CT-based segmentation of pectoral muscle using deep learning and association of computed metrics with aging and sex

Indra Narayan Dutta; Syed Ahmed Nadeem; Alejandro P. Comellas; Eric A. Hoffman; Punam K. Saha

doi:10.1117/12.2613073

4 April 2022 CT-based segmentation of pectoral muscle using deep learning and association of computed metrics with aging and sex

Indra Narayan Dutta, Syed Ahmed Nadeem, Alejandro P. Comellas, Eric A. Hoffman, Punam K. Saha

Author Affiliations +

Proceedings Volume 12036, Medical Imaging 2022: Biomedical Applications in Molecular, Structural, and Functional Imaging; 120360R (2022) https://doi.org/10.1117/12.2613073
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

Abstract

Deterioration of the overall musculoskeletal system with aging is a universal phenomenon influenced by different demographic and lifestyle factors. Often, pectoral muscle metrics are used to describe overall muscle health, and CTbased studies have demonstrated their associations with various diseases, lung function, and mortality. However, these studies use extremely laborious manual means to segment pectoral muscles limiting both study size and scope. Here, we present a CT-based automated method for segmentation of the pectoral muscle using deep learning and computation of pectoral muscle area (PMA). We examined the extent of change in PMA with aging and sex using retrospective chest CT scans (n = 260) from COPDGene Iowa cohort at baseline visits. A two-dimensional U-Net was developed, optimized, and trained (n = 60) to generate a pixel-wise pectoral muscle probability map from chest CT scans, which was followed by an image post-processing cascade to segment the muscle area. Preliminary results (n = 200) show that our CT-based automated segmentation method is accurate (Dice score = 0.93), and it detects muscle wasting with aging. Males had significantly greater PMA as compared to females (effect size: 0.84; p < 0.001). A five-year loss in PMA of 4.8% was observed in the study population with losses of 4.3% and 5.1% for females and males, respectively. Chest CT-based automated methods for pectoral muscle segmentation are suitable for large population studies exploring broader scientific knowledge under various diseases.

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Citation Download Citation

Indra Narayan Dutta, Syed Ahmed Nadeem, Alejandro P. Comellas, Eric A. Hoffman, and Punam K. Saha "CT-based segmentation of pectoral muscle using deep learning and association of computed metrics with aging and sex ", Proc. SPIE 12036, Medical Imaging 2022: Biomedical Applications in Molecular, Structural, and Functional Imaging, 120360R (4 April 2022); https://doi.org/10.1117/12.2613073

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