3D multimodal spatial fuzzy segmentation of intramuscular connective and adipose tissue from ultrashort TE MR images of calf muscle.
3D multimodal spatial fuzzy segmentation of intramuscular connective and adipose tissue from ultrashort TE MR images of calf muscle.
复制标题
根据小腿肌肉的超短 TE MR 图像对肌内结缔组织和脂肪组织进行 3D 多模态空间模糊分割。
DOI：
10.1002/mrm.26156
复制
发表时间：
2017
影响因子：
3.3
通讯作者：
Sinha,Shantanu
中科院分区：
文献类型：
--
作者：
Ugarte,Vincent;Sinha,Usha;Malis,Vadim;Csapo,Robert;Sinha,Shantanu
PurposeTo develop and evaluate an automated algorithm to segment intramuscular adipose (IMAT) and connective (IMCT) tissue from musculoskeletal MRI images acquired with a dual echo Ultrashort TE (UTE) sequence.Theory and MethodsThe dual echo images and calculated structure tensor images are the inputs to the multichannel fuzzy cluster mean (MCFCM) algorithm. Modifications to the basic multichannel fuzzy cluster mean include an adaptive spatial term and bias shading correction. The algorithm was tested on digital phantoms simulating IMAT/IMCT tissue under varying conditions of image noise and bias and on ten subjects with varying amounts of IMAT/IMCT.ResultsThe MCFCM including the adaptive spatial term and bias shading correction performed better than the original MCFCM and adaptive spatial MCFCM algorithms. IMAT/IMCT was segmented from the unsmoothed simulated phantom data with a mean Dice coefficient of 0.933 ±0.001 when contrast‐to‐noise (CNR) was 140 and bias was varied between 30% and 65%. The algorithm yielded accuratein vivosegmentations of IMAT/IMCT with a mean Dice coefficient of 0.977 ±0.066.ConclusionThe proposed algorithm is completely automated and yielded accurate segmentation of intramuscular adipose and connective tissue in the digital phantom and in human calf data. Magn Reson Med 77:870–883, 2017. © 2016 International Society for Magnetic Resonance in Medicine
