Meniscus segmentation from knee MR images is an essential step in finding the most suitable implant prototype for meniscus allograft transplantation using a 3D reconstruction model from the patient’s normal meniscus. However, the segmentation of the meniscus is challenging due to its thin shape, similar intensities with nearby structures such as cruciate and collateral ligaments in the knee MR images, large shape variations among patients, and inhomogeneous intensity within the meniscus. In addition, conventional deep convolutional neural network (DCNN)-based meniscus segmentation method mainly uses a pixel-wise objective function, and thus produces rather under-segmentation results due to the small shape of the meniscus or suffers from false positives that occur around the meniscus. To overcome these limitations, we propose a two-stage DCNN that combines a 2D U-Net-based meniscus localization network with a conditional generative adversarial network-based segmentation network. To efficiently localize the meniscus region to medial and lateral meniscus and feed the localized ROIs to the segmentation network, 2D U-Net-based DCNN segments knee MR images into six classes. To segment the medial and lateral meniscus while preventing under-segmentation due to intensity inhomogeneity within the meniscus and over-segmentation due to intensity similarity with surrounding structures, adversarial learning is performed repeatedly on the localized meniscus ROIs. The average DSC of the meniscus was 84.06% at the medial meniscus, and 83.19% at the lateral meniscus, respectively. These results showed that the proposed method prevented the meniscus from being over- and under-segmented by repeatedly judging and complementing the quality of segmentation results through adversarial learning.
We propose an automatic segmentation method of meniscus using cascaded segmentation network consisting of 2D and 3D convolutional neural networks and 2D conditional random fields in knee MR images. First, 2D segmentation network and 2D conditional random fields are performed to narrow the field of view of the medial and lateral meniscus. Second, 3D segmentation network considering local and spatial information is performed to segment the medial and lateral meniscus. The 2D segmentation network showed under-segmentation inside the meniscus. The under-segmentation was prevented after 2D CRF, but over-segmentation occurred in nearby ligaments with similar intensity. The 3D segmentation network prevented under- and over-segmentation due to considering local and spatial information, and showed the best performance. The average dice similarity coefficients of proposed method were 92.27% and 90.27% at medial and lateral meniscus, showed better results of 4.78% and 9.96% at medial meniscus, 3.94% and 9.58% at lateral meniscus compared to the segmentation method using 2D U-Net results and combined 2D U-Net and 2D CRF, respectively. The medial meniscus shows higher accuracy than the lateral meniscus due to less leakage into the collateral ligament.
We propose an automatic segmentation of meniscus from knee MR images using multi-atlas segmentation and patchbased edge classification. To prevent registration to large tissues, meniscus is targeted using segmented bone and articular cartilage information. To segment the meniscus with large shape variations and remove leakage to the collateral ligaments robustly, meniscus is segmented using shape- and intensity-based locally-weighted voting (LWV) and patchbased edge classification. Experimental result shows that the Dice similarity coefficient of proposed method as comparison with two manually outlining results provides over 80% in average and is improved compared to LWV based on multi-atlas.
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