Automatic meniscus segmentation using cascaded deep convolutional neural networks with 2D conditional random fields in knee MR images

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.