Yancheng First Hospital, Affiliated Hospital of Nanjing University Medical School, 166 Yulong Road West, Tinghu District, China; The First People's Hospital of Yancheng, 166 Yulong Road West, Tinghu District, China.
Yancheng First Hospital, Affiliated Hospital of Nanjing University Medical School, 166 Yulong Road West, Tinghu District, China; The First People's Hospital of Yancheng, 166 Yulong Road West, Tinghu District, China.
Comput Methods Programs Biomed. 2021 Nov;211:106325. doi: 10.1016/j.cmpb.2021.106325. Epub 2021 Jul 31.
Magnetic resonance imaging (MRI) is gradually replacing computed tomography (CT) in the examination of bones and joints. The accurate and automatic segmentation of the bone structure in the MRI of the shoulder joint is essential for the measurement and diagnosis of bone injuries and diseases. The existing bone segmentation algorithms cannot achieve automatic segmentation without any prior knowledge, and their versatility and accuracy are relatively low. For this reason, an automatic segmentation algorithm based on the combination of image blocks and convolutional neural networks is proposed.
First, we establish 4 segmentation models, including 3 U-Net-based bone segmentation models (humeral segmentation model, joint bone segmentation model, humeral head and articular bone segmentation model as a whole) and a block-based Alex Net segmentation model; Then we use 4 segmentation models to obtain the candidate bone area, and accurately detect the location area of the humerus and joint bone by voting. Finally, the Alex Net segmentation model is further used in the detected bone area to segment the bone edge with the accuracy of the pixel level.
The experimental data is obtained from 8 groups of patients in the orthopedics department of our hospital. Each group of scan sequence includes about 100 images, which have been segmented and labeled. Five groups of patients were used for training and five-fold cross-validation, and three groups of patients were used to test the actual segmentation effect. The average accuracy of Dice Coefficient, Positive Predicted Value (PPV) and Sensitivity reached 0.91 ± 0.02, respectively. 0.95 ± 0.03 and 0.95 ± 0.02.
The method in this paper is for a small sample of patient data sets, and only through deep learning on 2D medical images, very accurate shoulder joint segmentation results can be obtained, provide clinical diagnostic guidance to orthopedics. At the same time, the proposed algorithm framework has a certain versatility and is suitable for the precise segmentation of specific organs and tissues in MRI based on a small sample data.
磁共振成像（MRI）在骨骼和关节检查中逐渐取代计算机断层扫描（CT）。肩关节 MRI 中骨骼结构的准确和自动分割对于骨骼损伤和疾病的测量和诊断至关重要。现有的骨骼分割算法无法在没有任何先验知识的情况下实现自动分割，其通用性和准确性相对较低。为此，提出了一种基于图像块和卷积神经网络相结合的自动分割算法。
首先，我们建立了 4 个分割模型，包括 3 个基于 U-Net 的骨骼分割模型（肱骨分割模型、关节骨分割模型、肱骨头和关节骨整体分割模型）和一个基于图像块的 Alex Net 分割模型；然后，我们使用 4 个分割模型获得候选骨骼区域，并通过投票准确检测肱骨和关节骨的位置区域。最后，在检测到的骨骼区域中进一步使用 Alex Net 分割模型，以像素级精度分割骨骼边缘。
实验数据来自我院骨科的 8 组患者。每组扫描序列包含约 100 张图像，已进行分割和标记。其中 5 组患者用于训练和五折交叉验证，3 组患者用于测试实际分割效果。Dice 系数、阳性预测值（PPV）和敏感度的平均准确率分别达到 0.91±0.02、0.95±0.03 和 0.95±0.02。
本文方法针对小样本患者数据集，仅通过对 2D 医学图像进行深度学习，即可获得非常准确的肩关节分割结果，为骨科提供临床诊断指导。同时，所提出的算法框架具有一定的通用性，适用于基于小样本数据的 MRI 中特定器官和组织的精确分割。
