An automated method to detect interstitial adipose tissue in thigh muscles for patients with osteoarthritis.
In this paper we explore a method of segmentation of muscle interstitial adipose tissue (IAT) in MR images of the thigh. The objective is to apply the method towards research into biomarkers of osteoarthritis (OA). T1-weighted images of the thigh are intensity standardized through bias field correction and intensity normalization. IAT within the thigh muscles is then segmented using a threshold combined with morphological constraints applied on connected regions in the thresholded image. The morphological constraints can be adjusted to allow for highly sensitive or highly specific IAT segmentation. The use of the morphological constraints improved the specificity of IAT segmentation over a threshold segmentation method from 0.54 to 0.67, while retaining a nearly equivalent sensitivity of 0.82 compared to 0.84. We then present a preliminary statistical analysis to demonstrate the application of the automated IAT segmentation. Finally, we specify a protocol for further exploration of IAT by leveraging the massive imaging dataset of the Osteoarthritis Initiative (OAI).
Prescott
Jeffrey W
JW
Department of Biomedical Informatics, Ohio State University, Columbus, OH 43210, USA. jeffrey.prescott@osumc.edu
Priddy
Mike
M
Best
Thomas M
TM
Pennell
Michael
M
Swanson
Mark S
MS
Haq
Furqan
F
Jackson
Rebecca D
RD
Gurcan
Metin N
MN
D000273:Adipose Tissue / Q000473:pathology*; D000465:Algorithms; D001185:Artificial Intelligence; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008875:Middle Aged; D018482:Muscle, Skeletal / Q000473:pathology*; D010003:Osteoarthritis / Q000473:pathology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013848:Thigh / Q000473:pathology