Deep learning-based medical image segmentation methods have demonstrated significant clinical applications. However, training these methods on small-sample vascular datasets remains challenging due to the scarcity of labeled data and severe category imbalance. To address this, this paper proposes Mid-Net, which fully exploits the often-overlooked feature representation potential of the middle-layer network through cross-layer guidance to improve model learning efficiency in data-constrained environments. Mid-Net consists of three core components: the encoding path, the guidance path, and the calibration path. In the encoding path, a feature pyramid structure with large kernel convolutions is used to extract semantic information at different scales. The guidance path combines the sensitivity of the shallow-layer network to spatial details with the global perceptual abilities of the deep-layer network to provide more discriminative guidance to the middle-layer network in a feature-decoupled manner. The calibration path further calibrates the spatial location information of the middle-layer network through end-to-end supervised learning. Experiments conducted on the publicly available retinal vascular datasets DRIVE, STARE, and CHASE_DB1, as well as coronary angiography datasets DCA1 and CHUAC, demonstrate that Mid-Net achieves superior segmentation results with lower computational resource requirements compared to state-of-the-art methods.

中文翻译：

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Mid-Net：重新思考用于小样本血管分割的高效网络架构


基于深度学习的医学图像分割方法已显示出重要的临床应用。然而，由于标记数据的稀缺性和严重的类别不平衡，在小样本血管数据集上训练这些方法仍然具有挑战性。为了解决这个问题，本文提出了 Mid-Net，它通过跨层指导，充分利用中间层网络经常被忽视的特征表示潜力，以提高数据受限环境中的模型学习效率。Mid-Net 由三个核心组件组成：编码路径、引导路径和校准路径。在编码路径中，使用具有大核卷积的特征金字塔结构来提取不同尺度的语义信息。引导路径将浅层网络对空间细节的敏感性与深层网络的全局感知能力相结合，以特征解耦的方式为中间层网络提供更具辨别力的指导。校准路径通过端到端的监督学习进一步校准中间层网络的空间位置信息。在公开可用的视网膜血管数据集 DRIVE、STARE 和 CHASE_DB1以及冠状动脉造影数据集 DCA1 和 CHUAC 上进行的实验表明，与最先进的方法相比，Mid-Net 以较低的计算资源要求获得了卓越的分割结果。