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International Conference on Medical Image Computing and Computer-Assisted Intervention

MICCAI 2020: Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 pp 518–528Cite as

Multi-stream Progressive Up-Sampling Network for Dense CT Image Reconstruction

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Part of the Lecture Notes in Computer Science book series (LNIP,volume 12266)

Abstract

Pulmonary computerized tomography (CT) images with small slice thickness (thin) is very helpful in clinical practice due to its high resolution for precise diagnosis. However, there are still a lot of CT images with large slice thickness (thick) because of the benefits of storage-saving and short taking time. Therefore, it is necessary to build a pipeline to leverage advantages from both thin and thick slices. In this paper, we try to generate thin slices from the thick ones, in order to obtain high quality images with a low storage requirement. Our method is implemented in an encoder-decoder manner with a proposed progressive up-sampling module to exploit enough information for reconstruction. To further lower the difficulty of the task, a multi-stream architecture is established to separately learn the inner- and outer-lung regions. During training, a contrast-aware loss and feature matching loss are designed to capture the appearance of lung markings and reduce the influence of noise. To verify the performance of the proposed method, a total of 880 pairs of CT images with both thin and thick slices are collected. Ablation study demonstrates the effectiveness of each component of our method and higher performance is obtained compared with previous work. Furthermore, three radiologists are required to detect pulmonary nodules in raw thick slices and the generated thin slices independently, the improvement in both sensitivity and precision shows the potential value of the proposed method in clinical applications.

Keywords

  • Deep learning
  • Image reconstruction
  • Pulmonary computed tomography

Q. Liu and Z. Zhou—Equal contribution.

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  • DOI: 10.1007/978-3-030-59725-2_50
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Acknowledgment

This work was supported in part by National Key Research and Development Program of China (MOST-2018AAA0102004, 2019YFC0118100), Beijing Municipal Science and Technology Planning Project (Grant No. Z201100005620008), National Natural Science Foundation of China (NSFC-61625201), and Hong Kong Research Grants Council through Research Impact Fund under Grant R-5001-18.

Author information

Authors and Affiliations

  1. Center for Data Science, Peking University, Beijing, China

    Qiuyue Liu

  2. Advanced Institute of Information Technology, Peking University, Hangzhou, China

    Qiuyue Liu & Yizhou Wang

  3. Deepwise AI Lab, Beijing, China

    Feng Liu & Yizhou Yu

  4. Department of Radiology, Wuxi People’s Hospital, Nanjing Medical University, Wuxi, China

    Xiangming Fang

  5. The University of Hong Kong, Pokfulam, Hong Kong, China

    Yizhou Yu

  6. Center on Frontiers of Computing Studies, Peking University, Beijing, China

    Yizhou Wang

  7. Computer Science Department, Peking University, Beijing, China

    Zhen Zhou & Yizhou Wang

Authors
  1. Qiuyue Liu
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  2. Zhen Zhou
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  3. Feng Liu
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  4. Xiangming Fang
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  5. Yizhou Yu
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  6. Yizhou Wang
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Corresponding authors

Correspondence to Zhen Zhou or Yizhou Yu .

Editor information

Editors and Affiliations

  1. University of Toronto, Toronto, ON, Canada

    Prof. Anne L. Martel

  2. The University of British Columbia, Vancouver, BC, Canada

    Purang Abolmaesumi

  3. University College London, London, UK

    Danail Stoyanov

  4. École Centrale de Nantes, Nantes, France

    Diana Mateus

  5. EURECOM, Biot, France

    Maria A. Zuluaga

  6. Chinese Academy of Sciences, Beijing, China

    S. Kevin Zhou

  7. Sorbonne University, Paris, France

    Daniel Racoceanu

  8. The Hebrew University of Jerusalem, Jerusalem, Israel

    Prof. Leo Joskowicz

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Liu, Q., Zhou, Z., Liu, F., Fang, X., Yu, Y., Wang, Y. (2020). Multi-stream Progressive Up-Sampling Network for Dense CT Image Reconstruction. In: , et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. MICCAI 2020. Lecture Notes in Computer Science(), vol 12266. Springer, Cham. https://doi.org/10.1007/978-3-030-59725-2_50

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  • DOI: https://doi.org/10.1007/978-3-030-59725-2_50

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