Development and validation of fully automated robust deep learning models for multi-organ segmentation from whole-body CT images

Purpose: This study aimed to develop a deep-learning framework to generate multi-organ masks from CT images in adult and pediatric patients. Methods: A dataset consisting of 4082 CT images and ground-truth manual segmentation from various databases, including 300 pediatric cases, were collected. In strategy#1, the manual segmentation masks provided by public databases were split into training (90%) and testing (10% of each database named subset #1) cohort. The training set was used to train multiple nnU-Net networks in five-fold cross-validation (CV) for 26 separate organs. In the next step, the trained models from strategy #1 were used to generate missing organs for the entire dataset. This generated data was then used to train a multi-organ nnU-Net segmentation model in a five-fold CV (strategy#2). Models' performance were evaluated in terms of Dice coefficient (DSC) and other well-established image segmentation metrics. Results: The lowest CV DSC for strategy#1 was 0.804 +/- 0.094 for adrenal glands while average DSC > 0.90 were achieved for 17/26 organs. The lowest DSC for strategy#2 (0.833 +/- 0.177) was obtained for the pancreas, whereas DSC > 0.90 was achieved for 13/19 of the organs. For all mutual organs included in subset #1 and subset #2, our model outperformed the TotalSegmentator models in both strategies. In addition, our models outperformed the TotalSegmentator models on subset #3. Conclusions: Our model was trained on images with significant variability from different databases, producing acceptable results on both pediatric and adult cases, making it well-suited for implementation in clinical setting.