SGTC：用于稀疏标注半监督医学图像分割的语义引导三元协同训练
SGTC: Semantic-Guided Triplet Co-training for Sparsely Annotated Semi-Supervised Medical Image Segmentation
AI总结：
针对稀疏标注下医学图像分割忽略语义特征和弱边界的问题，提出SGTC框架，通过基于CLIP的语义引导辅助机制和三视图差异训练策略，仅用三个正交切片标注实现高性能分割，显著提升鲁棒性并超越现有半监督方法。
摘要：
尽管半监督学习在医学图像分割领域取得了显著进展，但逐片完全标注体积样本仍然是一项昂贵且耗时的任务。更糟糕的是，现有大多数方法过于关注图像级信息而忽略了语义特征，导致无法感知弱边界。为了解决这些问题，我们提出了一种新颖的语义引导三元协同训练（SGTC）框架，该框架仅通过标注少数体积样本的三个正交切片即可实现高端医学图像分割，显著减轻了放射科医生的负担。我们的方法包含两个主要组件。具体而言，为了实现语义感知的细粒度分割并提高伪标签质量，基于预训练的CLIP提出了一种新颖的语义引导辅助学习机制。此外，针对更具挑战性但符合临床现实的场景，提出了一种新的三视图差异训练策略，该策略使用稀疏标注（即少数体积仅有三个标注切片）在三个子网络之间进行协同训练，显著提升了鲁棒性。在三个公开医学数据集上的大量实验表明，在稀疏标注设置下，我们的方法优于大多数最先进的半监督同类方法。源代码可在 https://github.com/xmeimeimei/SGTC 获取。
英文摘要：
Although semi-supervised learning has made significant advances in the field of medical image segmentation, fully annotating a volumetric sample slice by slice remains a costly and time-consuming task. Even worse, most of the existing approaches pay much attention to image-level information and ignore semantic features, resulting in the inability to perceive weak boundaries. To address these issues, we propose a novel Semantic-Guided Triplet Co-training (SGTC) framework, which achieves high-end medical image segmentation by only annotating three orthogonal slices of a few volumetric samples, significantly alleviating the burden of radiologists. Our method consist of two main components. Specifically, to enable semantic-aware, fine-granular segmentation and enhance the quality of pseudo-labels, a novel semantic-guided auxiliary learning mechanism is proposed based on the pretrained CLIP. In addition, focusing on a more challenging but clinically realistic scenario, a new triple-view disparity training strategy is proposed, which uses sparse annotations (i.e., only three labeled slices of a few volumes) to perform co-training between three sub-networks, significantly improving the robustness. Extensive experiments on three public medical datasets demonstrate that our method outperforms most state-of-the-art semi-supervised counterparts under sparse annotation settings. The source code is available at https://github.com/xmeimeimei/SGTC.
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。
