CENet提升医学图像分割边界精度，专治器官形态多变与信息丢失问题。
CENet: Context Enhancement Network for Medical Image Segmentation
- 通过双选择增强块和上下文特征注意力模块，增强边界与小器官检测能力。
- 在多个影像数据集上达到最优性能，边界分割准确率显著优于现有方法。
- 适合复杂医学图像分析场景，尤其对多器官、小结构分割需求高。
医学图像分割在多领域场景下需精准保留解剖结构，但现有深度学习模型常面临边界表示不准确、器官形态变化大及下采样过程中的信息丢失问题，限制了其精度与鲁棒性。为此，我们提出上下文增强网络（CENet），包含两项关键创新：一是将双选择增强块（DSEB）嵌入跳跃连接，以情境感知方式提升边界细节并改善小器官检测；二是解码器中引入上下文特征注意力模块（CFAM），采用多尺度设计以保持空间完整性、减少特征冗余并缓解过度增强。在放射科与皮肤镜数据集上的广泛评估表明，CENet在多器官分割与边界细节保留方面均超越当前最优（SOTA）方法，为复杂医学图像分析提供了鲁棒且精确的解决方案。代码已公开于https://github.com/xmindflow/cenet。
原文摘要 · Abstract (English)
Medical image segmentation, particularly in multi-domain scenarios, requires precise preservation of anatomical structures across diverse representations. While deep learning has advanced this field, existing models often struggle with accurate boundary representation, variability in organ morphology, and information loss during downsampling, limiting their accuracy and robustness. To address these challenges, we propose the Context Enhancement Network (CENet), a novel segmentation framework featuring two key innovations. First, the Dual Selective Enhancement Block (DSEB) integrated into skip connections enhances boundary details and improves the detection of smaller organs in a context-aware manner. Second, the Context Feature Attention Module (CFAM) in the decoder employs a multi-scale design to maintain spatial integrity, reduce feature redundancy, and mitigate overly enhanced representations. Extensive evaluations on both radiology and dermoscopic datasets demonstrate that CENet outperforms state-of-the-art (SOTA) methods in multi-organ segmentation and boundary detail preservation, offering a robust and accurate solution for complex medical image analysis tasks. The code is publicly available at https://github.com/xmindflow/cenet.
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