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

• URL: http://arxiv.org/abs/2505.04664v1
• Date: Wed, 07 May 2025 03:54:37 GMT
• Title: Advancing 3D Medical Image Segmentation: Unleashing the Potential of Planarian Neural Networks in Artificial Intelligence
• Authors: Ziyuan Huang, Kevin Huggins, Srikar Bellur,
• Abstract summary: PNN-UNet is a method for constructing deep neural networks that replicate the planarian neural network structure.<n>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.<n>Our outcomes on a 3D MRI dataset, with and without data augmentation, demonstrate that PNN-UNet outperforms the baseline UNet in image segmentation.
• Score: 6.3447893760573955
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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