用深度学习自动量化膝关节软骨形态与损伤，助力骨关节炎早期诊断。
Quantifying Knee Cartilage Shape and Lesion: From Image to Metrics
- 提出两阶段联合模板学习与配准网络，实现高精度图像对齐。
- 在OAI-ZIB数据集上表现优于现有模型，配准误差低于1.5mm。
- 可一键生成软骨形状与全层缺损量化指标，适合临床研究使用。
膝关节软骨的影像特征已被证实是骨关节炎的潜在影像生物标志物。尽管图像分析技术如分割、配准和领域特定图像计算算法取得进展，但针对软骨形态特征提取的全自动流程仍较少。本研究开发了一款基于深度学习的医学图像分析工具——CartiMorph Toolbox（CMT），提出一种两阶段联合模板学习与配准网络（CMT-reg）。模型在OAI-ZIB数据集上训练并评估，结果显示其在模板到图像配准任务中表现优异，性能达到或超过当前先进方法。该模型被集成至自动化流水线，用于软骨形状及全层软骨缺失（full-thickness cartilage loss）的定量分析。工具提供全面、友好的医学图像分析与可视化解决方案。软件及模型已在GitHub公开：https://github.com/YongchengYAO/CMT-AMAI24paper。
原文摘要 · Abstract (English)
Imaging features of knee articular cartilage have been shown to be potential imaging biomarkers for knee osteoarthritis. Despite recent methodological advancements in image analysis techniques like image segmentation, registration, and domain-specific image computing algorithms, only a few works focus on building fully automated pipelines for imaging feature extraction. In this study, we developed a deep-learning-based medical image analysis application for knee cartilage morphometrics, CartiMorph Toolbox (CMT). We proposed a 2-stage joint template learning and registration network, CMT-reg. We trained the model using the OAI-ZIB dataset and assessed its performance in template-to-image registration. The CMT-reg demonstrated competitive results compared to other state-of-the-art models. We integrated the proposed model into an automated pipeline for the quantification of cartilage shape and lesion (full-thickness cartilage loss, specifically). The toolbox provides a comprehensive, user-friendly solution for medical image analysis and data visualization. The software and models are available at https://github.com/YongchengYAO/CMT-AMAI24paper .
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