自动检测胸腰椎残余肋骨并量化其形态特征，提升诊断准确性。
Automated Thoracolumbar Stump Rib Detection and Analysis in a Large CT Cohort
- 基于高分辨率深度学习模型实现肋骨精准分割，Dice分数达0.997。
- 残余肋骨更靠后、更细且向下倾斜，与正常肋骨差异显著（p<0.01）。
- 适用于医学影像分析人员及脊柱畸形研究者，支持公开模型复用。
胸腰椎残余肋骨是胸腰椎过渡椎或计数异常的重要指标。现有研究多依赖人工评估并定性描述，本研究旨在实现残余肋骨的自动化检测与定量形态分析。我们训练了一个高分辨率深度学习模型用于肋骨分割，在性能上显著优于现有模型（Dice分数0.997对比0.779，p<0.01）。同时，采用迭代算法与分段线性插值评估肋骨长度，成功率达98.2%。形态学分析显示，残余肋骨在椎体上附着点更靠后（-19.2±3.8 对比 -13.8±2.5，p<0.01），更纤细（260.6±103.4 对比 563.6±127.1，p<0.01），且在近端1厘米内更向下和侧向倾斜。针对部分可见肋骨，该特征组合可实现F1分数0.84的区分效果。模型权重与标注掩码已公开发布。
原文摘要 · Abstract (English)
Thoracolumbar stump ribs are one of the essential indicators of thoracolumbar transitional vertebrae or enumeration anomalies. While some studies manually assess these anomalies and describe the ribs qualitatively, this study aims to automate thoracolumbar stump rib detection and analyze their morphology quantitatively. To this end, we train a high-resolution deep-learning model for rib segmentation and show significant improvements compared to existing models (Dice score 0.997 vs. 0.779, p-value < 0.01). In addition, we use an iterative algorithm and piece-wise linear interpolation to assess the length of the ribs, showing a success rate of 98.2%. When analyzing morphological features, we show that stump ribs articulate more posteriorly at the vertebrae (-19.2 +- 3.8 vs -13.8 +- 2.5, p-value < 0.01), are thinner (260.6 +- 103.4 vs. 563.6 +- 127.1, p-value < 0.01), and are oriented more downwards and sideways within the first centimeters in contrast to full-length ribs. We show that with partially visible ribs, these features can achieve an F1-score of 0.84 in differentiating stump ribs from regular ones. We publish the model weights and masks for public use.
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