基于自动受试者内配准的水脂 MRI 腹部脂肪分割。

Automatic intra-subject registration-based segmentation of abdominal fat from water-fat MRI.

机构信息

Ming Hsieh Department of Electrical Engineering, University of Southern California, Los Angeles, California 90089-2564, USA.

出版信息

J Magn Reson Imaging. 2013 Feb;37(2):423-30. doi: 10.1002/jmri.23813. Epub 2012 Sep 25.

DOI:10.1002/jmri.23813

PMID:23011805

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3531568/

Abstract

PURPOSE

To develop an automatic registration-based segmentation algorithm for measuring abdominal adipose tissue depot volumes and organ fat fraction content from three-dimensional (3D) water-fat MRI data, and to evaluate its performance against manual segmentation.

MATERIALS AND METHODS

Data were obtained from 11 subjects at two time points with intermediate repositioning, and from four subjects before and after a meal with repositioning. Imaging was performed on a 3 Tesla MRI, using the IDEAL chemical-shift water-fat pulse sequence. Adipose tissue (subcutaneous--SAT, visceral--VAT) and organs (liver, pancreas) were manually segmented twice for each scan by a single trained observer. Automated segmentations of each subject's second scan were generated using a nonrigid volume registration algorithm for water-fat MRI images that used a b-spline basis for deformation and minimized image dissimilarity after the deformation. Manual and automated segmentations were compared using Dice coefficients and linear regression of SAT and VAT volumes, organ volumes, and hepatic and pancreatic fat fractions (HFF, PFF).

RESULTS

Manual segmentations from the 11 repositioned subjects exhibited strong repeatability and set performance benchmarks. The average Dice coefficients were 0.9747 (SAT), 0.9424 (VAT), 0.9404 (liver), and 0.8205 (pancreas); the linear correlation coefficients were 0.9994 (SAT volume), 0.9974 (VAT volume), 0.9885 (liver volume), 0.9782 (pancreas volume), 0.9996 (HFF), and 0.9660 (PFF). When comparing manual and automated segmentations, the average Dice coefficients were 0.9043 (SAT volume), 0.8235 (VAT), 0.8942 (liver), and 0.7168 (pancreas); the linear correlation coefficients were 0.9493 (SAT volume), 0.9982 (VAT volume), 0.9326 (liver volume), 0.8876 (pancreas volume), 0.9972 (HFF), and 0.8617 (PFF). In the four pre- and post-prandial subjects, the Dice coefficients were 0.9024 (SAT), 0.7781 (VAT), 0.8799 (liver), and 0.5179 (pancreas); the linear correlation coefficients were 0.9889, 0.9902 (SAT, and VAT volume), 0.9523 (liver volume), 0.8760 (pancreas volume), 0.9991 (HFF), and 0.6338 (PFF).

CONCLUSION

Automated intra-subject registration-based segmentation is potentially suitable for the quantification of abdominal and organ fat and achieves comparable quantitative endpoints with respect to manual segmentation.

摘要

目的

开发一种基于自动配准的分割算法，用于从三维（3D）水脂 MRI 数据中测量腹部脂肪组织容积和器官脂肪分数含量，并评估其与手动分割的性能。

材料和方法

数据来自 11 名受试者在两次中间重新定位时，以及 4 名受试者在重新定位前后餐前和餐后的数据。成像在 3.0T MRI 上进行，使用 IDEAL 化学位移水脂脉冲序列。脂肪组织（皮下-SAT，内脏-VAT）和器官（肝、胰腺）由一名经过培训的观察者分别进行两次手动分割。对每个受试者的第二扫描的自动分割是使用用于水脂 MRI 图像的非刚性体积配准算法生成的，该算法使用 b-样条作为变形的基础，并在变形后最小化图像差异。使用 Dice 系数和 SAT 和 VAT 体积、器官体积以及肝和胰腺脂肪分数（HFF、PFF）的线性回归比较手动和自动分割。

结果

11 名重新定位受试者的手动分割显示出很强的重复性和设定性能基准。平均 Dice 系数分别为 0.9747（SAT）、0.9424（VAT）、0.9404（肝）和 0.8205（胰腺）；线性相关系数分别为 0.9994（SAT 体积）、0.9974（VAT 体积）、0.9885（肝体积）、0.9782（胰腺体积）、0.9996（HFF）和 0.9660（PFF）。当比较手动和自动分割时，平均 Dice 系数分别为 0.9043（SAT 体积）、0.8235（VAT）、0.8942（肝）和 0.7168（胰腺）；线性相关系数分别为 0.9493（SAT 体积）、0.9982（VAT 体积）、0.9326（肝体积）、0.8876（胰腺体积）、0.9972（HFF）和 0.8617（PFF）。在四名餐前和餐后受试者中，Dice 系数分别为 0.9024（SAT）、0.7781（VAT）、0.8799（肝）和 0.5179（胰腺）；线性相关系数分别为 0.9889、0.9902（SAT 和 VAT 体积）、0.9523（肝体积）、0.8760（胰腺体积）、0.9991（HFF）和 0.6338（PFF）。