Automatic intra-subject registration-based segmentation of abdominal fat from water-fat MRI.
Automatic intra-subject registration-based segmentation of abdominal fat from water-fat MRI.
复制标题
DOI：
10.1002/jmri.23813
复制
发表时间：
2013-02
影响因子：
4.4
通讯作者：
Nayak, Krishna S.
中科院分区：
文献类型：
--
作者：
Joshi, Anand A.;Hu, Houchun H.;Leahy, Richard M.;Goran, Michael I.;Nayak, Krishna S.
开发一种基于自动配准的分割算法，用于从3D水-脂肪MRI数据中测量腹部脂肪组织贮库体积和器官脂肪分数含量，并评价其相对于手动分割的性能。数据来自11名受试者在两个时间点的中间重新定位，并从4名受试者餐前和餐后重新定位。在3 T MRI上进行成像，使用IDEAL化学位移水脂肪脉冲序列。由一名受过训练的观察者对脂肪组织（皮下-SAT、内脏-VAT）和器官（肝脏、胰腺）进行两次手动分割。使用水-脂肪MRI图像的非刚性体积配准算法生成每个受试者的第二次扫描的自动分割，该算法使用b样条基础进行变形并在变形后最小化图像的不相似性。使用Dice系数和SAT和VAT体积、器官体积以及肝脏和胰腺脂肪分数（HFF、PFF）的线性回归来比较手动和自动分割。手动分割从11个重新定位的主题表现出很强的可重复性和设置性能基准。平均Dice系数分别为0.9747（SAT）、0.9424（VAT）、0.9404（肝脏）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（肝脏体积）、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系数为0.9043（SAT体积）、0.8235（VAT）、0.8942（肝脏）和0.7168（胰腺）;线性相关系数为0.9493（SAT体积）、0.9982（VAT体积）、0.9326（肝脏体积）、0.8876（胰腺体积）、0.9972（HFF）和0.8617（PFF）。在4名餐前和餐后受试者中，Dice系数分别为0.9024（SAT）、0.7781（VAT）、0.8799（肝脏）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（肝脏体积）、0.8760（胰腺体积）、0.9991（HFF）和0.6338（PFF）。基于受试者内配准的自动分割可能适用于腹部和器官脂肪的量化，并实现与手动分割相当的定量终点。
To develop an automatic registration-based segmentation algorithm for measuring abdominal adipose tissue depot volumes and organ fat fraction content from 3D water-fat MRI data, and to evaluate its performance against manual segmentation. Data were obtained from eleven subjects at two time-points with intermediate repositioning, and from four subjects before and after a meal with repositioning. Imaging was performed on a 3T 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 non-rigid 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). Manual segmentations from the eleven 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). 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.
登录
查看更多内容
影响因子：
4.4
作者：
Kullberg, Joel;Johansson, Lars;Boernert, Peter
通讯作者：
Boernert, Peter
影响因子：
1.3
作者：
PELIZZARI, CA;CHEN, GTY;CHEN, CT
通讯作者：
CHEN, CT
影响因子：
4.4
作者：
Machann, J;Thamer, C;Schick, F
通讯作者：
Schick, F
影响因子：
3.3
作者：
Reeder, SB;Pineda, AR;Pelc, NJ
通讯作者：
Pelc, NJ
影响因子：
4.4
作者：
Positano, V;Gastaldelli, A;Landini, L
通讯作者：
Landini, L