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Shape-intensity knowledge distillation for robust medical image segmentation

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Abstract

Many medical image segmentation methods have achieved impressive results. Yet, most existing methods do not take into account the shape-intensity prior information. This may lead to implausible segmentation results, in particular for images of unseen datasets. In this paper, we propose a novel approach to incorporate joint shape-intensity prior information into the segmentation network. Specifically, we first train a segmentation network (regarded as the teacher network) on class-wise averaged training images to extract valuable shape-intensity information, which is then transferred to a student segmentation network with the same network architecture as the teacher via knowledge distillation. In this way, the student network regarded as the final segmentation model can effectively integrate the shape-intensity prior information, yielding more accurate segmentation results. Despite its simplicity, experiments on five medical image segmentation tasks of different modalities demonstrate that the proposed Shape-Intensity Knowledge Distillation (SIKD) consistently improves several baseline models (including recent MaxStyle and SAMed) under intra-dataset evaluation, and significantly improves the cross-dataset generalization ability. The source code will be publicly available after acceptance.

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Acknowledgements

This work was supported in part by the National Key Research and Development Program of China (Grant No. 2023YFC2705700), the National Natural Science Foundation of China (Grant Nos. 62222112, 62225113, and 62176186), the Innovative Research Group Project of Hubei Province (Grant No. 2024AFA017), and the CAAI Huawei MindSpore Open Fund.

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Authors and Affiliations

  1. National Engineering Research Center for Multimedia Software, School of Computer Science, Wuhan University, Wuhan, 430072, China

    Wenhui Dong, Bo Du & Yongchao Xu

  2. Institute of Artificial Intelligence, School of Computer Science, Wuhan University, Wuhan, 430072, China

    Wenhui Dong, Bo Du & Yongchao Xu

  3. Hubei Key Laboratory of Multimedia and Network Communication Engineering, Wuhan University, Wuhan, 430072, China

    Wenhui Dong, Bo Du & Yongchao Xu

  4. Medical Artificial Intelligence Research Institute of Renmin Hospital, Wuhan University, Wuhan, 430072, China

    Wenhui Dong, Bo Du & Yongchao Xu

Authors
  1. Wenhui Dong
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  2. Bo Du
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  3. Yongchao Xu
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Correspondence to Yongchao Xu.

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Additional information

Wenhui Dong received the MS degree from the School of Software Engineering, Wuhan University, China in 2020. He is currently pursuing the PhD degree in the School of Computer Science, Wuhan University, China. His main research interests include image segmentation, medical image analysis, and video object segmentation.

Bo Du received the PhD degree in photogrammetry and remote sensing from the State Key Lab of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, China in 2010. He is currently a professor and the dean of School of Computer Science. He is also the director of the National Engineering Research Center for Multimedia Software, Wuhan University, China. His major research interests include machine leanring, computer vision, and image processing. He has more than 80 journal papers published in IEEE TPAMI/TIP/TCYB/TGRS, and IJCV. He serves as associate editor of Neural Networks, Pattern Recognition, and Neurocomputing. He won the Highly Cited researcher (2019/2020/2021/2022) by the Web of Science Group. He also won IEEE Geoscience and Remote Sensing Society 2020 Transactions Prize Paper Award, and IJCAI Distinguished Paper Prize. He regularly serves as senior PC member of IJCAI and AAAI.

Yongchao Xu received the master degree in electronics and signal processing at Université Paris Sud, France in 2010 and the PhD degree in image processing at Université Paris Est, France in 2013. He is currently a professor with the School of Computer Science, Wuhan University, China. His research interests include image segmentation, medical image analysis, and cross-domain generalization for deep learning. He has published more than 40 scientific papers, such as IEEE TPAMI, IJCV, IEEE TIP, CVPR, ICCV, ECCV, and MICCAI. He serves as associate editor of Pattern Recognition, Image and Vision Computing, and young associate editor of Frontiers of Computer Science.

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Dong, W., Du, B. & Xu, Y. Shape-intensity knowledge distillation for robust medical image segmentation. Front. Comput. Sci. 19, 199705 (2025). https://doi.org/10.1007/s11704-024-40462-2

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