标题： 二分类医学图像分割的指导特征增强 显示英文标题

标题： Instructive Feature Enhancement for Dichotomous Medical Image Segmentation

摘要： 深度神经网络已被广泛应用于多种模态下许多解剖结构的二分类医学图像分割（DMIS），取得了有前景的性能。 然而，现有的网络倾向于在任务特定、繁重和复杂的设计上挣扎以提高准确性。 它们对哪些特征通道对分割更有益缺乏明确的指导，这可能是这些分割模型的性能和通用性受到阻碍的原因。 在本研究中，我们提出了一种指导性的特征增强方法，即IFE，以自适应地选择具有丰富纹理线索和强区分能力的特征通道，基于局部曲率或全局信息熵标准来增强原始特征。 IFE可即插即用，并适用于各种DMIS任务，它鼓励模型关注纹理丰富的特征，这对于模糊和具有挑战性的边界识别尤为重要，同时实现了简洁性、通用性和一定的可解释性。 为了评估所提出的IFE，我们构建了第一个大规模DMIS数据集Cosmos55k，该数据集包含来自7种模态和26个解剖结构的55,023张图像。 大量实验表明，IFE仅通过轻微修改即可提升不同解剖结构和模态的经典分割网络的性能。 代码可在https://github.com/yezi-66/IFE获取。 显示英文摘要

摘要： Deep neural networks have been widely applied in dichotomous medical image segmentation (DMIS) of many anatomical structures in several modalities, achieving promising performance. However, existing networks tend to struggle with task-specific, heavy and complex designs to improve accuracy. They made little instructions to which feature channels would be more beneficial for segmentation, and that may be why the performance and universality of these segmentation models are hindered. In this study, we propose an instructive feature enhancement approach, namely IFE, to adaptively select feature channels with rich texture cues and strong discriminability to enhance raw features based on local curvature or global information entropy criteria. Being plug-and-play and applicable for diverse DMIS tasks, IFE encourages the model to focus on texture-rich features which are especially important for the ambiguous and challenging boundary identification, simultaneously achieving simplicity, universality, and certain interpretability. To evaluate the proposed IFE, we constructed the first large-scale DMIS dataset Cosmos55k, which contains 55,023 images from 7 modalities and 26 anatomical structures. Extensive experiments show that IFE can improve the performance of classic segmentation networks across different anatomies and modalities with only slight modifications. Code is available at https://github.com/yezi-66/IFE