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Segmenting functional tissue units across human organs using community-driven development of generalizable machine learning algorithms

Author

Listed:
  • Yashvardhan Jain

    (Indiana University)

  • Leah L. Godwin

    (Indiana University)

  • Sripad Joshi

    (Indiana University)

  • Shriya Mandarapu

    (Indiana University)

  • Trang Le

    (KTH - Royal Institute of Technology
    Stanford University)

  • Cecilia Lindskog

    (Uppsala University)

  • Emma Lundberg

    (KTH - Royal Institute of Technology
    Stanford University
    Stanford University
    Chan Zuckerberg Biohub)

  • Katy Börner

    (Indiana University)

Abstract

The development of a reference atlas of the healthy human body requires automated image segmentation of major anatomical structures across multiple organs based on spatial bioimages generated from various sources with differences in sample preparation. We present the setup and results of the Hacking the Human Body machine learning algorithm development competition hosted by the Human Biomolecular Atlas (HuBMAP) and the Human Protein Atlas (HPA) teams on the Kaggle platform. We create a dataset containing 880 histology images with 12,901 segmented structures, engaging 1175 teams from 78 countries in community-driven, open-science development of machine learning models. Tissue variations in the dataset pose a major challenge to the teams which they overcome by using color normalization techniques and combining vision transformers with convolutional models. The best model will be productized in the HuBMAP portal to process tissue image datasets at scale in support of Human Reference Atlas construction.

Suggested Citation

  • Yashvardhan Jain & Leah L. Godwin & Sripad Joshi & Shriya Mandarapu & Trang Le & Cecilia Lindskog & Emma Lundberg & Katy Börner, 2023. "Segmenting functional tissue units across human organs using community-driven development of generalizable machine learning algorithms," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-40291-0
    DOI: 10.1038/s41467-023-40291-0
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    References listed on IDEAS

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