Improving the quality of information systems for diagnosis: an approach to detecting and correcting segmentation errors
Abstract
Research Context: The development of automated Information Systems (IS) capable of segmenting the left ventricle (LV) in cardiac magnetic resonance imaging (MRI) and estimating clinically relevant biomarkers is fundamental to support diagnostic decision-making. Scientific and/or Practical Problem: Many automated IS based on deep learning (DL) are not fully reliable and lack dedicated modules for error detection, which makes them dependent on constant manual inspection and correction. Proposed Solution and/or Analysis: We propose a post-processing method for IS that automatically detects and corrects segmentation errors in LV cardiac MRI produced by DL systems. Detection is performed by combining metrics computed between consecutive time frames of MRI to identify inconsistent segmentations in the temporal dimension; the correction step reconstructs the problematic frame by interpolating nearby segmentations. Related IS Theory: This work is grounded in the perspectives of Information Processing Theory. Research Method: The method was validated on LV segmentations containing both artificially generated and real DL errors, using the LVQuan19 dataset with reference segmentations for all time instants. Summary of Results: The method achieved detection performance with F1-score values up to 0.99 on real data, particularly for severe errors. Regarding the correction step, the selected strategy effectively improved segmentation consistency, achieving Dice coefficient values close to 0.95, indicating excellent agreement with reference segmentations. Contributions and Impact to IS area: This study contributes to the Information Systems field by introducing a method that improves the reliability of automated computer-aided systems for diagnosis. Although validated in the context of LV segmentation, the proposed approach can be applied to other domains where sequential data consistency is critical.
References
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Bergamasco, L. C. C., Lima, K. R. P. S., Rochitte, C. E., and Nunes, F. L. S. (2022). A bipartite graph approach to retrieve similar 3d models with different resolution and types of cardiomyopathies. Expert Systems with Applications, 193:116422.
Bernard, O., Lalande, A., Zotti, C., Cervenansky, F., Yang, X., Heng, P.-A., Cetin, I., Lekadir, K., Camara, O., Ballester, M. A. G., Sanroma, G., Napel, S., Petersen, S., Tziritas, G., Grinias, E., Khened, M., Kollerathu, V. A., Krishnamurthi, G., Rohe, M.-M., Pennec, X., Sermesant, M., Isensee, F., Jager, P., Maier-Hein, K. H., Full, P. M., Wolf, I., Engelhardt, S., Baumgartner, C. F., Koch, L. M., Wolterink, J. M., Isgum, I., Jang, Y., Hong, Y., Patravali, J., Jain, S., Humbert, O., and Jodoin, P.-M. (2018). Deep learning techniques for automatic MRI cardiac multi-structures segmentation and diagnosis: Is the problem solved? IEEE Transactions on Medical Imaging, 37(11):2514–2525.
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Guetari, R., Ayari, H., and Sakly, H. (2023). Computer-aided diagnosis systems: a comparative study of classical machine learning versus deep learning-based approaches. Knowledge and Information Systems, 65(10):3881–3921.
Izquierdo, C., Casas, G., Martin-Isla, C., Campello, V. M., Guala, A., Gkontra, P., Rodríguez-Palomares, J. F., and Lekadir, K. (2021). Radiomics-based classification of left ventricular non-compaction, hypertrophic cardiomyopathy, and dilated cardiomyopathy in cardiovascular magnetic resonance. Frontiers in Cardiovascular Medicine, 8.
Juhl, K. A., Slipsager, J., de Backer, O., Kofoed, K., Camara, O., and Paulsen, R. (2024). Signed distance field based segmentation and statistical shape modelling of the left atrial appendage.
Liu, Q., Lu, Q., Chai, Y., Tao, Z., Wu, Q., Jiang, M., and Pu, J. (2023). Radiomics-based quality control system for automatic cardiac segmentation: A feasibility study. Bioengineering, 10(7):791.
Moon, W. K., Lee, Y.-W., Ke, H.-H., Lee, S. H., Huang, C.-S., and Chang, R.-F. (2020). Computer-aided diagnosis of breast ultrasound images using ensemble learning from convolutional neural networks. Computer Methods and Programs in Biomedicine, 190:105361.
Mortensen, E. and Taylor, D. (1999). An image space algorithm for morphological contour interpolation.
Nunes, V., Cappelli, C., and Ralha, C. (2017). Transparency in information systems. In I GranDSI-BR: Grandes Desafios da Pesquisa em Sistemas de Informação no Brasil para o período de 2016 a 2026, pages 73–89. SBC.
Osher, S. and Fedkiw, R. (2003). Signed Distance Functions, page 17–22. Springer New York.
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.
Phellan, R., Lindner, T., Helle, M., Falcao, A. X., and Forkert, N. D. (2018). Automatic temporal segmentation of vessels of the brain using 4d asl mra images. IEEE Transactions on Biomedical Engineering, 65(7):1486–1494.
Rafael C. Gonzalez, R. E. W. (2002). Digital Image Processing. Prentice Hall, New Jersey, USA, 2nd ed edition.
Raquel, B. F., Ribeiro, M. A. O., and Nunes, F. L. S. (2025). “Fábrica de erros”: um arcabouço para simulação de erros na segmentação do ventrículo esquerdo em imagens cardíacas. In Anais Estendidos do XXV Simpósio Brasileiro de Computação Aplicada à Saúde (SBCAS 2025), SBCAS Estendido 2025, page 25–30. Sociedade Brasileira de Computação (SBC).
Raya, S. and Udupa, J. (1990). Shape-based interpolation of multidimensional objects. IEEE Transactions on Medical Imaging, 9(1):32–42.
Ribeiro, M. A. O. (2022). Segmentação do ventrículo esquerdo em exames de ressonância magnética cardíaca com aprendizado profundo e modelos deformáveis contendo restrições de forma. PhD thesis, Universidade de Sao Paulo, Agencia USP de Gestao da Informacao Academica (AGUIA).
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), pages 3505–3512.
Ribeiro, M. A. O. and Nunes, F. L. S. (2022a). Abordagem híbrida adaptativa para segmentação do ventrículo esquerdo em exames de ressonância magnética cardíaca. Projeto de Pesquisa (Doutorado em Ciências) – Programa de Pós-graduação em Sistemas de Informação (PPgSI), Escola de Artes, Ciências e Humanidades da Universidade de São Paulo (EACH-USP).
Ribeiro, M. A. O. and Nunes, F. L. S. (2022b). Left ventricle segmentation in cardiac MR: A systematic mapping of the past decade. ACM Comput. Surv., 54(11s).
Ribeiro, M. A. O. and Nunes, F. L. S. (2023). Left ventricle segmentation combining deep learning and deformable models with anatomical constraints. Journal of Biomedical Informatics, 142:104366.
Robinson, R., Valindria, V. V., Bai, W., Oktay, O., Kainz, B., Suzuki, H., Sanghvi, M. M., Aung, N., Paiva, J. M., Zemrak, F., Fung, K., Lukaschuk, E., Lee, A. M., Carapella, V., Kim, Y. J., Piechnik, S. K., Neubauer, S., Petersen, S. E., Page, C., Matthews, P. M., Rueckert, D., and Glocker, B. (2019). Automated quality control in image segmentation: application to the UK biobank cardiovascular magnetic resonance imaging study. Journal of Cardiovascular Magnetic Resonance, 21(1):18.
Robinson, R., Valindria, V. V., Bai, W., Suzuki, H., Matthews, P. M., Page, C., Rueckert, D., and Glocker, B. (2017). Automatic quality control of cardiac MRI segmentation in large-scale population imaging. In Medical Image Computing and Computer Assisted Intervention − MICCAI 2017: 20th International Conference, Quebec City, QC, Canada, September 11-13, 2017, Proceedings, Part I, page 720–727, Berlin, Heidelberg. Springer-Verlag.
Sander, J., de Vos, B. D., and Išgum, I. (2020). Automatic segmentation with detection of local segmentation failures in cardiac MRI. Scientific Reports, 10(1).
Shoaib, M. A., Chuah, J. H., Ali, R., Hasikin, K., Khalil, A., Hum, Y. C., Tee, Y. K., Dhanalakshmi, S., and Lai, K. W. (2023). An overview of deep learning methods for left ventricle segmentation. Computational Intelligence and Neuroscience, 2023(1).
Tavares, R. S., Sato, A. K., de Sales Guerra Tsuzuki, M., Gotoh, T., Kagei, S., and Iwasawa, T. (2010). Temporal segmentation of lung region mr image sequences using hough transform. In 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology, page 4789–4792. IEEE.
Thirion, J.-P. (1998). Image matching as a diffusion process: an analogy with maxwell’s demons. Medical Image Analysis, 2(3):243–260.
Valindria, V. V., Lavdas, I., Bai, W., Kamnitsas, K., Aboagye, E. O., Rockall, A. G., Rueckert, D., and Glocker, B. (2017). Reverse classification accuracy: Predicting segmentation performance in the absence of ground truth. IEEE Transactions on Medical Imaging, 36(8):1597–1606.
Xue, W., Li, J., Hu, Z., Kerfoot, E., Clough, J., Oksuz, I., Xu, H., Grau, V., Guo, F., Ng, M., Li, X., Li, Q., Liu, L., Ma, J., Grinias, E., Tziritas, G., Yan, W., Atehortua, A., Garreau, M., Jang, Y., Debus, A., Ferrante, E., Yang, G., Hua, T., and Li, S. (2021). Left ventricle quantification challenge: A comprehensive comparison and evaluation of segmentation and regression for mid-ventricular short-axis cardiac MR data. IEEE J. Biomed. Health Inform., 25(9):3541–3553.
Yang, R., Mirmehdi, M., Xie, X., and Hall, D. (2013). Shape and appearance priors for level set-based left ventricle segmentation. IET Computer Vision, 7(3):170–183.
Zou, K., Chen, Z., Yuan, X., Shen, X., Wang, M., and Fu, H. (2023). A review of uncertainty estimation and its application in medical imaging. Meta-Radiology, 1(1):100003.
Aganj, I., Iglesias, J. E., Reuter, M., Sabuncu, M. R., and Fischl, B. (2017). Mid-space-independent deformable image registration. NeuroImage, 152:158–170.
Anari, S., Tataei Sarshar, N., Mahjoori, N., Dorosti, S., and Rezaie, A. (2022). Review of deep learning approaches for thyroid cancer diagnosis. Mathematical Problems in Engineering, 2022:1–8.
Arega, T. W., Bricq, S., and Meriaudeau, F. (2025). Post-hoc out-of-distribution detection for cardiac MRI segmentation. Computerized Medical Imaging and Graphics, 119:102476.
Asiri, N., Hussain, M., Al Adel, F., and Alzaidi, N. (2019). Deep learning based computer-aided diagnosis systems for diabetic retinopathy: A survey. Artificial Intelligence in Medicine, 99:101701.
Avendi, M., Kheradvar, A., and Jafarkhani, H. (2016). A combined deep-learning and deformable-model approach to fully automatic segmentation of the left ventricle in cardiac MRI. Medical Image Analysis, 30:108–119. [link]
Bergamasco, L. C. C., Lima, K. R. P. S., Rochitte, C. E., and Nunes, F. L. S. (2018). 3D medical objects retrieval approach using spharms descriptor and network flow as similarity measure. In Proceedings of the 31st Conference on Graphics, Patterns and Images (SIBGRAPI 2018), pages 329–336, Piscataway, NJ, USA. IEEE.
Bergamasco, L. C. C., Lima, K. R. P. S., Rochitte, C. E., and Nunes, F. L. S. (2022). A bipartite graph approach to retrieve similar 3d models with different resolution and types of cardiomyopathies. Expert Systems with Applications, 193:116422.
Bernard, O., Lalande, A., Zotti, C., Cervenansky, F., Yang, X., Heng, P.-A., Cetin, I., Lekadir, K., Camara, O., Ballester, M. A. G., Sanroma, G., Napel, S., Petersen, S., Tziritas, G., Grinias, E., Khened, M., Kollerathu, V. A., Krishnamurthi, G., Rohe, M.-M., Pennec, X., Sermesant, M., Isensee, F., Jager, P., Maier-Hein, K. H., Full, P. M., Wolf, I., Engelhardt, S., Baumgartner, C. F., Koch, L. M., Wolterink, J. M., Isgum, I., Jang, Y., Hong, Y., Patravali, J., Jain, S., Humbert, O., and Jodoin, P.-M. (2018). Deep learning techniques for automatic MRI cardiac multi-structures segmentation and diagnosis: Is the problem solved? IEEE Transactions on Medical Imaging, 37(11):2514–2525.
Cosarinsky, M., Billot, R., Mansilla, L., Jimenez, G., Gaggión, N., Fu, G., Tirer, T., and Ferrante, E. (2025). Conformal in-context reverse classification accuracy: Efficient estimation of segmentation quality with statistical guarantees.
Costa, S. S. H., Gonçalves, V. M., Ribeiro, M. A. O., and Nunes, F. L. S. (2025). Generalization of cardiomyopathy classification models based on feature descriptors from magnetic resonance imaging. In Anais do XXV Simpósio Brasileiro de Computação Aplicada à Saúde (SBCAS 2025), SBCAS 2025, page 943–954. Sociedade Brasileira de Computação - SBC.
Dreijer, J. F., Herbst, B. M., and du Preez, J. A. (2013). Left ventricular segmentation from MRI datasets with edge modelling conditional random fields. BMC Medical Imaging, 13(1).
Gonçalves, V. M. and Nunes, F. L. S. (2021). Abordagem híbrida para auxílio ao diagnóstico de cardiomiopatias. Projeto de Pesquisa (Doutorado em Ciências) – Programa de Pós-graduação em Sistemas de Informação (PPgSI), Escola de Artes, Ciências e Humanidades da Universidade de São Paulo (EACH-USP).
Guetari, R., Ayari, H., and Sakly, H. (2023). Computer-aided diagnosis systems: a comparative study of classical machine learning versus deep learning-based approaches. Knowledge and Information Systems, 65(10):3881–3921.
Izquierdo, C., Casas, G., Martin-Isla, C., Campello, V. M., Guala, A., Gkontra, P., Rodríguez-Palomares, J. F., and Lekadir, K. (2021). Radiomics-based classification of left ventricular non-compaction, hypertrophic cardiomyopathy, and dilated cardiomyopathy in cardiovascular magnetic resonance. Frontiers in Cardiovascular Medicine, 8.
Juhl, K. A., Slipsager, J., de Backer, O., Kofoed, K., Camara, O., and Paulsen, R. (2024). Signed distance field based segmentation and statistical shape modelling of the left atrial appendage.
Liu, Q., Lu, Q., Chai, Y., Tao, Z., Wu, Q., Jiang, M., and Pu, J. (2023). Radiomics-based quality control system for automatic cardiac segmentation: A feasibility study. Bioengineering, 10(7):791.
Moon, W. K., Lee, Y.-W., Ke, H.-H., Lee, S. H., Huang, C.-S., and Chang, R.-F. (2020). Computer-aided diagnosis of breast ultrasound images using ensemble learning from convolutional neural networks. Computer Methods and Programs in Biomedicine, 190:105361.
Mortensen, E. and Taylor, D. (1999). An image space algorithm for morphological contour interpolation.
Nunes, V., Cappelli, C., and Ralha, C. (2017). Transparency in information systems. In I GranDSI-BR: Grandes Desafios da Pesquisa em Sistemas de Informação no Brasil para o período de 2016 a 2026, pages 73–89. SBC.
Osher, S. and Fedkiw, R. (2003). Signed Distance Functions, page 17–22. Springer New York.
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.
Phellan, R., Lindner, T., Helle, M., Falcao, A. X., and Forkert, N. D. (2018). Automatic temporal segmentation of vessels of the brain using 4d asl mra images. IEEE Transactions on Biomedical Engineering, 65(7):1486–1494.
Rafael C. Gonzalez, R. E. W. (2002). Digital Image Processing. Prentice Hall, New Jersey, USA, 2nd ed edition.
Raquel, B. F., Ribeiro, M. A. O., and Nunes, F. L. S. (2025). “Fábrica de erros”: um arcabouço para simulação de erros na segmentação do ventrículo esquerdo em imagens cardíacas. In Anais Estendidos do XXV Simpósio Brasileiro de Computação Aplicada à Saúde (SBCAS 2025), SBCAS Estendido 2025, page 25–30. Sociedade Brasileira de Computação (SBC).
Raya, S. and Udupa, J. (1990). Shape-based interpolation of multidimensional objects. IEEE Transactions on Medical Imaging, 9(1):32–42.
Ribeiro, M. A. O. (2022). Segmentação do ventrículo esquerdo em exames de ressonância magnética cardíaca com aprendizado profundo e modelos deformáveis contendo restrições de forma. PhD thesis, Universidade de Sao Paulo, Agencia USP de Gestao da Informacao Academica (AGUIA).
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), pages 3505–3512.
Ribeiro, M. A. O. and Nunes, F. L. S. (2022a). Abordagem híbrida adaptativa para segmentação do ventrículo esquerdo em exames de ressonância magnética cardíaca. Projeto de Pesquisa (Doutorado em Ciências) – Programa de Pós-graduação em Sistemas de Informação (PPgSI), Escola de Artes, Ciências e Humanidades da Universidade de São Paulo (EACH-USP).
Ribeiro, M. A. O. and Nunes, F. L. S. (2022b). Left ventricle segmentation in cardiac MR: A systematic mapping of the past decade. ACM Comput. Surv., 54(11s).
Ribeiro, M. A. O. and Nunes, F. L. S. (2023). Left ventricle segmentation combining deep learning and deformable models with anatomical constraints. Journal of Biomedical Informatics, 142:104366.
Robinson, R., Valindria, V. V., Bai, W., Oktay, O., Kainz, B., Suzuki, H., Sanghvi, M. M., Aung, N., Paiva, J. M., Zemrak, F., Fung, K., Lukaschuk, E., Lee, A. M., Carapella, V., Kim, Y. J., Piechnik, S. K., Neubauer, S., Petersen, S. E., Page, C., Matthews, P. M., Rueckert, D., and Glocker, B. (2019). Automated quality control in image segmentation: application to the UK biobank cardiovascular magnetic resonance imaging study. Journal of Cardiovascular Magnetic Resonance, 21(1):18.
Robinson, R., Valindria, V. V., Bai, W., Suzuki, H., Matthews, P. M., Page, C., Rueckert, D., and Glocker, B. (2017). Automatic quality control of cardiac MRI segmentation in large-scale population imaging. In Medical Image Computing and Computer Assisted Intervention − MICCAI 2017: 20th International Conference, Quebec City, QC, Canada, September 11-13, 2017, Proceedings, Part I, page 720–727, Berlin, Heidelberg. Springer-Verlag.
Sander, J., de Vos, B. D., and Išgum, I. (2020). Automatic segmentation with detection of local segmentation failures in cardiac MRI. Scientific Reports, 10(1).
Shoaib, M. A., Chuah, J. H., Ali, R., Hasikin, K., Khalil, A., Hum, Y. C., Tee, Y. K., Dhanalakshmi, S., and Lai, K. W. (2023). An overview of deep learning methods for left ventricle segmentation. Computational Intelligence and Neuroscience, 2023(1).
Tavares, R. S., Sato, A. K., de Sales Guerra Tsuzuki, M., Gotoh, T., Kagei, S., and Iwasawa, T. (2010). Temporal segmentation of lung region mr image sequences using hough transform. In 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology, page 4789–4792. IEEE.
Thirion, J.-P. (1998). Image matching as a diffusion process: an analogy with maxwell’s demons. Medical Image Analysis, 2(3):243–260.
Valindria, V. V., Lavdas, I., Bai, W., Kamnitsas, K., Aboagye, E. O., Rockall, A. G., Rueckert, D., and Glocker, B. (2017). Reverse classification accuracy: Predicting segmentation performance in the absence of ground truth. IEEE Transactions on Medical Imaging, 36(8):1597–1606.
Xue, W., Li, J., Hu, Z., Kerfoot, E., Clough, J., Oksuz, I., Xu, H., Grau, V., Guo, F., Ng, M., Li, X., Li, Q., Liu, L., Ma, J., Grinias, E., Tziritas, G., Yan, W., Atehortua, A., Garreau, M., Jang, Y., Debus, A., Ferrante, E., Yang, G., Hua, T., and Li, S. (2021). Left ventricle quantification challenge: A comprehensive comparison and evaluation of segmentation and regression for mid-ventricular short-axis cardiac MR data. IEEE J. Biomed. Health Inform., 25(9):3541–3553.
Yang, R., Mirmehdi, M., Xie, X., and Hall, D. (2013). Shape and appearance priors for level set-based left ventricle segmentation. IET Computer Vision, 7(3):170–183.
Zou, K., Chen, Z., Yuan, X., Shen, X., Wang, M., and Fu, H. (2023). A review of uncertainty estimation and its application in medical imaging. Meta-Radiology, 1(1):100003.
Published
2026-05-25
How to Cite
RAQUEL, Bruno F.; RIBEIRO, Matheus A. O.; NUNES, Fátima L. S..
Improving the quality of information systems for diagnosis: an approach to detecting and correcting segmentation errors. In: BRAZILIAN SYMPOSIUM ON INFORMATION SYSTEMS (SBSI), 22. , 2026, Vitória/ES.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2026
.
p. 101-120.
DOI: https://doi.org/10.5753/sbsi.2026.248301.
