Abstract
Diabetic retinopathy(DR) is the major cause of blindness, and the pathogenesis is unknown. Ultra-wide optical coherence tomography angiography imaging (UW-OCTA) can help ophthalmologists to diagnose DR. Automatic and accurate segmentation of lesions is essential for the diagnosis of DR, yet accurate identification and segmentation of lesions from UW-OCTA images remains a challenge. We proposed a modified nnUNet named nnUNet-CBAM. Three networks were trained to segment each lesion separately. Our method was evaluated in DRAC2022 diabetic retinopathy analysis challenge, where segmentation results were tested on 65 UW-OCTA images. These images are standardized UW-OCTA. Our method achieved a mean dice similarity coefficient (mDSC) of 0.4963 and a mean intersection over union (mIOU) of 0.3693.
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Gao, Z., Guo, J. (2023). Diabetic Retinal Overlap Lesion Segmentation Network. In: Sheng, B., Aubreville, M. (eds) Mitosis Domain Generalization and Diabetic Retinopathy Analysis. MIDOG DRAC 2022 2022. Lecture Notes in Computer Science, vol 13597. Springer, Cham. https://doi.org/10.1007/978-3-031-33658-4_5
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DOI: https://doi.org/10.1007/978-3-031-33658-4_5
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