Golzan, Seyyed Morteza (2025) Estimating whole-body tissue composition from sub-body CT scans using automated field-of-view detection. Masters thesis, Memorial University of Newfoundland.
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Abstract
Body composition is a critical health indicator with profound implications for clinical conditions, including sarcopenia, cancer, cardiovascular diseases, osteoporosis, and diabetes. Accurate assessment of body composition is essential for personalized and preventive medicine, as it aids in clinical decision-making and health optimization. This thesis investigates the prediction of whole-body tissue composition from subbody CT scans, focusing on four key tissue types: skeletal muscle (SKM), subcutaneous adipose tissue (SAT), visceral adipose tissue (VAT), and intermuscular adipose tissue (IMAT). The research has been conducted in two phases. The first phase involves developing a deep learning model to classify sub-body CT scans into six anatomical regions: chest (CHE), abdomen (ABD), pelvis (PLV), chest & abdomen (CHA), abdomen & pelvis (ABP), and chest, abdomen & pelvis (CAP). Utilizing maximum intensity projection to map 3D scans to 2D, the CNN-based model achieved over 95% classification accuracy across diverse patient demographics and clinical protocols. The second phase focuses on developing regression models to predict whole-body tissue volumes using features derived from classified sub-body scans. For each classified CT scan, a separate multivariate linear regression model is developed, incorporating demographic and CT-derived features. These models were validated using ten-fold cross-validation, achieving high performance across regions. For individual regions, SKM demonstrates strong performance across all areas with high R² values (≈ 0:9) and low MAD (e.g., chest: 1.09 ± 0.86 L). SAT performs best in the PLV region (R² = 0.894, MAD = 1.59 ± 1.29 L), while VAT achieves its highest performance in the ABD region (R² = 0.965, MAD = 0.26 ± 0.21 L). IMAT shows small MAD values (e.g., chest: 0.26 ± 0.20 L) but relatively low R², with slightly better predictability in the CHE (R² = 0.790). For combined regions, SKM performs best in the CAP region (R² = 0.949, MAD = 0.77 ± 0.65 L), while SAT improves in the ABP region (R² = 0.927, MAD = 1.36 ± 1.13 L). VAT shows consistently excellent performance across all combined regions, with similar R² values (CHA: 0.973, ABP: 0.979, CAP: 0.999) and low MAD. The broader anatomical coverage in combined regions enhances predictability, particularly for SKM, SAT, and VAT, as it captures more comprehensive tissue distribution. These findings highlight the feasibility of using sub-body CT scans to estimate whole-body tissue composition accurately, reducing the need for resource-intensive full-body scans while minimizing radiation exposure. This approach holds promise for advancing personalized healthcare strategies and preventive interventions.
| Item Type: | Thesis (Masters) |
|---|---|
| URI: | http://research.library.mun.ca/id/eprint/17010 |
| Item ID: | 17010 |
| Additional Information: | Includes bibliographical references (pages 58-68) -- Restricted until May 23, 2026 |
| Keywords: | body composition estimation, sub-body CT scans, deep learning, medical image analysis, field-of-view classification |
| Department(s): | Engineering and Applied Science, Faculty of |
| Date: | May 2025 |
| Date Type: | Submission |
| Library of Congress Subject Heading: | Body composition; Diagnostic imaging--Analysis; Tomography; Deep learning (Machine learning) |
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