• KSII Transactions on Internet and Information Systems
    Monthly Online Journal (eISSN: 1976-7277)

Boundary-Aware Dual Attention Guided Liver Segment Segmentation Model

Vol. 16, No. 1, January 31, 2022
10.3837/tiis.2022.01.002, Download Paper (Free):


Accurate liver segment segmentation based on radiological images is indispensable for the preoperative analysis of liver tumor resection surgery. However, most of the existing segmentation methods are not feasible to be used directly for this task due to the challenge of exact edge prediction with some tiny and slender vessels as its clinical segmentation criterion. To address this problem, we propose a novel deep learning based segmentation model, called Boundary-Aware Dual Attention Liver Segment Segmentation Model (BADA). This model can improve the segmentation accuracy of liver segments with enhancing the edges including the vessels serving as segment boundaries. In our model, the dual gated attention is proposed, which composes of a spatial attention module and a semantic attention module. The spatial attention module enhances the weights of key edge regions by concerning about the salient intensity changes, while the semantic attention amplifies the contribution of filters that can extract more discriminative feature information by weighting the significant convolution channels. Simultaneously, we build a dataset of liver segments including 59 clinic cases with dynamically contrast enhanced MRI(Magnetic Resonance Imaging) of portal vein stage, which annotated by several professional radiologists. Comparing with several state-of-the-art methods and baseline segmentation methods, we achieve the best results on this clinic liver segment segmentation dataset, where Mean Dice, Mean Sensitivity and Mean Positive Predicted Value reach 89.01%, 87.71% and 90.67%, respectively.


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Cite this article

[IEEE Style]
X. Jia, C. Qian, Z. Yang, H. Xu, X. Han, H. Ren, X. Wu, B. Ma, D. Yang and H. Min, "Boundary-Aware Dual Attention Guided Liver Segment Segmentation Model," KSII Transactions on Internet and Information Systems, vol. 16, no. 1, pp. 16-37, 2022. DOI: 10.3837/tiis.2022.01.002.

[ACM Style]
Xibin Jia, Chen Qian, Zhenghan Yang, Hui Xu, Xianjun Han, Hao Ren, Xinru Wu, Boyang Ma, Dawei Yang, and Hong Min. 2022. Boundary-Aware Dual Attention Guided Liver Segment Segmentation Model. KSII Transactions on Internet and Information Systems, 16, 1, (2022), 16-37. DOI: 10.3837/tiis.2022.01.002.

[BibTeX Style]
@article{tiis:25244, title="Boundary-Aware Dual Attention Guided Liver Segment Segmentation Model", author="Xibin Jia and Chen Qian and Zhenghan Yang and Hui Xu and Xianjun Han and Hao Ren and Xinru Wu and Boyang Ma and Dawei Yang and Hong Min and ", journal="KSII Transactions on Internet and Information Systems", DOI={10.3837/tiis.2022.01.002}, volume={16}, number={1}, year="2022", month={January}, pages={16-37}}