Key result
The proposed multichannel fuzzy cluster mean algorithm with adaptive spatial term and bias shading correction yielded accurate in vivo segmentations of intramuscular adipose and connective tissue with a mean Dice coefficient of 0.977.
The proposed automated algorithm accurately segments intramuscular adipose and connective tissue from UTE MRI images.
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.
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Ugarte et al. (2016) studied Intramuscular adipose and connective tissue segmentation (n=10). Multichannel fuzzy cluster mean (MCFCM) algorithm with adaptive spatial term and bias shading correction vs. Original MCFCM and adaptive spatial MCFCM algorithms was evaluated on Segmentation accuracy (Dice coefficient). The proposed multichannel fuzzy cluster mean algorithm with adaptive spatial term and bias shading correction yielded accurate in vivo segmentations of intramuscular adipose and connective tissue with a mean Dice coefficient of 0.977.
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