Deep Learning-Based CT Splenic Segmentation and Morphometrics: A Spleen Volume Prediction Model

Objective: To develop a deep learning (DL)-based automated segmentation model for normal spleens to enable automated assessment of splenic diameter, volume, and CT (computed tomography) values; and to investigate key physiological factors influencing normal adult splenic volume to ultimately develop a population-specific predictive model for the Chinese adult population. Methods and Materials: To train the 3D U-Net segmentation model, Dataset 1 was randomly split into training (n=3418), validation (n=413), and test (n=443) sets. For internal validation (Dataset 2), we utilized 1,996 thin-slice CT images from upper abdominal scans conducted at our institution (January–April 2024); all scans exhibited no indications of splenic lesions or structural anomalies. The external validation set included 2,856 publicly available thin-slice CT images. Model performance on the test set was evaluated using the Dice coefficient, Hausdorff distance, and volume similarity. Step 2: Develop a predictive model by investigating physiological factors influencing normal adult spleen volume. Dataset 2 and an additional 578 upper abdominal CT scans (Dataset 3) performed at our institution (January–April 2023) showed no evidence of splenic tumors or structural abnormalities. The model developed in Step 1 was used to segment the spleen and calculate its volume. Spleen volume, three-dimensional dimensions, mean CT values, and contrast enhancement patterns were analyzed for scans with adequate segmentation. Physiological parameters influencing normal adult spleen volume were evaluated using portal venous phase images. Results: The model's training and validation revealed a volume similarity of 0.997, a Hausdorff distance of 0.015 [0.013, 0.018] mm, and a Dice similarity coefficient of 0.988 [0.984, 0.989] (median [interquartile range]). The subjects' spleen volumes ranged from 51,086.25 to 644,376.86 mm³, with a median of 177,903.06 mm³. The measurement ranges for x, y, and z are 87.56 ± 11.61 mm (mean ± standard deviation), 92.02 mm [81.98, 104.61], and 91.00 mm [80.00, 103.00], respectively. Age (r = −0.24, p < 0.0001) and gender (male=0, female=1; r = −0.32, p < 0.0001) were negatively correlated with splenic volume (SV), while height (r = 0.35, p < 0.0001), weight (W; r = 0.45, p < 0.0001), body mass index (BMI; r = 0.32, p < 0.0001), and body surface area (BSA; r = 0.46, p < 0.0001) were positively correlated with SV. Using portal venous phase thin-slice CT images, the standard splenic volume (SSV) for the Chinese population was derived using the formula SSV = −202,839.48 + 25.26W + 214,521.59BSA (where SSV = standard splenic volume (mm³), W = weight (kg), and BSA = body surface area (m²). Conclusion: The 3D U-Net architecture enabled the development of an effective automated splenic segmentation model, facilitating automated assessment of quantitative metrics including splenic volume, 3D dimensions, and mean CT values. Among the anthropometric variables studied, splenic volume exhibited the strongest correlations with body weight and body surface area.