Automated meniscus segmentation and tear detection of knee MRI with a 3D mask-RCNN

作者：Yuanzhe Li, Yi Wang, Kaibin Fang, Huizhong Zheng, Qingquan Lai, Yong-Fa Xia, Jia-Yang Chen, Zhangsheng Dai · 发表于：European journal of medical research · 年份：2022 · DOI：10.1186/s40001-022-00883-w · 被引用次数：30 · 研究领域：Knee injuries and reconstruction techniques、Osteoarthritis Treatment and Mechanisms、Total Knee Arthroplasty Outcomes

BACKGROUND: The diagnostic results of magnetic resonance imaging (MRI) are essential references for arthroscopy as an invasive procedure. A deviation between medical imaging diagnosis and arthroscopy results may cause irreversible damage to patients and lead to excessive medical treatment. To improve the accurate diagnosis of meniscus injury, it is urgent to develop auxiliary diagnosis algorithms to improve the accuracy of radiological diagnosis. PURPOSE: This study aims to present a fully automatic 3D deep convolutional neural network (DCNN) for meniscus segmentation and detects arthroscopically proven meniscus tears. MATERIALS AND METHODS: Our institution retrospectively included 533 patients with 546 knees who underwent knee magnetic resonance imaging (MRI) and knee arthroscopy. Sagittal proton density-weighted (PDW) images in MRI of 382 knees were regarded as a training set to train our 3D-Mask RCNN. The remaining data from 164 knees were used to validate the trained network as a test set. The masks were hand-drawn by an experienced radiologist, and the reference standard is arthroscopic surgical reports. The performance statistics included Dice accuracy, sensitivity, specificity, FROC, receiver operating characteristic (ROC) curve analysis, and bootstrap test statistics. The segmentation performance was compared with a 3D-Unet, and the detection performance was compared with radiological evaluation by two experienced musculoskeletal radiologists without knowledge of the ...