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Three Uses of One Neural Network: Automatic Segmentation of Kidney Tumor and Cysts Based on 3D U-Net

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Book cover Kidney and Kidney Tumor Segmentation (KiTS 2021)

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Abstract

Medical image processing plays an increasingly important role in clinical diagnosis and treatment. Using the results of kidney CT image segmentation for three-dimensional reconstruction is an intuitive and accurate method for diagnosis. In this paper, we propose a three-step automatic segmentation method for kidney, tumors and cysts, including roughly segmenting the kidney and tumor from low-resolution CT, locating each kidney and fine segmenting the kidney, and finally extracting the tumor and cyst from the segmented kidney. The results show that the average dice of our method for kidney, tumor and cysts is about 0.93, 0.57, 0.73.

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Acknowledgements

Junchen Wang was funded by National Key R&D Program of China (2017YFB1303004) and National Natural Science Foundation of China (61701014, 61911540075).

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Authors and Affiliations

  1. North China Research Institute of Electro-Optics, Beijing, 100015, China

    Yi Lv

  2. School of Mechanical Engineering and Automation, Beihang University, Beijing, 100191, China

    Yi Lv & Junchen Wang

  3. Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, China

    Junchen Wang

Authors
  1. Yi Lv
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  2. Junchen Wang
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Corresponding author

Correspondence to Junchen Wang .

Editor information

Editors and Affiliations

  1. University of Minnesota, Minneapolis, MN, USA

    Nicholas Heller

  2. German Cancer Research Center (DKFZ), Heidelberg, Germany

    Fabian Isensee

  3. German Cancer Research Center (DKFZ), Heidelberg, Germany

    Darya Trofimova

  4. University of Minnesota, Minneapolis, MN, USA

    Resha Tejpaul

  5. University of Minnesota, Minneapolis, MN, USA

    Nikolaos Papanikolopoulos

  6. Cleveland Clinic, Cleveland, OH, USA

    Christopher Weight

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Lv, Y., Wang, J. (2022). Three Uses of One Neural Network: Automatic Segmentation of Kidney Tumor and Cysts Based on 3D U-Net. In: Heller, N., Isensee, F., Trofimova, D., Tejpaul, R., Papanikolopoulos, N., Weight, C. (eds) Kidney and Kidney Tumor Segmentation. KiTS 2021. Lecture Notes in Computer Science, vol 13168. Springer, Cham. https://doi.org/10.1007/978-3-030-98385-7_6

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  • DOI: https://doi.org/10.1007/978-3-030-98385-7_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-98384-0

  • Online ISBN: 978-3-030-98385-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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