使用MtRA-Unet并结合形状信息从膝关节MRI分割胫股关节组织：来自骨关节炎倡议的数据
Segmentation of tibiofemoral joint tissues from knee MRI using MtRA-Unet and incorporating shape information: Data from the Osteoarthritis Initiative
AI总结：
本文提出单阶段端到端MtRA-Unet框架，通过多分辨率特征融合和形状重建损失自动分割膝关节MRI中的股骨、胫骨及软骨，在骨关节炎倡议数据上取得高Dice系数和快速分割性能。
摘要：
膝骨关节炎（KOA）是仅次于颈痛和背痛的第三大常见肌肉骨骼疾病（MSD）。为了监测这种严重的MSD，通常利用膝关节磁共振成像（MRI）的自动分割算法来获取股骨、胫骨和胫股软骨的分割图。然而，在近期的工作中，这种分割只能通过多阶段框架实现，从而造成数据处理问题，并需要持续的人工干预，使其无法进行快速、精确的临床诊断。为了解决这些问题，本文提出了多分辨率注意力U-Net（MtRA-Unet），用于自动分割股骨、胫骨和胫股软骨。所提出的工作引入了一种新颖的多分辨率特征融合（MRFF）和形状重建（SR）损失，重点关注股骨、胫骨和胫股软骨的多上下文信息和结构解剖细节。与以往方法不同，所提出的工作是一个单阶段端到端框架，在关键MRI切片上实现了股骨98.5%、胫骨98.4%、股骨软骨（FC）89.1%和胫骨软骨（TC）86.1%的Dice相似系数（DSC），这有助于临床医生进行KOA分级。每个受试者分割MRI体积（160个切片）的时间为22秒，是当前最先进方法中最快的之一。此外，对FC和TC分割的全面实验——这对于基于形态学的研究以检查KOA进展至关重要——表明所提出的方法在二值分割中产生了优异的结果。
英文摘要：
Knee Osteoarthritis (KOA) is the third most prevalent Musculoskeletal Disorder (MSD) after neck and back pain. To monitor such a severe MSD, a segmentation map of the femur, tibia and tibiofemoral cartilage is usually accessed using the automated segmentation algorithm from the Magnetic Resonance Imaging (MRI) of the knee. But, in recent works, such segmentation is conceivable only from the multistage framework thus creating data handling issues and needing continuous manual inference rendering it unable to make a quick and precise clinical diagnosis. In order to solve these issues, in this paper the Multi-Resolution Attentive-Unet (MtRA-Unet) is proposed to segment the femur, tibia and tibiofemoral cartilage automatically. The proposed work has included a novel Multi-Resolution Feature Fusion (MRFF) and Shape Reconstruction (SR) loss that focuses on multi-contextual information and structural anatomical details of the femur, tibia and tibiofemoral cartilage. Unlike previous approaches, the proposed work is a single-stage and end-to-end framework producing a Dice Similarity Coefficient (DSC) of 98.5% for the femur, 98.4% for the tibia, 89.1% for Femoral Cartilage (FC) and 86.1% for Tibial Cartilage (TC) for critical MRI slices that can be helpful to clinicians for KOA grading. The time to segment MRI volume (160 slices) per subject is 22 sec. which is one of the fastest among state-of-the-art. Moreover, comprehensive experimentation on the segmentation of FC and TC which is of utmost importance for morphology-based studies to check KOA progression reveals that the proposed method has produced an excellent result with binary segmentation
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
