Abstract
Accurate whole-heart segmentation from multi-modality medical images (MRI, CT) plays an important role in many clinical applications, such as precision surgical planning and improvement of diagnosis and treatment. This paper presents a deeply-supervised 3D U-Net for fully automatic whole-heart segmentation by jointly using the multi-modal MRI and CT images. First, a 3D U-Net is employed to coarsely detect the whole heart and segment its region of interest, which can alleviate the impact of surrounding tissues. Then, we artificially enlarge the training set by extracting different regions of interest so as to train a deep network. We perform voxel-wise whole-heart segmentation with the end-to-end trained deeply-supervised 3D U-Net. Considering that different modality information of the whole heart has a certain complementary effect, we extract multi-modality features by fusing MRI and CT images to define the overall heart structure, and achieve final results. We evaluate our method on cardiac images from the multi-modality whole heart segmentation (MM-WHS) 2017 challenge.
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Acknowledgements
This work was supported by grants from Shenzhen Science and Technology Program (No. JCYJ20160429190300857), China Posdoctoral Science Foundation (2017M622831) and SIAT Innovation Program for Excellent Young Researchers (No. 2017059).
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Tong, Q., Ning, M., Si, W., Liao, X., Qin, J. (2018). 3D Deeply-Supervised U-Net Based Whole Heart Segmentation. In: Pop, M., et al. Statistical Atlases and Computational Models of the Heart. ACDC and MMWHS Challenges. STACOM 2017. Lecture Notes in Computer Science(), vol 10663. Springer, Cham. https://doi.org/10.1007/978-3-319-75541-0_24
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DOI: https://doi.org/10.1007/978-3-319-75541-0_24
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