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Double-Uncertainty Guided Spatial and Temporal Consistency Regularization Weighting for Learning-Based Abdominal Registration

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Abstract

In order to tackle the difficulty associated with the ill-posed nature of the image registration problem, regularization is often used to constrain the solution space. For most learning-based registration approaches, the regularization usually has a fixed weight and only constrains the spatial transformation. Such convention has two limitations: (i) Besides the laborious grid search for the optimal fixed weight, the regularization strength of a specific image pair should be associated with the content of the images, thus the “one value fits all” training scheme is not ideal; (ii) Only spatially regularizing the transformation may neglect some informative clues related to the ill-posedness. In this study, we propose a mean-teacher based registration framework, which incorporates an additional temporal consistency regularization term by encouraging the teacher model’s prediction to be consistent with that of the student model. More importantly, instead of searching for a fixed weight, the teacher enables automatically adjusting the weights of the spatial regularization and the temporal consistency regularization by taking advantage of the transformation uncertainty and appearance uncertainty. Extensive experiments on the challenging abdominal CT-MRI registration show that our training strategy can promisingly advance the original learning-based method in terms of efficient hyperparameter tuning and a better tradeoff between accuracy and smoothness.

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Acknowledgement

This research was done with Tencent Healthcare (Shenzhen) Co., LTD and Tencent Jarvis Lab and supported by General Research Fund from Research Grant Council of Hong Kong (No. 14205419) and the Scientific and Technical Innovation 2030-“New Generation Artificial Intelligence” Project (No. 2020AAA0104100).

Author information

Authors and Affiliations

  1. Department of Biomedical Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong, China

    Zhe Xu & Raymond Kai-yu Tong

  2. Tencent Healthcare Co., Jarvis Lab, Shenzhen, China

    Donghuan Lu & Yefeng Zheng

  3. Brigham and Women’s Hospital, Harvard Medical School, Boston, USA

    Jie Luo, Sarah Frisken, Jayender Jagadeesan & William M. Wells III

  4. Department of Automation, Tsinghua University, Beijing, China

    Jiangpeng Yan

  5. Shenzhen International Graduate School, Tsinghua University, Shenzhen, China

    Xiu Li

Authors
  1. Zhe Xu

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  2. Jie Luo

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  3. Donghuan Lu

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  4. Jiangpeng Yan

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  5. Sarah Frisken

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  6. Jayender Jagadeesan

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  7. William M. Wells III

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  8. Xiu Li

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  9. Yefeng Zheng

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  10. Raymond Kai-yu Tong

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Corresponding author

Correspondence toRaymond Kai-yu Tong.

Editor information

Editors and Affiliations

  1. Rochester Institute of Technology, Rochester, NY, USA

    Linwei Wang

  2. Chinese University of Hong Kong, Hong Kong, Hong Kong

    Qi Dou

  3. University of Virginia, Charlottesville, VA, USA

    P. Thomas Fletcher

  4. National Center for Tumor Diseases (NCT/UCC), Dresden, Germany

    Stefanie Speidel

  5. Case Western Reserve University, Cleveland, OH, USA

    Shuo Li

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Xu, Z.et al. (2022). Double-Uncertainty Guided Spatial and Temporal Consistency Regularization Weighting for Learning-Based Abdominal Registration. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2022. MICCAI 2022. Lecture Notes in Computer Science, vol 13436. Springer, Cham. https://doi.org/10.1007/978-3-031-16446-0_2

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