通过专家在环的掩码条件渐进式学习实现类专家的骨超声分割

中文摘要 AI 辅助

手动标注仍是超声（US）骨分割的主要瓶颈，专家通常会迭代地优化粗糙的画笔掩码，而非一次完成精确轮廓描绘。我们提出ExiL，一种掩码条件渐进式学习框架，将标注建模为结构化优化轨迹。ExiL结合了基于符号距离场的合成类专家画笔模拟器，与轻量级780万参数U-Net，该网络学习从超声图像中完成并优化不完美掩码。部署期间，专家模式会根据已接受的优化直接更新模型，实现对专家行为的持续适应。使用UltraBones100k尸体数据进行定量分割评估，并用前瞻性志愿者数据集进行标注效率分析：ExiL将单专家平均标注时间从每帧60秒降至20秒（降幅66.7%），相比非渐进式训练，平均Dice提升约0.045；在最佳轨迹感知设置下，达到0.87的Dice和2.7像素的边界误差。推理耗时10至50毫秒，ExiL可在实际临床标注中为超声引导骨科工作流实现实时、自优化的标注。

英文摘要

Manual annotation remains a major bottleneck in ultrasound (US) bone segmentation, where experts typically iteratively refine rough brush masks rather than delineating precise contours in a single pass. We present ExiL, a mask-conditioned progressive learning framework that models annotation as a structured refinement trajectory. ExiL combines a synthetic expert-like brush simulator based on signed distance fields with a lightweight 7.8M-parameter U-Net that learns to complete and refine imperfect masks from US images. During deployment, an expert mode updates the model directly from accepted refinements, enabling continual adaptation to expert behavior. Evaluated using UltraBones100k cadaver data for quantitative segmentation and a prospective volunteer dataset for annotation-efficiency analysis, ExiL reduced single-expert average annotation time from 60 to 20 seconds per frame (66.7\%) and improved mean Dice by approximately 0.045 over non-progressive training, while achieving 0.87 Dice and 2.7 px boundary error in the best trajectory-aware setting. With 10--50 ms inference, ExiL enables real-time, self-improving annotation for US-guided orthopedic workflows in practical clinical labeling.

发表机构

• McGill University（麦吉尔大学）
• Research Institute of the McGill University Health Centre（麦吉尔大学健康中心研究所）

机构由 AI 辅助整理，请以论文原文为准。