Focusing on the complicated pathological features, such as blurred boundaries, severe scale differences between symptoms, background noise interference, etc., in the task of retinal edema lesions joint segmentation from OCT images and enabling the segmentation results more reliable. In this paper, we propose a novel reliable multi-scale wavelet-enhanced transformer network, which can provide accurate segmentation results with reliability assessment. Specifically, aiming at improving the model's ability to learn the complex pathological features of retinal edema lesions in OCT images, we develop a novel segmentation backbone that integrates a wavelet-enhanced feature extractor network and a multi-scale transformer module of our newly designed. Meanwhile, to make the segmentation results more reliable, a novel uncertainty segmentation head based on the subjective logical evidential theory is introduced to generate the final segmentation results with a corresponding overall uncertainty evaluation score map. We conduct comprehensive experiments on the public database of AI-Challenge 2018 for retinal edema lesions segmentation, and the results show that our proposed method achieves better segmentation accuracy with a high degree of reliability as compared to other state-of-the-art segmentation approaches. The code will be released on: https://github.com/LooKing9218/ReliableRESeg.

翻译：针对OCT图像中视网膜水肿病变联合分割任务中存在的复杂病理特征（如边界模糊、症状间尺度差异严重、背景噪声干扰等），并提升分割结果的可靠性，本文提出了一种新颖的可靠多尺度小波增强Transformer网络，能够在提供可靠性评估的同时实现精确分割。具体而言，为提升模型对OCT图像中视网膜水肿病变复杂病理特征的学习能力，我们开发了一种新型分割主干网络，该网络集成了小波增强特征提取器与我们新设计的多尺度Transformer模块。同时，为使分割结果更可靠，引入了一种基于主观逻辑证据理论的新型不确定性分割头，生成最终分割结果及相应的整体不确定性评估得分图。我们在视网膜水肿病变分割公开数据库AI-Challenge 2018上进行了全面实验，结果表明，与其它最先进的分割方法相比，所提方法在分割精度和可靠性方面均表现更优。代码将发布于：https://github.com/LooKing9218/ReliableRESeg。