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Extremely Weakly-Supervised Blood Vessel Segmentation with Physiologically Based Synthesis and Domain Adaptation

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Medical Image Learning with Limited and Noisy Data (MILLanD 2023)

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Abstract

Accurate analysis and modeling of renal functions require a precise segmentation of the renal blood vessels. Micro-CT scans provide image data at higher resolutions, making deeper vessels near the renal cortex visible. Although deep-learning-based methods have shown state-of-the-art performance in automatic blood vessel segmentations, they require a large amount of labeled training data. However, voxel-wise labeling in micro-CT scans is extremely time-consuming, given the huge volume sizes. To mitigate the problem, we simulate synthetic renal vascular trees physiologically while generating corresponding scans of the simulated trees by training a generative model on unlabeled scans. This enables the generative model to learn the mapping implicitly without the need for explicit functions to emulate the image acquisition process. We further propose an additional segmentation branch over the generative model trained on the generated scans. We demonstrate that the model can directly segment blood vessels on real scans and validate our method on both 3D micro-CT scans of rat kidneys and a proof-of-concept experiment on 2D retinal images. Code and 3D results are available at (https://github.com/diku-dk/RenalVesselSeg).

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Authors and Affiliations

  1. Department of Computer Science, University of Copenhagen, Copenhagen, Denmark

    Peidi Xu, Kenny Erleben & Sune Darkner

  2. Department of Biomedical Sciences, University of Copenhagen, Copenhagen, Denmark

    Blaire Lee, Olga Sosnovtseva & Charlotte Mehlin Sørensen

Authors
  1. Peidi Xu
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  2. Blaire Lee
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  3. Olga Sosnovtseva
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  4. Charlotte Mehlin Sørensen
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  5. Kenny Erleben
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  6. Sune Darkner
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Corresponding author

Correspondence to Peidi Xu .

Editor information

Editors and Affiliations

  1. National Library of Medicine, National Institutes of Health, Bethesda, MD, USA

    Zhiyun Xue

  2. National Library of Medicine, National Institutes of Health, Bethesda, MD, USA

    Sameer Antani

  3. National Library of Medicine, National Institutes of Health, Bethesda, MD, USA

    Ghada Zamzmi

  4. National Library of Medicine, National Institutes of Health, Bethesda, MD, USA

    Feng Yang

  5. National Library of Medicine, National Institutes of Health, Bethesda, MD, USA

    Sivaramakrishnan Rajaraman

  6. College of Information Sciences and Technology, Penn State University, University Park, PA, USA

    Sharon Xiaolei Huang

  7. Sheikh Zayed Institute for Pediatric Surgical Innovation, Children’s National Hospital, Washington, DC, USA

    Marius George Linguraru

  8. National Library of Medicine, National Institutes of Health, Bethesda, MD, USA

    Zhaohui Liang

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Xu, P., Lee, B., Sosnovtseva, O., Sørensen, C.M., Erleben, K., Darkner, S. (2023). Extremely Weakly-Supervised Blood Vessel Segmentation with Physiologically Based Synthesis and Domain Adaptation. In: Xue, Z., et al. Medical Image Learning with Limited and Noisy Data. MILLanD 2023. Lecture Notes in Computer Science, vol 14307. Springer, Cham. https://doi.org/10.1007/978-3-031-44917-8_18

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  • DOI: https://doi.org/10.1007/978-3-031-44917-8_18

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  • Online ISBN: 978-3-031-44917-8

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