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Deep Learning Technique for Automatic Segmentation of Proximal Hip Musculoskeletal Tissues From CT Scan Images: A
Mahdi Imani1,2, Jared Buratto2, Thang Dao2
1Australian Institute for Musculoskeletal Science (AIMSS), The University of Melbourne and Western Health, St. Albans, Victoria, Australia.
Background:
Age-related conditions, such as osteoporosis and sarcopenia, alongside chronic diseases, can result in significant musculoskeletal tissue loss. This impacts individuals' quality of life and increases risk of falls and fractures. Computed tomography (CT) has been widely used for assessing musculoskeletal tissues. Although automatic techniques have been investigated for segmenting tissues in the abdomen and mid-thigh regions, studies in proximal hip remain limited. This study aims to develop a deep learning technique for segmentation and quantification of musculoskeletal tissues in CT scans of proximal hip.
Methods:
We examined 300 participants (men, 73 ± 6 years) from two cohorts of the Osteoporotic Fractures in Men Study (MrOS). We manually segmented cortical bone, trabecular bone, marrow adipose tissue (MAT), haematopoietic bone marrow (HBM), muscle, intermuscular adipose tissue (IMAT) and subcutaneous adipose tissue (SAT) from CT scan images at the proximal hip level. Using these data, we trained a U-Net-like deep learning model for automatic segmentation. The association between model-generated quantitative results and outcome variables such as grip strength, chair sit-to-stand time, walking speed, femoral neck and spine bone mineral density (BMD), and total lean mass was calculated.
Results:
An average Dice similarity coefficient (DSC) above 90% was observed across all tissue types in the test dataset. Grip strength showed positive correlations with cortical bone area (coefficient: 0.95, 95% confidence interval: [0.10, 1.80]), muscle area (0.41, [0.19, 0.64]) and average Hounsfield unit for muscle adjusted for height squared (AHU/h2) (1.1, [0.53, 1.67]), while it was negatively correlated with IMAT (-1.45, [-2.21, -0.70]) and SAT (-0.32, [-0.50, -0.13]). Gait speed was directly related to muscle area (0.01, [0.00, 0.02]) and inversely to IMAT (-0.04, [-0.07, -0.01]), while chair sit-to-stand time was associated with muscle area (0.98, [0.98, 0.99]), IMAT area (1.04, [1.01, 1.07]), SAT area (1.01, [1.01, 1.02]) and AHU/h2 for muscle (0.97, [0.95, 0.99]). MAT area showed a potential link to non-trauma fractures post-50 years (1.67, [0.98, 2.83]). Femoral neck BMD was associated with cortical bone (0.09, [0.08, 0.10]), MAT (-0.11, [-0.13, -0.10]), MAT adjusted for total bone marrow area (-0.06, [-0.07, -0.05]) and AHU/h2 for muscle (0.01, [0.00, 0.02]). Total spine BMD showed similar associations and with AHU for muscle (0.02, [0.00, 0.05]). Total lean mass was correlated with cortical bone (517.3, [148.26, 886.34]), trabecular bone (924, [262.55, 1585.45]), muscle (381.71, [291.47, 471.96]), IMAT (-1096.62, [-1410.34, -782.89]), SAT (-413.28, [-480.26, -346.29]), AHU (527.39, [159.12, 895.66]) and AHU/h2 (300.03, [49.23, 550.83]).
Conclusion:
Our deep learning-based technique offers a fast and accurate method for segmentation and quantification of musculoskeletal tissues in proximal hip, with potential clinical value.
