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Learning Incrementally to Segment Multiple Organs in a CT Image

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Abstract

There exists a large number of datasets for organ segmentation, which are partially annotated and sequentially constructed. A typical dataset is constructed at a certain time by curating medical images and annotating the organs of interest. In other words, new datasets with annotations of new organ categories are built over time. To unleash the potential behind these partially labeled, sequentially-constructed datasets, we propose to incrementally learn a multi-organ segmentation model. In each incremental learning (IL) stage, we lose the access to previous data and annotations, whose knowledge is assumingly captured by the current model, and gain the access to a new dataset with annotations of new organ categories, from which we learn to update the organ segmentation model to include the new organs. While IL is notorious for its ‘catastrophic forgetting’ weakness in the context of natural image analysis, we experimentally discover that such a weakness mostly disappears for CT multi-organ segmentation. To further stabilize the model performance across the IL stages, we introduce alight memory module and some loss functions to restrain the representation of different categories in feature space, aggregating feature representation of the same class and separating feature representation of different classes. Extensive experiments on five open-sourced datasets are conducted to illustrate the effectiveness of our method.

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Author information

Authors and Affiliations

  1. Center for Medical Imaging, Robotics, Analytic Computing and Learning (MIRACLE), School of Biomedical Engineering and Suzhou Institute for Advanced Research, University of Science and Technology of China, Suzhou, China

    Pengbo Liu & S. Kevin Zhou

  2. Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS, Beijing, China

    Pengbo Liu, Li Xiao & S. Kevin Zhou

  3. The First Affiliated Hospital of Anhui Medical University, Anhui, China

    Xia Wang, Mengsi Fan, Hongli Pan, Minmin Yin, Xiaohong Zhu, Dandan Du, Xiaoying Zhao & Xingwang Wu

  4. Huawei Cloud Computing Technology Co. Ltd., Dongguan, China

    Lian Ding

Authors
  1. Pengbo Liu

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  2. Xia Wang

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  3. Mengsi Fan

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  4. Hongli Pan

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  5. Minmin Yin

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  6. Xiaohong Zhu

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  7. Dandan Du

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  8. Xiaoying Zhao

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  9. Li Xiao

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  10. Lian Ding

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  11. Xingwang Wu

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  12. S. Kevin Zhou

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Corresponding author

Correspondence toS. Kevin Zhou.

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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Cite this paper

Liu, P.et al. (2022). Learning Incrementally to Segment Multiple Organs in a CT Image. 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 13434. Springer, Cham. https://doi.org/10.1007/978-3-031-16440-8_68

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JPY 6291
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