Deep-Learning-based Automatic Segmentation and Quantitative Measurement of Normal Spleen in Chinese Adults

The spleen is a key immune organ whose volume and attenuation can change in inflammatory, infectious, and hematologic diseases. In routine practice, splenic enlargement is often assessed using semi-quantitative linear criteria, such as rib-unit-based estimates, which can miss modest but clinically relevant volume changes. While deep learning enables reliable organ segmentation on CT, large-scale descriptions of normal splenic morphometrics in Chinese adults, including age- and sex-related patterns, remain limited.

This study developed a 3D V-Net for automated spleen segmentation, and trained it on 2,856 CT examinations from four public datasets: Abdomen CT-1K, FLARE23, AMOS, and the RSNA 2023 Abdominal Trauma dataset. The model’s performance was assessed using the Dice similarity coefficient (DSC), volume similarity (VS), Hausdorff distance (HD), and average Hausdorff distance. For morphometric analysis and institutional validation, this model was then applied to 1,520 CT examinations in 2023 of individuals with a clinically and radiologically normal spleen, comprising a total of 3,490 CT images across phases (NOC/AP/PVP/DP), from which spleen volume, mean CT value, and three-dimensional diameters were extracted.

On the public test set, the model achieved a Dice similarity coefficient (DSC) of 0.982 [interquartile range (IQR): 0.975-0.988], a volumetric similarity (VS) of 0.995 [IQR: 0.991-0.998], and a Hausdorff distance (HD) of 3.047 mm [IQR: 2.578-4.653 mm]. Within the institutional cohort, the median spleen volume was significantly greater in males (213.74 cm3 [IQR: 158.40-284.88]) than in females (163.70 cm3 [IQR: 125.99-217.72]; p < 0.001). Age-related volumetric patterns also differed by sex: males exhibited an inverted U-shaped trajectory, peaking at 273.75 ± 92.96 cm3 in the 28-37 year age group, while females displayed a bimodal pattern, peaking at 225.01 ± 65.53 cm3in the 18-27 year age group. Furthermore, splenic attenuation was significantly higher in females across the arterial, portal venous, and delayed contrast phases (all p < 0.001).

The 3D V-Net enabled accurate and efficient spleen segmentation on CT, which permitted automated and reproducible measurement of splenic morphometrics. The observed sex- and age-specific patterns in spleen volume and attenuation likely reflect the combined effects of physiology, including hormonal status, as well as metabolic and body-composition factors. Reported differences across populations suggest that splenic reference values may not be directly transferable between ethnic groups, underscoring the need for population-calibrated morphometric data. Limitations of this study include potential residual confounding, incomplete adjustment for anthropometric covariates, and validation within a single institutional center.

This study establishes a practical deep learning-based pipeline for automated spleen segmentation and standardized morphometric extraction on CT in Chinese adults. The resulting population-specific reference distributions may support more objective interpretation of spleen size and attenuation in routine practice and provide a foundation for future disease-oriented applications. Further multicenter validation is warranted.