Improved muscle and fat segmentation for body composition measures on quantitative CT.

Purpose: Body composition analysis on abdominal CT scans is useful for opportunistic screening. It also offers prognostic insights into mortality and cardiovascular risk. However, current segmentation methods for muscle and fat often fail on quantitative CT scans used for bone densitometry. These scans are commonly used to diagnose and monitor osteoporosis. This study aims to develop an accurate segmentation method for such scans and compare its performance with existing methods.

Methods: We applied an nnU-Net framework to segment muscle, subcutaneous fat, visceral fat, and an added 'body' class for other non-background voxels. Training data included CT scans with bone densitometry phantoms, with segmentation annotations generated using our previous segmentation method followed by manual refinement. The proposed method was evaluated on 980 CT scans across two internal and external datasets, including 30 CT scans with phantoms in internal and external datasets (15 scans in each). Comparison was made with TotalSegmentator and our previous approach.

Results: The proposed method achieved the highest accuracy for muscle and subcutaneous fat segmentation across all four datasets ( $p<0.05$ ) and delivered comparable accuracy for visceral fat. In comparison with TotalSegmentator and the previous method, there were no false segmentations in the densitometry phantom included within the display field-of-view of the patient scan.

Conclusion: Experimental results showed that the proposed method improved segmentation accuracy for muscle and subcutaneous fat while maintaining high accuracy for visceral fat. Notably, segmentation accuracy was also high in the quantitative CT scans for bone densitometry. These findings highlight the potential of the method to advance body composition analysis in clinical practice.