School of Information, Yunnan University, East outer ring south road, Kunming, 650504, China.
Orthopedics Department, Yunnan Provincial People's Hospital: First People's Hospital of Yunnan, 157 Jinbi Road, Kunming, 650034, China.
Med Biol Eng Comput. 2022 Aug;60(8):2257-2269. doi: 10.1007/s11517-022-02563-7. Epub 2022 Jun 9.
The accuracy of the Cobb measurement is essential for the diagnosis and treatment of scoliosis. Manual measurement is however influenced by the observer variability hence affecting progression evaluation. In this paper, we propose a fully automatic Cobb measurement method to address the accuracy issue of manual measurement. We improve the U-shaped network based on the multi-scale feature fusion to segment each vertebra. To enable multi-scale feature extraction, the convolution kernel of the U-shaped network is substituted by the Inception Block. To solve the problem of gradient disappearance caused by the widening of the network structure from the Inception Block, we propose using Res Block. CBAM (Convolutional Block Attention Module) can help the network judges the importance of the feature map to modify learning weight. Also, to further enhance the accuracy of feature extraction, we add the CBAM to the U-shaped network bottleneck. Finally, based on the segmented vertebrae, the efficient automatic Cobb angle measurement method is proposed to estimate the Cobb angle. In the experiments, 75 spinal X-ray images are tested. We compare the proposed U-Shaped network with the state-of-the-art methods including DeepLabV3 + , FCN8S, SegNet, U-Net, U-Net + + , BASNet, and UNet for vertebra segmentation. Our results show that compared to these methods, the Dice coefficient is improved by 32.03%, 33.58%, 12.42%, 5.65%, 4.55%, 4.42%, and 3.27%, respectively. The CMAE of the calculated Cobb measurement is 2.45°, which is lower than the average error of 5-7° of manual measurement. The experimental results indicate that the improved U-shaped network improves the accuracy of vertebra segmentation. The proposed efficient automatic Cobb measurement method can be used in clinics to reduce observer variability.
Cobb 测量的准确性对于脊柱侧凸的诊断和治疗至关重要。然而，手动测量受到观察者变异性的影响，从而影响进展评估。在本文中，我们提出了一种全自动 Cobb 测量方法，以解决手动测量的准确性问题。我们改进了基于多尺度特征融合的 U 形网络来分割每个椎体。为了实现多尺度特征提取，U 形网络的卷积核被 Inception 块替换。为了解决因 Inception 块导致的网络结构加宽而引起的梯度消失问题，我们提出使用 Res Block。CBAM（卷积块注意力模块）可以帮助网络判断特征图的重要性，从而修改学习权重。此外，为了进一步提高特征提取的准确性，我们在 U 形网络瓶颈处添加了 CBAM。最后，基于分割的椎体，提出了有效的自动 Cobb 角测量方法来估计 Cobb 角。在实验中，测试了 75 张脊柱 X 射线图像。我们将所提出的 U 形网络与包括 DeepLabV3+、FCN8S、SegNet、U-Net、U-Net++、BASNet 和 UNet 在内的最新方法进行比较，用于椎体分割。我们的结果表明，与这些方法相比，Dice 系数分别提高了 32.03%、33.58%、12.42%、5.65%、4.55%、4.42%和 3.27%。计算的 Cobb 测量的 CMAE 为 2.45°，低于手动测量的 5-7°的平均误差。实验结果表明，改进的 U 形网络提高了椎体分割的准确性。所提出的高效自动 Cobb 测量方法可用于临床，以减少观察者变异性。
