Background and objective

Calcaneus is the largest tarsal bone to withstand the daily stresses of weight-bearing. The calcaneal fracture is the most common type in the tarsal bone fractures. After a fracture is suspected, plain radiographs should be taken first. Bohler's Angle (BA) and Critical Angle of Gissane (CAG), measured by four anatomic landmarks in lateral foot radiograph, can guide fracture diagnosis and facilitate operative recovery of the fractured calcaneus. This study aims to develop an analysis system that can automatically locate four anatomic landmarks, measure BA and CAG for fracture assessment, identify fractured calcaneus, and segment fractured regions.

Methods

For landmark detection, we proposed a coarse-to-fine Rotation-Invariant Regression-Voting (RIRV) landmark detection method based on regressive Multi-Layer Perceptron (MLP) and Scale Invariant Feature Transform (SIFT) patch descriptor, which solves the problem of fickle rotation of calcaneus. By implementing a novel normalization approach, the RIRV method is explicitly rotation-invariance comparing with traditional regressive methods. For fracture identification and segmentation, a convolution neural network (CNN) based on U-Net with auxiliary classification head (U-Net-CH) is designed. The input ROIs of the CNN are normalized by detected landmarks to uniform view, orientation, and scale. The advantage of this approach is the multi-task learning that combines classification and segmentation.

Results

Our system can accurately measure BA and CAG with a mean angle error of 3.8^○ and 6.2^○ respectively. For fracture identification and fracture region segmentation, our system presents good performance with an F1-score of 96.55%, recall of 94.99%, and segmentation IoU-score of 0.586.

Conclusion

A powerful calcaneal radiograph analysis system including anatomical angles measurement, fracture identification, and fracture segmentation can be built. The proposed analysis system can aid orthopedists to improve the efficiency and accuracy of calcaneus fracture diagnosis.

中文翻译：

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跟骨X线照片自动分析系统：旋转固定界标检测，用于跟骨角度测量，骨折识别和骨折区域分割

背景和目标

跟骨是最大的骨，可以承受日常的负重压力。跟骨骨折是骨骨折中最常见的类型。怀疑有骨折后，应首先拍摄X光平片。通过外侧足部X射线照相中的四个解剖学标志来测量的布勒角（BA）和吉萨涅临界角（CAG）可以指导骨折诊断并促进骨折跟骨的手术恢复。这项研究旨在开发一种分析系统，该系统可以自动定位四个解剖标志，测量BA和CAG以进行骨折评估，识别跟骨骨折和分割骨折区域。

方法

对于地标检测，我们提出了一种基于回归多层感知器（MLP）和尺度不变特征变换（SIFT）补丁描述符的从细到细旋转不变投票（RIRV）地标检测方法，解决了该问题。跟骨多变。通过实施一种新颖的归一化方法，与传统的回归方法相比，RIRV方法显式地具有旋转不变性。为了进行裂缝识别和分割，设计了基于U-Net和辅助分类头（U-Net-CH）的卷积神经网络（CNN）。CNN的输入ROI通过检测到的界标进行标准化，以实现统一的视图，方向和比例。这种方法的优点是结合了分类和细分的多任务学习。

结果

我们的系统可以准确地测量BA和CAG，平均角度误差分别为3.8 ^○和6.2 ^○。对于骨折识别和骨折区域分割，我们的系统表现出良好的性能，F1分数为96.55％，召回率为94.99％，分割IoU分数为0.586。

结论

可以构建一个功能强大的跟骨X射线照片分析系统，其中包括解剖角度测量，骨折识别和骨折分割。所提出的分析系统可以帮助骨科医生提高跟骨骨折诊断的效率和准确性。