发表机构
Istituto di Ricerche Farmacologiche Mario Negri IRCCS（马里奥·内格里药物研究所IRCCS）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对X射线显微CT中复杂结构分割标注成本高的问题，提出基于nnU-Net的弱监督方法，利用稀疏点标注辅以少量全分割图像，在大鼠肾脏肾小球分割上接近全监督性能。
AI 中文摘要
在X射线断层扫描数据中对复杂结构进行分割是生物医学研究中的一项基本任务，但该任务通常需要大量精确标注的数据，这使得全监督方法成本高昂且难以扩展。本研究探讨了弱监督深度学习作为一种减少标注工作量同时保持准确分割的策略。基于nnU-Net框架的二维卷积神经网络被调整为弱监督设置，使用稀疏点状标注，并辅以有限数量的全分割图像。该方法在高分辨率的大鼠肾脏显微CT切片上进行了评估，目标是分割肾小球，这是一种体积小、对比度低的解剖结构。结果表明，弱监督提供了有意义的学习信号，即使在缺乏密集标签的情况下也能实现对肾小球的可靠定位。加入少量高质量标注可显著提高分割性能，接近全监督模型的表现。这些发现凸显了弱监督学习作为一种标注高效策略在X射线断层扫描数据复杂结构分析中的潜力，并表明针对稀疏标注定制的替代损失函数可能进一步提升性能。
英文摘要
Segmentation of complex structures in X-ray tomographic data is a fundamental task in biomedical research, but it often requires large amounts of precisely annotated data, making fully supervised approaches costly and difficult to scale. In this study, weakly supervised deep learning is investigated as a strategy to reduce annotation effort while maintaining accurate segmentation. A two-dimensional convolutional neural network based on the nnU-Net framework was adapted to a weak supervision setting using sparse dot-based annotations, complemented by a limited number of fully segmented images. The approach was evaluated on high-resolution microCT slices of rat kidneys, targeting the segmentation of renal glomeruli, which are small, low-contrast anatomical structures. Results indicate that weak supervision provides a meaningful learning signal, enabling reliable localization of glomeruli even in the absence of dense labels. Incorporating a small set of high-quality annotations substantially improves segmentation performance, approaching that of a fully supervised model. These findings highlight the potential of weakly supervised learning as an annotation-efficient strategy for the analysis of complex structures in X-ray tomographic data, and suggest that alternative loss formulations tailored to sparse annotations may further enhance performance.
