3D multimodal spatial fuzzy segmentation of intramuscular connective and adipose tissue from ultrashort TE MR images of calf muscle

• Published in: Magnetic resonance in medicine Vol. 77; no. 2; pp. 870 - 883
• Main Authors: Ugarte, Vincent, Sinha, Usha, Malis, Vadim, Csapo, Robert, Sinha, Shantanu
• Format: Journal Article
• Language: English
• Published: United States Wiley Subscription Services, Inc 01.02.2017
• Subjects: Adaptive algorithms; Adipose tissue; Adipose Tissue - diagnostic imaging; Adult; Aged, 80 and over; Algorithms; Automation; Bias; Cluster Analysis; Clusters; Connective Tissue - diagnostic imaging; Connective tissues; Digital imaging; fuzzy cluster mean segmentation; Fuzzy Logic; Humans; Image acquisition; Image segmentation; Imaging, Three-Dimensional - methods; intramuscular adipose tissue; intramuscular connective tissue; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Mathematical analysis; Multimodal Imaging - methods; Muscle, Skeletal - diagnostic imaging; Muscles; Shading; structure tensor maps; Tensors; ultrashort TE; intramuscular connective tissue; fuzzy cluster mean segmentation; structure tensor maps; intramuscular adipose tissue; ultrashort TE
• ISSN: 0740-3194; 1522-2594; 1522-2594
• DOI: 10.1002/mrm.26156

Purpose To 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 Methods The 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. Results The 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 accurate in vivo segmentations of IMAT/IMCT with a mean Dice coefficient of 0.977 ±0.066. Conclusion The 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