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、症状间尺度差异显著、背景噪声干扰等），并提高分割结果的可靠性，本文提出了一种新颖的可靠多尺度小波增强Transformer网络，该网络可在提供可靠性评估的同时实现精确的分割结果。具体而言，为提升模型学习OCT图像中视网膜水肿病变复杂病理特征的能力，我们开发了一种融合小波增强特征提取器网络与我们新设计的的多尺度Transformer模块的新型分割主干网络。同时，为使分割结果更加可靠，引入了一种基于主观逻辑证据理论的新型不确定度分割头，以生成最终分割结果及对应的整体不确定度评估得分图。我们在AI-Challenge 2018视网膜水肿病变分割公开数据库上进行了综合实验，结果表明，与其他先进分割方法相比，本方法在获得更高分割精度的同时兼具高可靠性。相关代码将在https://github.com/LooKing9218/ReliableRESeg发布。