ErrorSim: a deep learning error simulator in left ventricle segmentations

• Bruno F. Raquel USP
• Matheus A. O. Ribeiro USP
• Fátima L. S. Nunes USP

DOI: https://doi.org/10.5753/eniac.2025.13909

Resumo

A segmentação do ventrículo esquerdo em exames de Ressonância Magnética Cardíaca é importante para o diagnóstico médico. Métodos de aprendizado profundo têm se destacado ao obter segmentações semelhantes à de especialistas. Entretanto, uma das limitações atuais é a produção arbitrária de erros anatômicos que podem comprometer o diagnóstico. Dado esse problema, esse trabalho propõe um arcabouço para categorização e simulação controlada e automática de diferentes erros anatômicos. O arcabouço favorece o desenvolvimento de métodos voltados à detecção e à correção desses erros. Resultados indicam que o arcabouço proposto é capaz de gerar erros de diversas categorias e replicar os mesmos erros produzidos por redes (Dice > 0.8).

Referências

Bernard, O. et al. (2018). Deep learning techniques for automatic MRI cardiac multistructures segmentation and diagnosis: Is the problem solved? IEEE Transactions on Medical Imaging, 37(11):2514–2525.

Cong, C. and Zhang, H. (2018). Invert-u-net dnn segmentation model for MRI cardiac left ventricle segmentation. The Journal of Engineering, 2018(16):1463–1467.

Graves, C. V., Moreno, R. A., Rebelo, M. S., Nomura, C. H., and Gutierrez, M. A. (2020). Improving the generalization of deep learning methods to segment the left ventricle in short axis MR images. In 2020 42nd Annual International Conference of the IEEE Engineering in Medicine &; Biology Society (EMBC), page 1203–1206. IEEE.

Guan, S., Samala, R. K., Arab, A., and Chen, W. (2023). Miss-tool: medical image segmentation synthesis tool to emulate segmentation errors. In Iftekharuddin, K. M. and Chen, W., editors, Medical Imaging 2023: Computer-Aided Diagnosis, page 41. SPIE.

Guo, F., Ng, M., Goubran, M., Petersen, S. E., Piechnik, S. K., Neubauer, S., and Wright, G. (2020). Improving cardiac mri convolutional neural network segmentation on small training datasets and dataset shift: A continuous kernel cut approach. Medical Image Analysis, 61:101636.

Khened, M., Kollerathu, V. A., and Krishnamurthi, G. (2019). Fully convolutional multi-scale residual densenets for cardiac segmentation and automated cardiac diagnosis using ensemble of classifiers. Medical Image Analysis, 51:21–45.

Landman, B. and Warfield, S. (2013). 2013 cardiac atlas project standard challenge - participant project.

Lohr, D. et al. (2024). Precision imaging of cardiac function and scar size in acute and chronic porcine myocardial infarction using ultrahigh-field mri. Communications Medicine, 4(1).

Painchaud, N., Skandarani, Y., Judge, T., Bernard, O., Lalande, A., and Jodoin, P.-M. (2020). Cardiac segmentation with strong anatomical guarantees. IEEE Transactions on Medical Imaging, 39(11):3703–3713.

Radau, P., Lu, Y., Connelly, K., Paul, G., Dick, A. J., and Wright, G. A. (2009). Evaluation framework for algorithms segmenting short axis cardiac MRI. The MIDAS Journal.

Ribeiro, M. A. O. and Nunes, F. L. S. (2021). Evaluating the pre-processing impact on the generalization of deep learning networks for left ventricle segmentation. In 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), page 3505–3512. IEEE.

Ribeiro, M. A. O. and Nunes, F. L. S. (2022). Left ventricle segmentation in cardiac MR: A systematic mapping of the past decade. ACM Comput. Surv., 54(11s).

Sayin, B. Y. and Oto, A. (2022). Left ventricular hypertrophy: Etiology-based therapeutic options. Cardiology and Therapy, 11(2):203–230.

Suzuki, S. and Abe, K. (1985). Topological structural analysis of digitized binary images by border following. Computer Vision, Graphics, and Image Processing, 30(1):32–46.

Tajbakhsh, N., Lai, B., Ananth, S. P., and Ding, X. (2020). Errornet: Learning error representations from limited data to improve vascular segmentation. In 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), page 1364–1368. IEEE.

Waite, S., Kolla, S., Jeudy, J., Legasto, A., Macknik, S. L., Martinez-Conde, S., Krupinski, E. A., and Reede, D. L. (2017). Tired in the reading room: The influence of fatigue in radiology. Journal of the American College of Radiology, 14(2):191–197.

Wang, Y. et al. (2021). Deep learning based fully automatic segmentation of the left ventricular endocardium and epicardium from cardiac cine mri. Quantitative Imaging in Medicine and Surgery, 11(4):1600–1612.