面向三维医学分割的置信度感知师生蒸馏

中文摘要 AI 辅助

医学图像分割模型通常依赖大量密集标注的体数据，这限制了其在不同任务和成像模态上的可扩展性。本研究针对从极端稀疏标注中预测完整三维解剖结构这一挑战。我们提出了一种标注高效的师生框架，用于自动三维医学分割，该框架仅需每个体数据中单个二维切片上的一组点提示作为输入。一个基础模型作为离线教师，利用所选切片提供的点提示生成全体积的伪标注及其对应的空间置信度分数，用于学生训练之前。为了减轻噪声伪标注的错误传播，我们使用一种置信度感知的优化策略训练一个任务特定的三维学生网络。通过利用教师预先计算的置信度分数，该策略明确地将伪标注中静态不确定的区域从损失计算中排除，同时强调置信度较高的区域。在三维心脏MRI数据集上的评估表明，我们的框架优于最先进的半监督方法，分割性能提升高达43.6%。此外，它将手动标注负担大幅减少到每个体数据仅需几个阳性点提示，同时相对于教师将表面边界精度提升高达14.7%，并成功恢复了与全监督上限性能差距的34.1%。

英文摘要

Medical image segmentation models typically rely on large amounts of densely annotated volumetric data, limiting their scalability across tasks and imaging modalities. This work addresses the challenge of predicting entire 3D anatomical structures from extreme annotation sparsity. An annotation-efficient student-teacher framework is proposed for automatic 3D medical segmentation that requires only a set of point prompts on a single 2D slice per volume, as input. A foundation model serves as an offline teacher, utilizing the provided point prompts from the selected slice to full-volume pseudo-annotations alongside their corresponding spatial confidence scores prior to student training. To mitigate the error propagation of noisy pseudo-annotations, a task-specific 3D student network is trained using a confidence-aware optimization strategy. By leveraging the teacher's pre-computed confidence scores, this strategy explicitly excludes statically uncertain regions of the pseudo-annotations from the loss calculation, while simultaneously emphasizing regions with higher confidence. Evaluated on 3D cardiac MRI datasets, our framework outperforms state-of-the-art semi-supervised methods, improving segmentation performance by up to 43.6%. Furthermore, it drastically reduces the manual annotation burden to just a few positive point prompts per volume, while improving surface boundary precision by up to 14.7% over the teacher and successfully recovering up to 34.1% of the performance gap toward the fully supervised upper bound.

发表机构

• University of Thessaly（色萨利大学）

机构由 AI 辅助整理，请以论文原文为准。