Abstract
Automatically generating Couinaud segments on liver, a prerequisite for modern surgery of the liver, from computed tomography (CT) volumes is a challenge for the computer-aided diagnosis (CAD). In this paper, we propose a novel global and local contexts UNet (GLC-UNet) for Couinaud segmentation. In this framework, intra-slice features and 3D contexts are effectively probed and jointly optimized for accurate liver and Couinaud segmentation using attention mechanism. We comprehensively evaluate our system performance (\(98.51\%\) in terms of Dice per case on liver segmentation, and \(92.46\%\) on Couinaud segmentation) on the Medical Segmentation Decathlon dataset (task 8, hepatic vessels and tumor) from MICCAI 2018 with our annotated 43, 205 CT slices on liver and Couinaud segmentation. (https://github.com/GLCUnet/dataset).
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A set of CT slices selected from the CT volume.
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Tian, J., Liu, L., Shi, Z., Xu, F. (2019). Automatic Couinaud Segmentation from CT Volumes on Liver Using GLC-UNet. In: Suk, HI., Liu, M., Yan, P., Lian, C. (eds) Machine Learning in Medical Imaging. MLMI 2019. Lecture Notes in Computer Science(), vol 11861. Springer, Cham. https://doi.org/10.1007/978-3-030-32692-0_32
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DOI: https://doi.org/10.1007/978-3-030-32692-0_32
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