Enhanced Semantic Segmentation of Retinal Microlesions through R2U-Net Architecture

• Alejandro Pereira UFPel
• Carlos Santos IFFar
• Marilton Aguiar UFPel
• Daniel Welfer UFSM
• Marcelo Dias UFPel
• Rafaela de Menezes IFFar
• Douglas Santana IFG

DOI: https://doi.org/10.5753/sbcas.2024.1737

Resumo

Diabetic Retinopathy (DR) is a microvascular complication related to diabetes that affects approximately 33% of individuals with this condition and, if not detected and treated early, can lead to irreversible vision loss. Fundus lesions such as Hard and Soft Exudates, Hemorrhages, and Microaneurysms typically identify DR. The development of computational methods to segment these lesions plays a fundamental role in the early diagnosis of the disease. This article proposes a new approach that uses an R2U-Net combined with data augmentation techniques for segmenting fundus lesions. We trained, adjusted, and evaluated the proposed approach in the DDR dataset, achieving an accuracy of 99.87% and an mIoU equal to 59.69%. Furthermore, we assessed it in the IDRiD dataset, achieving an mIoU of 49.92%. The results obtained in the experiments highlight the potential contribution of the approach in generating lesion annotations in creating new DR datasets, which is essential given the scarcity of annotations in publicly available datasets.

Referências

Alom, M. Z., Hasan, M., Yakopcic, C., Taha, T. M., and Asari, V. K. (2018). Recurrent residual convolutional neural network based on u-net (r2u-net) for medical image segmentation. arXiv preprint arXiv:1802.06955.

Anand, M. and Sundaram, A. M. (2023). Channel and spatial attention aware unet architecture for segmentation of blood vessels, exudates and microaneurysms in diabetic retinopathy. Preprint.

Buslaev, A., Iglovikov, V. I., Khvedchenya, E., Parinov, A., Druzhinin, M., and Kalinin, A. A. (2020). Albumentations: Fast and flexible image augmentations. Information (Switzerland), 11(2).

Chen, L. C., Zhu, Y., Papandreou, G., Schroff, F., and Adam, H. (2018). Encoder-decoder with atrous separable convolution for semantic image segmentation. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11211 LNCS:833–851.

He, K., Zhang, X., Ren, S., and Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, volume 2016-Decem, pages 770–778, Las Vegas, NV, USA, 27–30 June 2016.

Kanimozhi, J., Vasuki, P., and Roomi, S. M. M. (2021). Fundus image lesion detection algorithm for diabetic retinopathy screening. Journal of Ambient Intelligence and Humanized Computing, 12:7407–7416.

Lee, L. C., Liong, C.-Y., and Jemain, A. A. (2018). Validity of the best practice in splitting data for hold-out validation strategy as performed on the ink strokes in the context of forensic science. Microchemical Journal, 139:125–133.

Li, T., Gao, Y., Wang, K., Guo, S., Liu, H., and Kang, H. (2019). Diagnostic assessment of deep learning algorithms for diabetic retinopathy screening. Information Sciences, 501:511–522.

Liang, M. and Hu, X. (2015). Recurrent convolutional neural network for object recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 3367–3375.

Nair, A. T., ML, A., and MN, A. K. (2023). Segmentation of retinal images using improved segmentation network, mesu-net. International Journal of Online & Biomedical Engineering, 19(15).

Oktay, O., Schlemper, J., Folgoc, L. L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N. Y., Kainz, B., et al. (2018). Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:1804.03999.

Porwal, P., Pachade, S., Kokare, M., and Deshmukh, G. (2020). IDRiD: Diabetic Retinopathy – Segmentation and Grading Challenge. Medical Image Analysis, 59.

Ronneberger, O., Fischer, P., and Brox, T. (2015). U-net: Convolutional networks for biomedical image segmentation. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9351:234–241.

Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015). Imagenet large scale visual recognition challenge. International journal of computer vision, 115:211–252.

Shim, J.-H., Kim, W. S., Kim, K. G., Yee, G. T., Kim, Y. J., and Jeong, T. S. (2022). Evaluation of u-net models in automated cervical spine and cranial bone segmentation using x-ray images for traumatic atlanto-occipital dislocation diagnosis. Scientific Reports, 12(1):21438.

Tan, T.-E. and Wong, T. Y. (2023). Diabetic retinopathy: Looking forward to 2030. Frontiers in Endocrinology, 13:1077669.

Wang, H., Cao, P., Yang, J., and Zaiane, O. (2023). Mca-unet: multi-scale cross coattentional u-net for automatic medical image segmentation. Health Information Science and Systems, 11(1):10.

Xie, S. and Tu, Z. (2015). Holistically-nested edge detection. Proceedings of the IEEE International Conference on Computer Vision, 2015 Inter:1395–1403.

Zhou, Z., Rahman Siddiquee, M. M., Tajbakhsh, N., and Liang, J. (2018). Unet++: A nested u-net architecture for medical image segmentation. In Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support: 4th International Workshop, DLMIA 2018, and 8th International Workshop, ML-CDS 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 20, 2018, Proceedings 4, pages 3–11. Springer.