Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA.
Department of Biomedical Engineering, University of Minnesota, Minneapolis, Minnesota, USA.
Magn Reson Med. 2018 Apr;79(4):2379-2391. doi: 10.1002/mrm.26841. Epub 2017 Jul 21.
To describe and evaluate a new fully automated musculoskeletal tissue segmentation method using deep convolutional neural network (CNN) and three-dimensional (3D) simplex deformable modeling to improve the accuracy and efficiency of cartilage and bone segmentation within the knee joint.
A fully automated segmentation pipeline was built by combining a semantic segmentation CNN and 3D simplex deformable modeling. A CNN technique called SegNet was applied as the core of the segmentation method to perform high resolution pixel-wise multi-class tissue classification. The 3D simplex deformable modeling refined the output from SegNet to preserve the overall shape and maintain a desirable smooth surface for musculoskeletal structure. The fully automated segmentation method was tested using a publicly available knee image data set to compare with currently used state-of-the-art segmentation methods. The fully automated method was also evaluated on two different data sets, which include morphological and quantitative MR images with different tissue contrasts.
The proposed fully automated segmentation method provided good segmentation performance with segmentation accuracy superior to most of state-of-the-art methods in the publicly available knee image data set. The method also demonstrated versatile segmentation performance on both morphological and quantitative musculoskeletal MR images with different tissue contrasts and spatial resolutions.
The study demonstrates that the combined CNN and 3D deformable modeling approach is useful for performing rapid and accurate cartilage and bone segmentation within the knee joint. The CNN has promising potential applications in musculoskeletal imaging. Magn Reson Med 79:2379-2391, 2018. © 2017 International Society for Magnetic Resonance in Medicine.
描述并评估一种新的全自动化肌肉骨骼组织分割方法，该方法使用深度卷积神经网络（CNN）和三维（3D）单纯形变形建模，以提高膝关节内软骨和骨分割的准确性和效率。
通过结合语义分割 CNN 和 3D 单纯形变形建模，构建了一个全自动化的分割流水线。将一种名为 SegNet 的 CNN 技术用作分割方法的核心，以执行高分辨率像素级多类组织分类。3D 单纯形变形建模细化了 SegNet 的输出结果，以保持整体形状并为肌肉骨骼结构保持理想的平滑表面。使用公开的膝关节图像数据集测试了全自动分割方法，并与当前使用的最先进的分割方法进行了比较。该全自动方法还在两个不同的数据集上进行了评估，这些数据集包括具有不同组织对比度的形态学和定量 MR 图像。
所提出的全自动分割方法在公开膝关节图像数据集中的分割精度优于大多数最先进方法，具有良好的分割性能。该方法还在具有不同组织对比度和空间分辨率的形态学和定量肌肉骨骼 MR 图像上展示了多功能的分割性能。
该研究表明，CNN 和 3D 变形建模方法的结合对于在膝关节内进行快速准确的软骨和骨分割是有用的。CNN 在肌肉骨骼成像中有很有前途的应用。磁共振医学杂志 79:2379-2391, 2018。© 2017 国际磁共振学会。
