受扁虫神经结构启发，新模型提升3D脑部影像分割精度
Advancing 3D Medical Image Segmentation: Unleashing the Potential of Planarian Neural Networks in Artificial Intelligence
- 模仿扁虫双神经索结构，设计双路UNet融合架构
- 在无增强数据下，分割准确率较基线UNet提升4.2%
- 适合医学影像分割场景，尤其数据有限时表现更优
本研究提出PNN-UNet，一种仿照扁虫神经网络结构的深度神经网络，用于3D医学图像分割。扁虫具有两个神经索和一个协调中枢的脑结构，PNN-UNet据此构建深部UNet与宽幅UNet作为神经索，以密集连接自编码器充当大脑角色。该架构在3D MRI海马体数据集上，无论是否使用数据增强，均优于基础UNet及多种UNet变体，在无数据增强条件下平均交并比（mIoU）达89.7%，较基准提升4.2%。
原文摘要 · Abstract (English)
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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