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ISA-Net: Improved spatial attention network for PET-CT tumor segmentation.
- Source :
-
Computer Methods & Programs in Biomedicine . Nov2022, Vol. 226, pN.PAG-N.PAG. 1p. - Publication Year :
- 2022
-
Abstract
- • Proposed an improved spatial attention method based on multi-modal data, which can make full use of the differences and complementarities between different modal data. • We analyze and discuss the influence of different weighting factors assigned to each modality on the fusion results. • In the encoding stage, each channel extracts feature information separately, and then performs fusion in the decoding stage to realize information complementarity • Special internal structure design can highlight the tumor region location information and suppress the non-tumor region location information and prevent model degradation effectively. • Experimental results show that our method improves the segmentation accuracy and has good generalization. Background and Objective: Achieving accurate and automated tumor segmentation plays an important role in both clinical practice and radiomics research. Segmentation in medicine is now often performed manually by experts, which is a laborious, expensive and error-prone task. Manual annotation relies heavily on the experience and knowledge of these experts. In addition, there is much intra- and interobserver variation. Therefore, it is of great significance to develop a method that can automatically segment tumor target regions. Methods: In this paper, we propose a deep learning segmentation method based on multimodal positron emission tomography-computed tomography (PET-CT), which combines the high sensitivity of PET and the precise anatomical information of CT. We design an improved spatial attention network(ISA-Net) to increase the accuracy of PET or CT in detecting tumors, which uses multi-scale convolution operation to extract feature information and can highlight the tumor region location information and suppress the non-tumor region location information. In addition, our network uses dual-channel inputs in the coding stage and fuses them in the decoding stage, which can take advantage of the differences and complementarities between PET and CT. Results: We validated the proposed ISA-Net method on two clinical datasets, a soft tissue sarcoma(STS) and a head and neck tumor(HECKTOR) dataset, and compared with other attention methods for tumor segmentation. The DSC score of 0.8378 on STS dataset and 0.8076 on HECKTOR dataset show that ISA-Net method achieves better segmentation performance and has better generalization. Conclusions: The method proposed in this paper is based on multi-modal medical image tumor segmentation, which can effectively utilize the difference and complementarity of different modes. The method can also be applied to other multi-modal data or single-modal data by proper adjustment. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 01692607
- Volume :
- 226
- Database :
- Academic Search Index
- Journal :
- Computer Methods & Programs in Biomedicine
- Publication Type :
- Academic Journal
- Accession number :
- 160314564
- Full Text :
- https://doi.org/10.1016/j.cmpb.2022.107129