{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T20:35:13Z","timestamp":1761165313934,"version":"build-2065373602"},"reference-count":15,"publisher":"Sociedade Brasileira de Computa\u00e7\u00e3o - SBC","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:p>Deep Learning has been widely applied to medical image segmentation, aiming to make structures clearer in images to help physicians identify unusual patterns and anomalies. Segmentation models face challenges in collecting a large amount of data for training, due to privacy concerns and pathological representation. Data Augmentation (DA) is an alternative to mitigate this challenge, expanding the dataset by applying transformations to the original set or creating new samples using generative methods. Despite the extensive use of DA techniques, there is still limited understanding of their relative effectiveness for medical image segmentation tasks. This work presents a method for evaluating the impact of DA methods, analyzing traditional augmentation techniques, and diffusion models for generating synthetic data in medical image segmentation models.<\/jats:p>","DOI":"10.5753\/sbbd.2025.247731","type":"proceedings-article","created":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T19:26:36Z","timestamp":1761074796000},"page":"830-836","source":"Crossref","is-referenced-by-count":0,"title":["Data Augmentation for Medical Image Segmentation: A Comparative Analysis of Traditional Techniques and Synthetic Data Generation"],"prefix":"10.5753","author":[{"given":"Mariana Aya S.","family":"Uchida","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Erikson J.","family":"de Aguiar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6625-6047","authenticated-orcid":false,"given":"Caetano","family":"Traina-Jr","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4929-7258","authenticated-orcid":false,"given":"Agma J. M.","family":"Traina","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"3742","published-online":{"date-parts":[[2025,9,29]]},"reference":[{"key":"1","doi-asserted-by":"crossref","unstructured":"Aktas, B., Ates, D. D., Duzyel, O., and Gumus, A. (2025). Diffusionbased data augmentation methodology for improved performance in ocular disease diagnosis using retinography images. International Journal of Machine Learning and Cybernetics, 16(5):3843\u20133864.","DOI":"10.1007\/s13042-024-02485-w"},{"key":"2","doi-asserted-by":"crossref","unstructured":"Azad, R., Aghdam, E. K., Rauland, A., Jia, Y., Avval, A. H., Bozorgpour, A., Karimijafarbigloo, S., Cohen, J. P., Adeli, E., and Merhof, D. (2024). Medical image segmentation review: The success of u-net. 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E., Dusza, S., Gutman, D., Helba, B., Kalloo, A., Liopyris, K., Marchetti, M., Kittler, H., and Halpern, A. (2019). Skin lesion analysis toward melanoma detection 2018: A challenge hosted by the international skin imaging collaboration (isic)."},{"key":"6","unstructured":"Consortium, M. (2024). Monai: Medical open network for ai."},{"key":"7","doi-asserted-by":"crossref","unstructured":"Goceri, E. (2023). Medical image data augmentation: techniques, comparisons and interpretations. Artificial Intelligence Review, 56(11):12561\u201312605.","DOI":"10.1007\/s10462-023-10453-z"},{"key":"8","unstructured":"Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., and Hochreiter, S. (2017). Gans trained by a two time-scale update rule converge to a local nash equilibrium. In Guyon, I., Luxburg, U. V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., and Garnett, R., editors, Advances in Neural Information Processing Systems, volume 30. 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P., Rodrigues, P. S., Lopes, F. J. P., Palmeira, O. F. J., Falc\u00e3o, A. X., Benato, B. C., and Giraldi, G. A. (2021). Aumento de dados utilizando firefly e level sets aplicado \u00e0 segmenta\u00e7\u00e3o de imagens m\u00e9dicas e biol\u00f3gicas. Revista Eletr\u00f4nica de Inicia\u00e7\u00e3o Cient\u00edfica em Computa\u00e7\u00e3o, 19(2)."},{"key":"13","doi-asserted-by":"crossref","unstructured":"Rayed, M. E., Islam, S. S., Niha, S. I., Jim, J. R., Kabir, M. M., and Mridha, M. (2024). Deep learning for medical image segmentation: State-of-the-art advancements and challenges. Informatics in Medicine Unlocked, 47:101504.","DOI":"10.1016\/j.imu.2024.101504"},{"key":"14","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015). U-net: Convolutional networks for biomedical image segmentation. In Navab, N., Hornegger, J., Wells, W. M., and Frangi, A. 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