Deep convolutional neural network for segmentation of knee joint anatomy

Magnetic Resonance in Medicine : Official Journal of the Society of Magnetic Resonance in Medicine
Zhaoye ZhouFang Liu

Abstract

To describe and evaluate a new segmentation method using deep convolutional neural network (CNN), 3D fully connected conditional random field (CRF), and 3D simplex deformable modeling to improve the efficiency and accuracy of knee joint tissue segmentation. A segmentation pipeline was built by combining a semantic segmentation CNN, 3D fully connected CRF, and 3D simplex deformable modeling. A convolutional encoder-decoder network was designed as the core of the segmentation method to perform high resolution pixel-wise multi-class tissue classification for 12 different joint structures. The 3D fully connected CRF was applied to regularize contextual relationship among voxels within the same tissue class and between different classes. The 3D simplex deformable modeling refined the output from 3D CRF to preserve the overall shape and maintain a desirable smooth surface for joint structures. The method was evaluated on 3D fast spin-echo (3D-FSE) MR image data sets. Quantitative morphological metrics were used to evaluate the accuracy and robustness of the method in comparison to the ground truth data. The proposed segmentation method provided good performance for segmenting all knee joint structures. There were 4 tissue types with ...Continue Reading

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Citations

Mar 13, 2019·Journal of Applied Clinical Medical Physics·Fang LiuAlan B McMillan
Mar 13, 2019·Magnetic Resonance in Medicine : Official Journal of the Society of Magnetic Resonance in Medicine·Fang LiuRichard Kijowski
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