Abstract：
OBJECTIVE: To develop and validate an open-source deep learning model for automatically quantifying scapular and glenoid morphology using CT images of normal subjects and patients with glenohumeral osteoarthritis.
METHODS: First, we used deep learning to segment the scapula from CT images and then to identify the location of 13 landmarks on the scapula, 9 of them to establish a coordinate system unaffected by osteoarthritis-related changes, and the remaining 4 landmarks on the glenoid cavity to determine the glenoid size and orientation in this scapular coordinate system. The glenoid version, glenoid inclination, critical shoulder angle, glenopolar angle, glenoid height, and glenoid width were subsequently measured in this coordinate system. A 5-fold cross-validation was performed to evaluate the performance of this approach on 60 normal/non-osteoarthritic and 56 pathological/osteoarthritic scapulae.
RESULTS: The Dice similarity coefficient between manual and automatic scapular segmentations exceeded 0.97 in both normal and pathological cases. The average error in automatic scapular and glenoid landmark positioning ranged between 1 and 2.5 mm and was comparable between the automatic method and human raters. The automatic method provided acceptable estimates of glenoid version (R2 = 0.95), glenoid inclination (R2 = 0.93), critical shoulder angle (R2 = 0.95), glenopolar angle (R2 = 0.90), glenoid height (R2 = 0.88) and width (R2 = 0.94). However, a significant difference was found for glenoid inclination between manual and automatic measurements (p < 0.001).
CONCLUSIONS: This open-source deep learning model enables the automatic quantification of scapular and glenoid morphology from CT scans of patients with glenohumeral osteoarthritis, with sufficient accuracy for clinical use.
METHODS: First, we used deep learning to segment the scapula from CT images and then to identify the location of 13 landmarks on the scapula, 9 of them to establish a coordinate system unaffected by osteoarthritis-related changes, and the remaining 4 landmarks on the glenoid cavity to determine the glenoid size and orientation in this scapular coordinate system. The glenoid version, glenoid inclination, critical shoulder angle, glenopolar angle, glenoid height, and glenoid width were subsequently measured in this coordinate system. A 5-fold cross-validation was performed to evaluate the performance of this approach on 60 normal/non-osteoarthritic and 56 pathological/osteoarthritic scapulae.
RESULTS: The Dice similarity coefficient between manual and automatic scapular segmentations exceeded 0.97 in both normal and pathological cases. The average error in automatic scapular and glenoid landmark positioning ranged between 1 and 2.5 mm and was comparable between the automatic method and human raters. The automatic method provided acceptable estimates of glenoid version (R2 = 0.95), glenoid inclination (R2 = 0.93), critical shoulder angle (R2 = 0.95), glenopolar angle (R2 = 0.90), glenoid height (R2 = 0.88) and width (R2 = 0.94). However, a significant difference was found for glenoid inclination between manual and automatic measurements (p < 0.001).
CONCLUSIONS: This open-source deep learning model enables the automatic quantification of scapular and glenoid morphology from CT scans of patients with glenohumeral osteoarthritis, with sufficient accuracy for clinical use.
摘要：
目的：开发并验证一种开源深度学习模型，用于使用正常人和肱骨关节炎患者的CT图像自动量化肩胛骨和关节盂的形态。
方法：首先，我们使用深度学习从CT图像中分割出肩胛骨，然后确定肩胛骨上13个标志的位置，其中9个建立不受骨关节炎相关变化影响的坐标系，和关节盂腔上的其余4个界标来确定关节盂在这个肩胛骨坐标系中的大小和方向。关节盂版本，关节盂倾斜,临界肩角,阴极角，关节盂高度,和关节盂宽度随后在该坐标系中测量。进行了5倍交叉验证，以评估该方法在60例正常/非骨关节炎和56例病理/骨关节炎肩胛骨上的性能。
结果：在正常和病理病例中，手动和自动肩胛骨分割之间的Dice相似系数均超过0.97。自动肩胛骨和关节盂界标定位的平均误差在1到2.5mm之间，并且在自动方法和人类评估者之间具有可比性。自动方法提供了关节盂版本的可接受估计（R2=0.95），关节盂倾角(R2=0.93),临界肩角(R2=0.95),阴极角（R2=0.90），关节盂高度(R2=0.88)和宽度(R2=0.94)。然而,人工和自动测量的关节盂倾角差异显著(p<0.001).
结论：这种开源深度学习模型能够自动量化肩胛骨关节炎患者CT扫描的肩胛骨和关节盂形态，具有足够的临床使用精度。
方法：首先，我们使用深度学习从CT图像中分割出肩胛骨，然后确定肩胛骨上13个标志的位置，其中9个建立不受骨关节炎相关变化影响的坐标系，和关节盂腔上的其余4个界标来确定关节盂在这个肩胛骨坐标系中的大小和方向。关节盂版本，关节盂倾斜,临界肩角,阴极角，关节盂高度,和关节盂宽度随后在该坐标系中测量。进行了5倍交叉验证，以评估该方法在60例正常/非骨关节炎和56例病理/骨关节炎肩胛骨上的性能。
结果：在正常和病理病例中，手动和自动肩胛骨分割之间的Dice相似系数均超过0.97。自动肩胛骨和关节盂界标定位的平均误差在1到2.5mm之间，并且在自动方法和人类评估者之间具有可比性。自动方法提供了关节盂版本的可接受估计（R2=0.95），关节盂倾角(R2=0.93),临界肩角(R2=0.95),阴极角（R2=0.90），关节盂高度(R2=0.88)和宽度(R2=0.94)。然而,人工和自动测量的关节盂倾角差异显著(p<0.001).
结论：这种开源深度学习模型能够自动量化肩胛骨关节炎患者CT扫描的肩胛骨和关节盂形态，具有足够的临床使用精度。
