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:以复杂的病理特征为重点,例如边界模糊、症状之间的严重比例差异、背景噪音干扰等,以完成OCT图像中的视网膜水肿损伤联合分解任务,并使分解结果更加可靠。在本文件中,我们提出一个新的可靠的多尺度波子增强变压器网络,通过可靠性评估提供准确的分解结果。具体而言,目的是提高模型学习OCT图像中视网膜肿损伤的复杂病理特征的能力,我们开发了一个新颖的分解主干柱,将波浪加固地貌提取网络和我们新设计的多尺度变异器模块结合起来。与此同时,为了使分解结果更加可靠,我们根据主观逻辑证据理论引进了一个新的不确定分解头,以产生最后分解结果,并绘制相应的总体不确定性评估分数地图。我们在IR-Challenge 2018公共数据库上进行了全面的实验,结果显示,我们拟议的方法将实现更好的分解精度精度精度和高水平的分解分解器。