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

Yashvardhan Jain (), Leah L. Godwin, Sripad Joshi, Shriya Mandarapu, Trang Le, Cecilia Lindskog, Emma Lundberg and Katy Börner ()
Additional contact information
Yashvardhan Jain: Indiana University
Leah L. Godwin: Indiana University
Sripad Joshi: Indiana University
Shriya Mandarapu: Indiana University
Trang Le: KTH - Royal Institute of Technology
Cecilia Lindskog: Uppsala University
Emma Lundberg: KTH - Royal Institute of Technology
Katy Börner: Indiana University

Nature Communications, 2023, vol. 14, issue 1, 1-11

Abstract: 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.

Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.nature.com/articles/s41467-023-40291-0 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-40291-0

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-023-40291-0

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().