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Focusing on the complicated pathological features, such as blurredboundaries, severe scale differences between symptoms, background noiseinterference, etc., in the task of retinal edema lesions joint segmentationfrom OCT images and enabling the segmentation results more reliable. In thispaper, we propose a novel reliable multi-scale wavelet-enhanced transformernetwork, which can provide accurate segmentation results with reliabilityassessment. Specifically, aiming at improving the model's ability to learn thecomplex pathological features of retinal edema lesions in OCT images, wedevelop a novel segmentation backbone that integrates a wavelet-enhancedfeature extractor network and a multi-scale transformer module of our newlydesigned. Meanwhile, to make the segmentation results more reliable, a noveluncertainty segmentation head based on the subjective logical evidential theoryis introduced to generate the final segmentation results with a correspondingoverall uncertainty evaluation score map. We conduct comprehensive experimentson the public database of AI-Challenge 2018 for retinal edema lesionssegmentation, and the results show that our proposed method achieves bettersegmentation accuracy with a high degree of reliability as compared to otherstate-of-the-art segmentation approaches. The code will be released on:https://github.com/LooKing9218/ReliableRESeg.

Reliable Joint Segmentation of Retinal Edema Lesions in OCT Images