Laboratory of Image Science and Technology, School of Computer Science and Engineering, Southeast University, Nanjing, 210096, China.
Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing, School of Computer Science and Engineering, Southeast University, Nanjing, 210096, China.
Int J Comput Assist Radiol Surg. 2022 Jun;17(6):1115-1124. doi: 10.1007/s11548-022-02607-1. Epub 2022 Apr 6.
Clinical rib fracture diagnosis via computed tomography (CT) screening has attracted much attention in recent years. However, automated and accurate segmentation solutions remain a challenging task due to the large sets of 3D CT data to deal with. Down-sampling is often required to face computer constraints, but the performance of the segmentation may decrease in this case.
A new multi-angle projection network (MAPNet) method is proposed for accurately segmenting rib fractures by means of a deep learning approach. The proposed method incorporates multi-angle projection images to complementarily and comprehensively extract the rib characteristics using a rib extraction (RE) module and the fracture features using a fracture segmentation (FS) module. A multi-angle projection fusion (MPF) module is designed for fusing multi-angle spatial features. RESULTS: It is shown that MAPNet can capture more detailed rib fracture features than some commonly used segmentation networks. Our method achieves a better performance in accuracy (88.06 ± 6.97%), sensitivity (89.26 ± 5.69%), specificity (87.58% ± 7.66%) and in terms of classical criteria like dice (85.41 ± 3.35%), intersection over union (IoU, 80.37 ± 4.63%), and Hausdorff distance (HD, 4.34 ± 3.1).
We propose a rib fracture segmentation technique to deal with the problem of automatic fracture diagnosis. The proposed method avoids the down-sampling of 3D CT data through a projection technique. Experimental results show that it has excellent potential for clinical applications.
近年来，通过计算机断层扫描（CT）筛查对临床肋骨骨折的诊断引起了广泛关注。然而，由于要处理大量的 3D CT 数据，自动化和准确的分割解决方案仍然是一项具有挑战性的任务。为了应对计算机的限制，通常需要进行下采样，但在这种情况下，分割的性能可能会下降。
提出了一种新的多角度投影网络（MAPNet）方法，通过深度学习方法准确分割肋骨骨折。该方法采用多角度投影图像，通过肋骨提取（RE）模块和骨折分割（FS）模块互补和全面地提取肋骨特征和骨折特征。设计了一个多角度投影融合（MPF）模块，用于融合多角度空间特征。
结果表明，MAPNet 可以比一些常用的分割网络更好地捕捉肋骨骨折的详细特征。与其他方法相比，我们的方法在准确性（88.06±6.97%）、敏感度（89.26±5.69%）、特异性（87.58%±7.66%）以及传统指标（如骰子系数（85.41±3.35%）、交并比（IoU，80.37±4.63%）和 Hausdorff 距离（HD，4.34±3.1））方面都表现出了更好的性能。
我们提出了一种肋骨骨折分割技术来解决自动骨折诊断的问题。该方法通过投影技术避免了 3D CT 数据的下采样。实验结果表明，它具有很好的临床应用潜力。
