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使用全监督和弱监督语义分割对小腿/大腿磁共振成像中的肌内脂肪浸润进行定量分析。

Quantification of Intra-Muscular Adipose Infiltration in Calf/Thigh MRI Using Fully and Weakly Supervised Semantic Segmentation.

作者信息

Amer Rula, Nassar Jannette, Trabelsi Amira, Bendahan David, Greenspan Hayit, Ben-Eliezer Noam

机构信息

Department of Biomedical Engineering, Tel Aviv University, Tel Aviv 6139001, Israel.

Center for Magnetic Resonance in Biology and Medicine, Aix Marseille University, 13007 Marseille, France.

出版信息

Bioengineering (Basel). 2022 Jul 14;9(7):315. doi: 10.3390/bioengineering9070315.

Abstract

Infiltration of fat into lower limb muscles is one of the key markers for the severity of muscle pathologies. The level of fat infiltration varies in its severity across and within patients, and it is traditionally estimated using visual radiologic inspection. Precise quantification of the severity and spatial distribution of this pathological process requires accurate segmentation of lower limb anatomy into muscle and fat. Quantitative magnetic resonance imaging () of the calf and thigh muscles is one of the most effective techniques for estimating pathological accumulation of intra-muscular adipose tissue () in muscular dystrophies. In this work, we present a new deep learning () network tool for automated and robust segmentation of lower limb anatomy that is based on the quantification of MRI's transverse (T) relaxation time. The network was used to segment calf and thigh anatomies into viable muscle areas and IMAT using a weakly supervised learning process. A new disease biomarker was calculated, reflecting the level of abnormal fat infiltration and disease state. A biomarker was then applied on two patient populations suffering from dysferlinopathy and Charcot-Marie-Tooth () diseases. Comparison of manual vs. automated segmentation of muscle anatomy, viable muscle areas, and intermuscular adipose tissue (IMAT) produced high Dice similarity coefficients (DSCs) of 96.4%, 91.7%, and 93.3%, respectively. Linear regression between the biomarker value calculated based on the ground truth segmentation and based on automatic segmentation produced high correlation coefficients of 97.7% and 95.9% for the dysferlinopathy and CMT patients, respectively. Using a combination of qMRI and DL-based segmentation, we present a new quantitative biomarker of disease severity. This biomarker is automatically calculated and, most importantly, provides a spatially global indication for the state of the disease across the entire thigh or calf.

摘要

脂肪浸润到下肢肌肉是肌肉病变严重程度的关键标志物之一。脂肪浸润的程度在患者之间和患者体内的严重程度各不相同,传统上是通过视觉放射学检查来估计的。对这一病理过程的严重程度和空间分布进行精确量化,需要将下肢解剖结构准确分割为肌肉和脂肪。小腿和大腿肌肉的定量磁共振成像(qMRI)是估计肌肉营养不良症中肌肉内脂肪组织(IMAT)病理积聚的最有效技术之一。在这项工作中,我们提出了一种新的深度学习(DL)网络工具,用于基于MRI横向(T)弛豫时间的量化对下肢解剖结构进行自动且稳健的分割。该网络用于通过弱监督学习过程将小腿和大腿解剖结构分割为可行的肌肉区域和IMAT。计算出一种新的疾病生物标志物,反映异常脂肪浸润水平和疾病状态。然后将该生物标志物应用于患有dysferlinopathy和Charcot-Marie-Tooth(CMT)疾病的两个患者群体。肌肉解剖结构、可行肌肉区域和肌间脂肪组织(IMAT)的手动分割与自动分割的比较分别产生了96.4%、91.7%和93.3%的高Dice相似系数(DSC)。基于地面真值分割和基于自动分割计算出的生物标志物值之间的线性回归,对于dysferlinopathy患者和CMT患者分别产生了97.7%和95.9%的高相关系数。通过结合qMRI和基于DL的分割,我们提出了一种新的疾病严重程度定量生物标志物。该生物标志物是自动计算的,最重要的是,它为整个大腿或小腿的疾病状态提供了空间全局指示。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a9f0/9312115/c12d633bb88d/bioengineering-09-00315-g001.jpg

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