Advancing 3D Medical Image Segmentation: Unleashing the Potential of Planarian Neural Networks in Artificial Intelligence

TL;DR

This paper introduces PNN-UNet, a novel neural network architecture inspired by planarian neural structures, which improves 3D medical image segmentation performance over traditional UNet models.

Contribution

The study proposes a new neural network architecture, PNN-UNet, that mimics planarian neural structures, combining Deep-UNet and Wide-UNet with autoencoders for enhanced segmentation.

Findings

PNN-UNet outperforms baseline UNet in 3D MRI hippocampus segmentation.

Data augmentation further improves PNN-UNet performance.

The architecture offers advantages over monolithic and ensemble networks.

Abstract

Our study presents PNN-UNet as a method for constructing deep neural networks that replicate the planarian neural network (PNN) structure in the context of 3D medical image data. Planarians typically have a cerebral structure comprising two neural cords, where the cerebrum acts as a coordinator, and the neural cords serve slightly different purposes within the organism's neurological system. Accordingly, PNN-UNet comprises a Deep-UNet and a Wide-UNet as the nerve cords, with a densely connected autoencoder performing the role of the brain. This distinct architecture offers advantages over both monolithic (UNet) and modular networks (Ensemble-UNet). Our outcomes on a 3D MRI hippocampus dataset, with and without data augmentation, demonstrate that PNN-UNet outperforms the baseline UNet and several other UNet variants in image segmentation.