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Decoupled pyramid correlation network for liver tumor segmentation from CT images

文献类型：期刊论文

作者Zhang, Yao2,5,6; Yang, Jiawei7; Liu, Yang5,6; Tian, Jiang2; Wang, Siyun3; Zhong, Cheng2; Shi, Zhongchao2; Zhang, Yang4; He, Zhiqiang1
刊名MEDICAL PHYSICS
出版日期2022-08-17
页码15
ISSN号0094-2405
关键词attention mechanism computed tomography liver segmentation liver tumor segmentation
DOI10.1002/mp.15723
英文摘要Purpose Automated liver tumor segmentation from computed tomography (CT) images is a necessary prerequisite in the interventions of hepatic abnormalities and surgery planning. However, accurate liver tumor segmentation remains challenging due to the large variability of tumor sizes and inhomogeneous texture. Recent advances based on fully convolutional network (FCN) for medical image segmentation drew on the success of learning discriminative pyramid features. In this paper, we propose a decoupled pyramid correlation network (DPC-Net) that exploits attention mechanisms to fully leverage both low- and high-level features embedded in FCN to segment liver tumor. Methods We first design a powerful pyramid feature encoder (PFE) to extract multilevel features from input images. Then we decouple the characteristics of features concerning spatial dimension (i.e., height, width, depth) and semantic dimension (i.e., channel). On top of that, we present two types of attention modules, spatial correlation (SpaCor) and semantic correlation (SemCor) modules, to recursively measure the correlation of multilevel features. The former selectively emphasizes global semantic information in low-level features with the guidance of high-level ones. The latter adaptively enhance spatial details in high-level features with the guidance of low-level ones. Results We evaluate the DPC-Net on MICCAI 2017 LiTS Liver Tumor Segmentation (LiTS) challenge data set. Dice similarity coefficient (DSC) and average symmetric surface distance (ASSD) are employed for evaluation. The proposed method obtains a DSC of 76.4% and an ASSD of 0.838 mm for liver tumor segmentation, outperforming the state-of-the-art methods. It also achieves a competitive result with a DSC of 96.0% and an ASSD of 1.636 mm for liver segmentation. Conclusions The experimental results show promising performance of DPC-Net for liver and tumor segmentation from CT images. Furthermore, the proposed SemCor and SpaCor can effectively model the multilevel correlation from both semantic and spatial dimensions. The proposed attention modules are lightweight and can be easily extended to other multilevel methods in an end-to-end manner.
WOS研究方向Radiology, Nuclear Medicine & Medical Imaging
语种英语
出版者WILEY
WOS记录号WOS:000841375700001
源URL[http://119.78.100.204/handle/2XEOYT63/19468]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者He, Zhiqiang
作者单位1.Lenovo Ltd, Beijing, Peoples R China
2.Lenovo Res, AI Lab, Beijing, Peoples R China
3.Univ Southern Calif, Dornsife Coll Letters Arts & Sci, Los Angeles, CA 90007 USA
4.Lenovo Res, Beijing, Peoples R China
5.Chinese Acad Sci, Inst Comp Technol, Beijing, Peoples R China
6.Univ Chinese Acad Sci, Comp Sci & Technol, Beijing, Peoples R China
7.Univ Calif Los Angeles, Elect & Comp Engn, Los Angeles, CA USA
推荐引用方式
GB/T 7714		 Zhang, Yao,Yang, Jiawei,Liu, Yang,et al. Decoupled pyramid correlation network for liver tumor segmentation from CT images[J]. MEDICAL PHYSICS,2022:15.
APA Zhang, Yao.,Yang, Jiawei.,Liu, Yang.,Tian, Jiang.,Wang, Siyun.,...&He, Zhiqiang.(2022).Decoupled pyramid correlation network for liver tumor segmentation from CT images.MEDICAL PHYSICS,15.
MLA Zhang, Yao,et al."Decoupled pyramid correlation network for liver tumor segmentation from CT images".MEDICAL PHYSICS (2022):15.

入库方式： OAI收割

来源：计算技术研究所

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