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Zhang T, Wang K, Cui H, Jin Q, Cheng P, Nakaguchi T, Li C, Ning Z, Wang L, Xuan P. Topological structure and global features enhanced graph reasoning model for non-small cell lung cancer segmentation from CT. Phys Med Biol 2023; 68. [PMID: 36625358 DOI: 10.1088/1361-6560/acabff] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Accepted: 12/15/2022] [Indexed: 01/11/2023]
Abstract
Objective.Accurate and automated segmentation of lung tumors from computed tomography (CT) images is critical yet challenging. Lung tumors are of various sizes and locations and have indistinct boundaries adjacent to other normal tissues.Approach.We propose a new segmentation model that can integrate the topological structure and global features of image region nodes to address the challenges. Firstly, we construct a weighted graph with image region nodes. The graph topology reflects the complex spatial relationships among these nodes, and each node has its specific attributes. Secondly, we propose a node-wise topological feature learning module based on a new graph convolutional autoencoder (GCA). Meanwhile, a node information supplementation (GNIS) module is established by integrating specific features of each node extracted by a convolutional neural network (CNN) into each encoding layer of GCA. Afterwards, we construct a global feature extraction model based on multi-layer perceptron (MLP) to encode the features learnt from all the image region nodes which are crucial complementary information for tumor segmentation.Main results.Ablation study results over the public lung tumor segmentation dataset demonstrate the contributions of our major technical innovations. Compared with other segmentation methods, our new model improves the segmentation performance and has generalization ability on different 3D image segmentation backbones. Our model achieved Dice of 0.7827, IoU of 0.6981, and HD of 32.1743 mm on the public dataset 2018 Medical Segmentation Decathlon challenge, and Dice of 0.7004, IoU of 0.5704 and HD of 64.4661 mm on lung tumor dataset from Shandong Cancer Hospital.Significance. The novel model improves automated lung tumor segmentation performance especially the challenging and complex cases using topological structure and global features of image region nodes. It is of great potential to apply the model to other CT segmentation tasks.
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Affiliation(s)
- Tiangang Zhang
- School of Computer Science and Technology, Heilongjiang University, Harbin, People's Republic of China.,School of Mathematical Science, Heilongjiang University, Harbin, People's Republic of China
| | - Kai Wang
- School of Computer Science and Technology, Heilongjiang University, Harbin, People's Republic of China
| | - Hui Cui
- Department of Computer Science and Information Technology, La Trobe University, Melbourne, Australia
| | - Qiangguo Jin
- School of Software, Northwestern Polytechnical University, Xi'an, People's Republic of China
| | - Peng Cheng
- Department of Computer Science and Information Technology, La Trobe University, Melbourne, Australia
| | - Toshiya Nakaguchi
- Center for Frontier Medical Engineering, Chiba University, Chiba, Japan
| | | | - Zhiyu Ning
- Sydney Polytechnic Institute, Sydney, Australia
| | - Linlin Wang
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical Universitmy of Medical Sciences, Jinan, People's Republic of China
| | - Ping Xuan
- Department of Computer Science, School of Engineering, Shantou University, Shantou, People's Republic of China
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