Advancing 3D Medical Image Segmentation: Unleashing the Potential of Planarian Neural Networks in Artificial Intelligence
To address insufficient accuracy, efficiency, and generalizability in 3D medical image segmentation, this paper proposes Bio-inspired PNN-UNet—a novel architecture mapping the neural anatomy of planarians onto deep network topology. It features a dual-pathway collaborative design (Deep-UNet and Wide-UNet) integrated with a dense-connection autoencoder serving as a global feature fusion “brain” module. The method combines 3D convolutions, multi-scale feature fusion, and optional data augmentation, enabling end-to-end training. Evaluated on 3D MRI hippocampal segmentation, it achieves statistically significant improvements in Dice score over UNet and multiple state-of-the-art variants (p < 0.01), while demonstrating superior robustness and cross-dataset generalizability. The core contribution lies in pioneering the integration of neurobiologically inspired principles—specifically modular organization and functional division-cooperation mechanisms—into the architectural design of 3D medical image segmentation networks.