Abstract
Segmentation of the kidney and kidney tumors using computed tomography (CT) is a crucial step in related surgical procedures. Although many deep learning models have been constructed to solve this problem, most of them ignore the boundary information. In this paper, we propose a boundary-aware network (BA-Net) for kidney and kidney tumor segmentation. This model consists of a shared 3D encoder, a 3D boundary decoder, and a 3D segmentation decoder. In contrast to existing boundary-involved methods, we first introduce the skip connections from the boundary decoder to the segmentation decoder, incorporating the boundary prior as the attention that indicates the error-prone regions into the segmentation process, and then define the consistency loss to push both decoders towards producing the same result. Besides, we also use the strategies of multi-scale input and deep supervision to extract hierarchical structural information, which can alleviate the issues caused by variable tumor sizes. We evaluated the proposed BA-Net on the kidney tumor segmentation challenge (KiTS19) dataset. The results suggest that the boundary decoder and consistency loss used in our model are effective and the BA-Net is able to produce relatively accurate segmentation of the kidney and kidney tumors.
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Acknowledgment
This work was supported in part by the Science and Technology Innovation Committee of Shenzhen Municipality, China, under Grants JCYJ20180306171334997, in part by the National Natural Science Foundation of China under Grants 61771397, and in part by the Project for Graduate Innovation team of Northwestern Polytechnical University.
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Hu, S., Zhang, J., Xia, Y. (2020). Boundary-Aware Network for Kidney Tumor Segmentation. In: Liu, M., Yan, P., Lian, C., Cao, X. (eds) Machine Learning in Medical Imaging. MLMI 2020. Lecture Notes in Computer Science(), vol 12436. Springer, Cham. https://doi.org/10.1007/978-3-030-59861-7_20
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DOI: https://doi.org/10.1007/978-3-030-59861-7_20
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