019). Automatic mitosis detection in breast histopathology images using Convolutional Neural Network based deep transfer learning. Biocybernetics and Biomedical Engineering, 39(1):214–223.
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Tashk, A., Helfroush, M. S., Danyali, H., and Akbarzadeh-jahromi, M. (2015). Automatic detection of breast cancer mitotic cells based on the combination of textural, statistical and innovative mathematical features. Applied Mathematical Modelling, 39(20):6165– 6182.
World Cancer Research Fund (2019). Breast cancer statistics.
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Yan, R., Ren, F., Wang, Z., Wang, L., Ren, Y., Liu, Y., Rao, X., Zheng, C., and Zhang, F. (2019). A Hybrid Convolutional and Recurrent Deep Neural Network for Breast Cancer Pathological Image Classification. IEEE International Conference on Bioinformatics and Biomedicine, pages 957–962.
Baker, Q. B., Zaitoun, T. A., Banat, S., Eaydat, E., and Alsmirat, M. (2018). Automated Detection of Benign and Malignant in Breast Histopathology Images. In 15th International Conference on Computer Systems and Applications, pages 1–5. IEEE.
Balazsi, M., Blanco, P., Zoroquiain, P., Levine, M. D., and Burnier, M. N. (2016). Invasive ductal breast carcinoma detector that is robust to image magnification in whole digital slides. Journal of Medical Imaging, 3(2).
Bray, F., , et al. (2018). Global cancer statistics 2018: Globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: A Cancer Journal for Clinicians.
Chan, J. K. C. (2014). The wonderful colors of the hematoxylin–eosin stain in diagnostic surgical pathology. International Journal of Surgical Pathology, 22(1):12–32.
Cruz-Roa, A., Basavanhally, A., Gonzalez, F., Gilmore, H., Feldman, M., Ganesan, S., Shih, N., Tomaszewski, J., and Madabhushi, A. (2014). Automatic detection of invasive ductal carcinoma in whole slide images with convolutional neural networks. Medical Imaging 2014: Digital Pathology, 9041:904103.
Gecer, B., Aksoy, S., Mercan, E., Shapiro, L. G., Weaver, D. L., and Elmore, J. G. (2018). Detection and classification of cancer in whole slide breast histopathology images using deep convolutional networks. Pattern Recognition, 84:345–356.
Gonzalez, R. C. and Woods, R. E. (2018). Digital Image Processing. Pearson, New York, 4 edition.
Guo, Z., Liu, H., Ni, H., Wang, X., Su, M., Guo, W., Wang, K., Jiang, T., and Qian, Y. (2019). A Fast and Refined Cancer Regions Segmentation Framework in Whole-slide Breast Pathological Images. Scientific Reports, 9(1):882.
He, K., Zhang, X., Ren, S., and Sun, J. (2016). Deep residual learning for image recognition. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 770–778.
INCA (2019). Cancer de mama.
Issac Niwas, S., Palanisamy, P., Chibbar, R., and Zhang, W. J. (2012). An expert support system for breast cancer diagnosis using color wavelet features. Journal of Medical Systems, 36(5):3091–3102.
Khan, A. M., Sirinukunwattana, K., and Rajpoot, N. (2015). A Global Covariance Descriptor for Nuclear Atypia Scoring in Breast Histopathology Images. IEEE Journal of Biomedical and Health Informatics, 19(5):1637–1647.
Kumar, V., Abbas, A. K., and Aster, J. C. (2016). Robbins & Cotran Patologia — Bases Patologicas das Doenças . Elsevier, Sao Paulo, 9 edition.
Li, C., Wang, X., Liu, W., and Latecki, L. J. (2018). DeepMitosis: Mitosis detection via deep detection, verification and segmentation networks. Medical Image Analysis, 45:121–133.
Makki, J. (2015). Diversity of breast carcinoma: histological subtypes and clinical relevance. Clinical Medicine Insights: Pathology, 8:CPath–S31563.
Mescher, A. L. (2018). Junqueira’s Basic Histology: Text and Atlas, Thirteenth Edition: Text and Atlas, Thirteenth Edition. McGraw-Hill Education, New York, 15 edition.
Mittal, H. and Saraswat, M. (2019). An automatic nuclei segmentation method using intelligent gravitational search algorithm based superpixel clustering. Swarm and Evolutionary Computation, 45:15–32.
Otsu, N. (1979). A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics, 9(1):62–66.
Ronneberger, O., Fischer, P., and Brox, T. (2015). U-net: Convolutional networks for biomedical image segmentation. In Medical Image Computing and Computer-Assisted Intervention - MICCAI 2015, pages 234–241, Cham. Springer International Publishing.
Roy, K., Banik, D., Bhattacharjee, D., and Nasipuri, M. (2019). Patch-based system for Classification of Breast Histology images using deep learning. Computerized Medical Imaging and Graphics, 71:90–103.
Sabeena Beevi, K., Nair, M. S., and Bindu, G. R. (2019). Automatic mitosis detection in breast histopathology images using Convolutional Neural Network based deep transfer learning. Biocybernetics and Biomedical Engineering, 39(1):214–223.
Spanhol, F. A., Oliveira, L. S., Petitjean, C., and Heutte, L. (2016). Breast cancer histopathological image classification using Convolutional Neural Networks. In Proceedings of the International Joint Conference on Neural Networks, volume 2016-Octob, pages 2560–2567. Institute of Electrical and Electronics Engineers Inc.
Tashk, A., Helfroush, M. S., Danyali, H., and Akbarzadeh-jahromi, M. (2015). Automatic detection of breast cancer mitotic cells based on the combination of textural, statistical and innovative mathematical features. Applied Mathematical Modelling, 39(20):6165– 6182.
World Cancer Research Fund (2019). Breast cancer statistics.
World Health Organization (2019a). Cancer.
World Health Organization (2019b). Cancer - diagnosis and treatment.
Yan, R., Ren, F., Wang, Z., Wang, L., Ren, Y., Liu, Y., Rao, X., Zheng, C., and Zhang, F. (2019). A Hybrid Convolutional and Recurrent Deep Neural Network for Breast Cancer Pathological Image Classification. IEEE International Conference on Bioinformatics and Biomedicine, pages 957–962.
Publicado
15/09/2020
Como Citar
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.
