International journal of retina and vitreous · 2026
Microaneurysm segmentation under out-of-domain generalization: from diabetic retinopathy to leukemic retinopathy.
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Platas-Campero Edgar, Díaz-Hernández Raquel, Sánchez-Sánchez Anabel, Zapotecas-Martínez Saúl, Sánchez-Medel Nohemi, Mohan Sashwanthi, Habib-Castillo Jamyl, Altamirano-Robles Leopoldo
Аннотация
PURPOSE: To propose inter-disease out-of-domain generalization (OODG) across retinal diseases for microaneurysm (MA) segmentation using a deep-learning model trained and validated on diabetic retinopathy (DR) and qualitatively evaluated on leukemic retinopathy (LR).
METHODS: A U-Net based segmentation model was trained using the IDRiD dataset, which comprises 81 DR images, using only MA annotations. The images were split into patches, and a statistical filtering step was applied to retain only structurally homogeneous patches. The study was organized in two phases: in Phase I, the U-Net was trained and evaluated using DR patches; in Phase II, the model was tested directly, without retraining, on LR images. Finally, MA segmentations were subjected to qualitative assessment by clinical specialists.
RESULTS: The proposed U-Net achieved an IoU of 0.842, a Dice score of 0.914, an accuracy of 0.998, and a validation loss of 0.120.
CONCLUSION: The results suggest that knowledge learned from DR can generalize effectively to a related clinical context such as LR, opening the possibility of reusing models in diseases with similar structures and lesion patterns.
METHODS: A U-Net based segmentation model was trained using the IDRiD dataset, which comprises 81 DR images, using only MA annotations. The images were split into patches, and a statistical filtering step was applied to retain only structurally homogeneous patches. The study was organized in two phases: in Phase I, the U-Net was trained and evaluated using DR patches; in Phase II, the model was tested directly, without retraining, on LR images. Finally, MA segmentations were subjected to qualitative assessment by clinical specialists.
RESULTS: The proposed U-Net achieved an IoU of 0.842, a Dice score of 0.914, an accuracy of 0.998, and a validation loss of 0.120.
CONCLUSION: The results suggest that knowledge learned from DR can generalize effectively to a related clinical context such as LR, opening the possibility of reusing models in diseases with similar structures and lesion patterns.