Segmentação automática de núcleos cervicais em imagens de Papanicolaou
Resumo
A análise de células cervicais a partir de exames de Papanicolaou convencionais ainda é um grande desafio. Diferentemente das imagens de exame em meio líquido, a citologia convencional possui muita sobreposição celular e diversas estruturas epiteliais que dificultam a implementação de metodologias computacionais que possam dar suporte à automação dos exames. Este trabalho compara o desempenho de duas arquiteturas de redes neurais convolucionais para segmentar núcleos celulares. A avaliação considerou o uso de um novo banco de dados de segmentação. Os resultados mostraram que nossa proposta consegue segmentar núcleos celulares a partir de imagens de citologia convencional com múltiplas células e sobreposição.
Referências
Allehaibi, K. H. S., Nugroho, L. E., Lazuardi, L., Prabuwono, A. S., Mantoro, T., et al. (2019). Segmentation and classification of cervical cells using deep learning. IEEE Access, 7:116925-116941.
Amorim, J. G., Cerentini, A., Macarini, L. A. B., Matias, A. V., and von Wangenheim, A. (2020). Systematic literature review of computer vision-aided cytology.
Araújo, F. H., Silva, R. R., Ushizima, D. M., Rezende, M. T., Carneiro, C. M., Bianchi, A. G. C., and Medeiros, F. N. (2019). Deep learning for cell image segmentation and ranking. Computerized Medical Imaging and Graphics, 72:13-21.
Brostow, G. J., Fauqueur, J., and Cipolla, R. (2009). Semantic object classes in video: A high-definition ground truth database. Pattern Recognition Letters, 30(2):88-97.
Diniz, D. N., Rezende, M. T., Bianchi, A. G. C., Carneiro, C. M., Ushizima, D. M., de Medeiros, F. N. S., and Souza, M. J. F. (2021). A hierarchical feature-based methodology to perform cervical cancer classification. Applied Sciences, 11(9).
Gençtav, A., Aksoy, S., and Önder, S. (2012). Unsupervised segmentation and classification of cervical cell images. Pattern Recognition, 45(12):4151-4168.
Hussain, E., Mahanta, L. B., Das, C. R., Choudhury, M., and Chowdhury, M. (2020). A shape context fully convolutional neural network for segmentation and classification of cervical nuclei in pap smear images. Artificial Intelligence in Medicine, page 101897.
INCA (2020). Câncer no Brasil: dados dos registros de base populacional, volume 4. Instituto Nacional do Câncer.
Kendall, A., Badrinarayanan, V., and Cipolla, R. (2015). Bayesian segnet: Model uncertainty in deep convolutional encoder-decoder architectures for scene understanding. arXiv preprint arXiv:1511.02680.
Liu, Y., Zhang, P., Song, Q., Li, A., Zhang, P., and Gui, Z. (2018). Automatic segmentation of cervical nuclei based on deep learning and a conditional random field. IEEE Access, 6:53709-53721.
Long, J., Shelhamer, E., and Darrell, T. (2015). Fully convolutional networks for semantic segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 3431-3440.
Lu, Z., Carneiro, G., and Bradley, A. P. (2015). An improved joint optimization of multiple level set functions for the segmentation of overlapping cervical cells. IEEE Transactions on Image Processing, 24(4):1261-1272.
Nayar, R. and Wilbur, D. C. (2017). The bethesda system for reporting cervical cytology: a historical perspective. Acta cytologica, 61(4-5):359-372.
Paillassa, M., Bertin, E., and Bouy, H. (2019). Maximask and maxitrack: Two new tools for identifying contaminants in astronomical images using convolutional neural networks. Astronomy & Astrophysics, 634.
Plissiti, M. E., Dimitrakopoulos, P., Sfikas, G., Nikou, C., Krikoni, O., and Charchanti, A. (2018). Sipakmed: A new dataset for feature and image based classification of normal and pathological cervical cells in pap smear images. In 2018 25th IEEE International Conference on Image Processing (ICIP), pages 3144-3148. IEEE.
Rezende, M. T., Raniere, S., Machado, T. M., Costa, C. S., Ushizima, D. M., Carneiro, C. M., and Bianchi, A. G. C. (2021). Cric searchable image database as a public platform for conventional pap smear cytology data. Scientific Data, 8(1).
Ronneberger, O., Fischer, P., and Brox, T. (2015). U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention, pages 234-241. Springer.
Silva, R. R., Araujo, F. H., Ushizima, D. M., Bianchi, A. G., Carneiro, C. M., and Medeiros, F. N. (2019). Radial feature descriptors for cell classification and recommendation. Journal of Visual Communication and Image Representation, 62:105-116.
Simonyan, K. and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S. E., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2014). Going deeper with convolutions. CoRR, abs/1409.4842.
Wan, T., Xu, S., Sang, C., Jin, Y., and Qin, Z. (2019). Accurate segmentation of overlapping cells in cervical cytology with deep convolutional neural networks. Neurocomputing, 365:157-170.
Win, K. P., Kitjaidure, Y., Hamamoto, K., and Myo Aung, T. (2020). Computer-assisted screening for cervical cancer using digital image processing of pap smear images. Applied Sciences, 10(5):1800.
Xiang, Y., Sun, W., Pan, C., Yan, M., Yin, Z., and Liang, Y. (2020). A novel automationassisted cervical cancer reading method based on convolutional neural network. Biocybernetics and Biomedical Engineering, 40(2):611-623.
Zhang, J., Liu, Z., Du, B., He, J., Li, G., and Chen, D. (2019). Binary tree-like network with two-path fusion attention feature for cervical cell nucleus segmentation. Computers in biology and medicine, 108:223-233.
Zhang, L., Lu, L., Nogues, I., Summers, R. M., Liu, S., and Yao, J. (2017). Deeppap: deep convolutional networks for cervical cell classification. IEEE journal of biomedical and health informatics, 21(6):1633-1643.
Zou, J., Xue, Z., Brown, G., Long, R., and Antani, S. (2020). Deep learning for nuclei segmentation and cell classification in cervical liquid based cytology. In Medical Imaging 2020: Imaging Informatics for Healthcare, Research, and Applications, volume 11318, page 1131811. International Society for Optics and Photonics.
Amorim, J. G., Cerentini, A., Macarini, L. A. B., Matias, A. V., and von Wangenheim, A. (2020). Systematic literature review of computer vision-aided cytology.
Araújo, F. H., Silva, R. R., Ushizima, D. M., Rezende, M. T., Carneiro, C. M., Bianchi, A. G. C., and Medeiros, F. N. (2019). Deep learning for cell image segmentation and ranking. Computerized Medical Imaging and Graphics, 72:13-21.
Brostow, G. J., Fauqueur, J., and Cipolla, R. (2009). Semantic object classes in video: A high-definition ground truth database. Pattern Recognition Letters, 30(2):88-97.
Diniz, D. N., Rezende, M. T., Bianchi, A. G. C., Carneiro, C. M., Ushizima, D. M., de Medeiros, F. N. S., and Souza, M. J. F. (2021). A hierarchical feature-based methodology to perform cervical cancer classification. Applied Sciences, 11(9).
Gençtav, A., Aksoy, S., and Önder, S. (2012). Unsupervised segmentation and classification of cervical cell images. Pattern Recognition, 45(12):4151-4168.
Hussain, E., Mahanta, L. B., Das, C. R., Choudhury, M., and Chowdhury, M. (2020). A shape context fully convolutional neural network for segmentation and classification of cervical nuclei in pap smear images. Artificial Intelligence in Medicine, page 101897.
INCA (2020). Câncer no Brasil: dados dos registros de base populacional, volume 4. Instituto Nacional do Câncer.
Kendall, A., Badrinarayanan, V., and Cipolla, R. (2015). Bayesian segnet: Model uncertainty in deep convolutional encoder-decoder architectures for scene understanding. arXiv preprint arXiv:1511.02680.
Liu, Y., Zhang, P., Song, Q., Li, A., Zhang, P., and Gui, Z. (2018). Automatic segmentation of cervical nuclei based on deep learning and a conditional random field. IEEE Access, 6:53709-53721.
Long, J., Shelhamer, E., and Darrell, T. (2015). Fully convolutional networks for semantic segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 3431-3440.
Lu, Z., Carneiro, G., and Bradley, A. P. (2015). An improved joint optimization of multiple level set functions for the segmentation of overlapping cervical cells. IEEE Transactions on Image Processing, 24(4):1261-1272.
Nayar, R. and Wilbur, D. C. (2017). The bethesda system for reporting cervical cytology: a historical perspective. Acta cytologica, 61(4-5):359-372.
Paillassa, M., Bertin, E., and Bouy, H. (2019). Maximask and maxitrack: Two new tools for identifying contaminants in astronomical images using convolutional neural networks. Astronomy & Astrophysics, 634.
Plissiti, M. E., Dimitrakopoulos, P., Sfikas, G., Nikou, C., Krikoni, O., and Charchanti, A. (2018). Sipakmed: A new dataset for feature and image based classification of normal and pathological cervical cells in pap smear images. In 2018 25th IEEE International Conference on Image Processing (ICIP), pages 3144-3148. IEEE.
Rezende, M. T., Raniere, S., Machado, T. M., Costa, C. S., Ushizima, D. M., Carneiro, C. M., and Bianchi, A. G. C. (2021). Cric searchable image database as a public platform for conventional pap smear cytology data. Scientific Data, 8(1).
Ronneberger, O., Fischer, P., and Brox, T. (2015). U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention, pages 234-241. Springer.
Silva, R. R., Araujo, F. H., Ushizima, D. M., Bianchi, A. G., Carneiro, C. M., and Medeiros, F. N. (2019). Radial feature descriptors for cell classification and recommendation. Journal of Visual Communication and Image Representation, 62:105-116.
Simonyan, K. and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S. E., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2014). Going deeper with convolutions. CoRR, abs/1409.4842.
Wan, T., Xu, S., Sang, C., Jin, Y., and Qin, Z. (2019). Accurate segmentation of overlapping cells in cervical cytology with deep convolutional neural networks. Neurocomputing, 365:157-170.
Win, K. P., Kitjaidure, Y., Hamamoto, K., and Myo Aung, T. (2020). Computer-assisted screening for cervical cancer using digital image processing of pap smear images. Applied Sciences, 10(5):1800.
Xiang, Y., Sun, W., Pan, C., Yan, M., Yin, Z., and Liang, Y. (2020). A novel automationassisted cervical cancer reading method based on convolutional neural network. Biocybernetics and Biomedical Engineering, 40(2):611-623.
Zhang, J., Liu, Z., Du, B., He, J., Li, G., and Chen, D. (2019). Binary tree-like network with two-path fusion attention feature for cervical cell nucleus segmentation. Computers in biology and medicine, 108:223-233.
Zhang, L., Lu, L., Nogues, I., Summers, R. M., Liu, S., and Yao, J. (2017). Deeppap: deep convolutional networks for cervical cell classification. IEEE journal of biomedical and health informatics, 21(6):1633-1643.
Zou, J., Xue, Z., Brown, G., Long, R., and Antani, S. (2020). Deep learning for nuclei segmentation and cell classification in cervical liquid based cytology. In Medical Imaging 2020: Imaging Informatics for Healthcare, Research, and Applications, volume 11318, page 1131811. International Society for Optics and Photonics.
Publicado
07/06/2022
Como Citar
TEIXEIRA, Julia Beatriz A. et al.
Segmentação automática de núcleos cervicais em imagens de Papanicolaou. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO APLICADA À SAÚDE (SBCAS), 22. , 2022, Teresina.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2022
.
p. 346-357.
ISSN 2763-8952.
DOI: https://doi.org/10.5753/sbcas.2022.222682.
