Automated Segmentation of the Hippocampus in Pediatric Imaging

The infant hippocampus plays a pivotal role in early brain development and is linked to cognitive and memory functions. Accurate delineation of the hippocampus is essential for studying normal brain development and detecting early abnormalities associated with various neurodevelopmental disorders. In this paper, different deep neural network models were trained for 3D-automatic segmentation of the hippocampus based on cross-validation on a cohort of T1-Weighted (T1W) images acquired from 100 subjects with ground truth. The models were tested on another image cohort of 86 subjects without ground truth. Ensembling the single-trained models during cross-validation resulted in the final segmentation. Among all the trained networks, nnUNet and SegResNet achieved the best average Dice Similarity Coefficient (DSC)=0.82±0.01 and 95th Hausdorff Distance (95HD)=2.45±1.10 mm, respectively, in 5-fold cross-validation. We presented a comprehensive comparison between different architectures in terms of their generalizability and effectiveness, suggesting the potential for developing on-the-fly automated segmentation of the hippocampus in pediatric MRI.