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作者

Xue, Jie Ren, Liwen Song, Bosheng Guo, Yujie Lu, Jie Liu, Xiyu Gong, Guanzhong Li, Dengwang

作者单位

²  College of Computer Science and Electronic Engineering, Hunan University, Changsha, Hunan, China  ¹  Business School, Shandong Key Laboratory of Medical Physics and Image Processing, Shandong Normal University, Jinan, Shandong, China  ³  Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, China  ⁴  School of Physics and Electronics, Shandong Key Laboratory of Medical Physics and Image Processing, Shandong Institute of Industrial Technology for Health Sciences and Precision Medicine, Shandong Normal University, Jinan, Shandong, China 

刊名

IEEE Transactions on Parallel and Distributed Systems

年份

2023

卷号

Vol.34 No.4

页码

1202-1214

ISSN

1045-9219

关键词

Neurons Segment Neoplasm Hypergraph Image segmentation Image segmentation Computational modeling Medical diagnostic imaging Numerical models Hippocampus Biological neural networks Hypergraph neural-like P systems medical image segmentation

摘要

Neural-like P systems are membrane computing models inspired by natural computing and are viewed as third-generation neural network models. Although real neurons have complex structures, classical neural-like P systems simplify the structures and corresponding mechanisms to two-dimensional graphs or tree-based firing and forgetting communications, which limit the real applications of these models. In this paper, we propose a hypergraph-based numerical neural-like P system containing five types ...更多

Neural-like P systems are membrane computing models inspired by natural computing and are viewed as third-generation neural network models. Although real neurons have complex structures, classical neural-like P systems simplify the structures and corresponding mechanisms to two-dimensional graphs or tree-based firing and forgetting communications, which limit the real applications of these models. In this paper, we propose a hypergraph-based numerical neural-like P system containing five types of neurons to describe the high-order correlations among neuron structures. Three new kinds of communication mechanisms among neurons are also proposed to address numerical variables and functions. Based on the new neural-like P system, a tumor/organ segmentation model for medical images is developed. The experimental results indicate that the proposed models outperform the state-of-the-art methods based on two hippocampal datasets and a multiple brain metastases dataset, thus verifying the effectiveness of the HNN P system in correctly segmenting tumors/organs.收起

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