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

翻译：深度神经网络已广泛应用于多种模态下众多解剖结构的二分医学图像分割（DMIS），并取得了显著性能。然而，现有网络往往依赖任务特定且繁重复杂的设计来提升精度，对哪些特征通道更有利于分割缺乏明确指导，这可能是限制其性能与通用性的原因。本研究提出了一种名为IFE的指导性特征增强方法，基于局部曲率或全局信息熵准则，自适应选择富含纹理线索且判别性强的特征通道以增强原始特征。IFE即插即用且适用于多种DMIS任务，促使模型聚焦于对模糊且具有挑战性的边界识别尤为关键的纹理丰富特征，同时实现了简洁性、通用性与一定的可解释性。为评估所提IFE，我们构建了首个大规模DMIS数据集Cosmos55k，包含来自7种模态、26个解剖结构的55,023张图像。大量实验表明，IFE仅需微调即可提升经典分割网络在不同解剖结构与模态下的性能。代码见https://github.com/yezi-66/IFE