The Battle of the Titans: Comparing Three Leading Deep Learning Methods for 3D Medical Imaging

Accurate hippocampal segmentation is critical for neuroimaging research, clinical diagnosis, and surgical planning. This paper presents a rigorous comparison of three leading deep learning frameworks - 3D U-Net, nnU-Net V2, and MONAI U-Net - on the Medical Segmentation Decathlon (MSD) Task 4 hippocampus dataset. Using five-fold cross-validation and fifteen complementary metrics spanning overlap, boundary accuracy, classification, and statistical reliability, we evaluate each model's strengths and limitations. Results show that nnU-Net delivers the highest volumetric accuracy (Dice: 0.891) and strongest statistical consistency (Pearson: 0.989), 3D U-Net achieves superior boundary precision (ASSD: 0.159 mm), and MONAI offers high sensitivity (Recall: 0.991) with strong adaptability for research workflows. We provide application-driven recommendations: nnU-Net is optimal for longitudinal and multi-center studies, 3D U-Net for boundary-sensitive surgical tasks, and MONAI for rapid prototyping. This work establishes a comprehensive benchmark for hippocampal segmentation and offers practical guidance for framework selection in clinical and research settings.