重新思考医学图像分割深度学习模型中的边界检测
Rethinking Boundary Detection in Deep Learning Models for Medical Image Segmentation
AI总结：
针对医学图像分割任务，提出结合CNN、ViT与显式边界检测算子的CTO架构，通过边界引导解码器增强边界学习，在6个数据集上以有竞争力的复杂度实现SOTA精度。
摘要：
医学图像分割是医学图像分析领域的一项基础任务。本文提出了一种名为Convolution, Transformer, and Operator（CTO，卷积、Transformer与算子）的新型网络架构。CTO结合了Convolutional Neural Networks（CNNs，卷积神经网络）、Vision Transformer（ViT，视觉Transformer）和显式边界检测算子，在实现高识别精度的同时，还能在精度与效率之间保持最优平衡。所提出的CTO遵循标准的编码器-解码器分割范式，其中编码器网络采用主流CNN骨干网络来捕获局部语义信息，同时搭配轻量级ViT辅助模块来整合长距离依赖关系。为增强边界学习能力，本文提出了边界引导解码器网络，该网络利用专用边界检测算子得到的边界掩码作为显式监督，来指导解码学习过程。研究在6个具有挑战性的医学图像分割数据集上评估了所提方法的性能，结果表明CTO以极具竞争力的模型复杂度实现了当前最优（state-of-the-art）精度。
英文摘要：
Medical image segmentation is a fundamental task in the community of medical image analysis. In this paper, a novel network architecture, referred to as Convolution, Transformer, and Operator (CTO), is proposed. CTO employs a combination of Convolutional Neural Networks (CNNs), Vision Transformer (ViT), and an explicit boundary detection operator to achieve high recognition accuracy while maintaining an optimal balance between accuracy and efficiency. The proposed CTO follows the standard encoder-decoder segmentation paradigm, where the encoder network incorporates a popular CNN backbone for capturing local semantic information, and a lightweight ViT assistant for integrating long-range dependencies. To enhance the learning capacity on boundary, a boundary-guided decoder network is proposed that uses a boundary mask obtained from a dedicated boundary detection operator as explicit supervision to guide the decoding learning process. The performance of the proposed method is evaluated on six challenging medical image segmentation datasets, demonstrating that CTO achieves state-of-the-art accuracy with a competitive model complexity.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
