Anatomy-guided deep learning for visceral fat segmentation in positron emission tomography-computed tomography

Background Accurate segmentation of abdominopelvic visceral adipose tissue (visceral fat) is critical for assessing the health risks associated with central obesity using positron emission tomography and cone-beam computed tomography. However, low-dose cone-beam computed tomography images are difficult to analyze because of anatomical complexity and low contrast. We developed an Anatomical Structure-Guided Segmentation Network, a deep learning–based artificial intelligence framework that integrates anatomical priors through Spatially Adaptive Normalization within a residual encoder–decoder backbone to achieve anatomically consistent visceral fat segmentation. Methods Data from 150 individuals who underwent positron emission tomography and cone-beam computed tomography as part of health screening were retrospectively analyzed. Ground-truth segmentations were manually refined from TotalSegmentator outputs. Five-fold cross-validation was applied to ensure robustness and generalizability. Segmentation accuracy was evaluated using Dice Similarity Coefficient, Intersection over Union, and 95th-percentile Hausdorff Distance, and compared with representative convolutional and transformer-based architectures. Results Visceral fat volumes from the proposed model and reference volumes showed high agreement (concordance correlation coefficient = 0.999; 95% confidence interval: 0.998–0.999). The mean percentage difference was −1.0%, with 95% limits of agreement from −8.5% to +6.5%. The proposed framework achieved the highest overall segmentation accuracy (Dice = 0.965 ± 0.004; Intersection = 0.932 ± 0.007; Hausdorff = 1.632 ± 0.512) and maintained robustness across abdominal regions. Conclusion The Anatomical Structure-Guided Segmentation Network offers a robust, anatomically guided framework for accurate visceral fat segmentation, with the potential to stratify clinical risk in metabolic and oncologic conditions.