Deep Depthwise Residual Network for Knee Meniscus Segmentation From Magnetic Resonance Imaging

Abstract

The menisci within the knee are essential for various anatomical functions, including load-bearing, joint stability, cartilage protection, shock absorption, and lubrication. Magnetic resonance imaging (MRI) provides highly detailed images of internal organs and soft tissues, which are indispensable for physicians and radiologists assessing the meniscus. Given the multitude of images in each MRI sequence and diverse MRI data, the segmentation of the meniscus presents considerable challenges through image processing methods. The region-specific characteristics of the meniscus can vary from one image to another within the sequence. Consequently, achieving automatic and accurate segmentation of meniscus in knee MRI images is a crucial step in meniscus analysis. This paper introduces the “UNet with depthwise residual network” (DR-UNet), a depthwise convolutional neural network, designed specifically for meniscus segmentation in MRI images. The proposed architecture significantly improves the accuracy of meniscus segmentation compared to different segmentation networks. The training and testing phases utilized fat suppression turbo-spin-echo (FS TSE) MRI sequences collected from 100 distinct knee joints using a Siemens 3 Tesla MRI machine. Additionally, we employed data augmentation techniques to expand the dataset strategically, addressing the challenge of a substantial training dataset requirement. The DR-UNet model demonstrated impressive meniscus segmentation performance, achieving a Dice similarity coefficient range of 0.743–0.9646 and a Jaccard index range of 0.653–0.869, thereby showcasing its advanced segmentation capabilities.