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.