Segmentation of human functional tissue units in support of a Human Reference Atlas. Jain, Y., Godwin, L. L., Ju, Y., Sood, N., Quardokus, E. M., Bueckle, A., Longacre, T., Horning, A., Lin, Y., Esplin, E. D., Hickey, J. W., Snyder, M. P., Patterson, N. H., Spraggins, J. M., & Börner, K. Communications Biology, 6:717, July, 2023. ![Segmentation of human functional tissue units in support of a Human Reference Atlas [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex The Human BioMolecular Atlas Program (HuBMAP) aims to compile a Human Reference Atlas (HRA) for the healthy adult body at the cellular level. Functional tissue units (FTUs), relevant for HRA construction, are of pathobiological significance. Manual segmentation of FTUs does not scale; highly accurate and performant, open-source machine-learning algorithms are needed. We designed and hosted a Kaggle competition that focused on development of such algorithms and 1200 teams from 60 countries participated. We present the competition outcomes and an expanded analysis of the winning algorithms on additional kidney and colon tissue data, and conduct a pilot study to understand spatial location and density of FTUs across the kidney. The top algorithm from the competition, Tom, outperforms other algorithms in the expanded study, while using fewer computational resources. Tom was added to the HuBMAP infrastructure to run kidney FTU segmentation at scale—showcasing the value of Kaggle competitions for advancing research., Results from a Kaggle competition and expanded analysis of the winning algorithms are presented for segmentation of functional tissue units as part of the Human BioMolecular Atlas Program (HuBMAP).
@article{jain_segmentation_2023,
title = {Segmentation of human functional tissue units in support of a {Human} {Reference} {Atlas}},
volume = {6},
issn = {2399-3642},
url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10356924/},
doi = {10.1038/s42003-023-04848-5},
abstract = {The Human BioMolecular Atlas Program (HuBMAP) aims to compile a Human Reference Atlas (HRA) for the healthy adult body at the cellular level. Functional tissue units (FTUs), relevant for HRA construction, are of pathobiological significance. Manual segmentation of FTUs does not scale; highly accurate and performant, open-source machine-learning algorithms are needed. We designed and hosted a Kaggle competition that focused on development of such algorithms and 1200 teams from 60 countries participated. We present the competition outcomes and an expanded analysis of the winning algorithms on additional kidney and colon tissue data, and conduct a pilot study to understand spatial location and density of FTUs across the kidney. The top algorithm from the competition, Tom, outperforms other algorithms in the expanded study, while using fewer computational resources. Tom was added to the HuBMAP infrastructure to run kidney FTU segmentation at scale—showcasing the value of Kaggle competitions for advancing research., Results from a Kaggle competition and expanded analysis of the winning algorithms are presented for segmentation of functional tissue units as part of the Human BioMolecular Atlas Program (HuBMAP).},
urldate = {2023-07-25},
journal = {Communications Biology},
author = {Jain, Yashvardhan and Godwin, Leah L. and Ju, Yingnan and Sood, Naveksha and Quardokus, Ellen M. and Bueckle, Andreas and Longacre, Teri and Horning, Aaron and Lin, Yiing and Esplin, Edward D. and Hickey, John W. and Snyder, Michael P. and Patterson, Nathan Heath and Spraggins, Jeffrey M. and Börner, Katy},
month = jul,
year = {2023},
pmid = {37468557},
pmcid = {PMC10356924},
pages = {717},
}
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Manual segmentation of FTUs does not scale; highly accurate and performant, open-source machine-learning algorithms are needed. We designed and hosted a Kaggle competition that focused on development of such algorithms and 1200 teams from 60 countries participated. We present the competition outcomes and an expanded analysis of the winning algorithms on additional kidney and colon tissue data, and conduct a pilot study to understand spatial location and density of FTUs across the kidney. The top algorithm from the competition, Tom, outperforms other algorithms in the expanded study, while using fewer computational resources. Tom was added to the HuBMAP infrastructure to run kidney FTU segmentation at scale—showcasing the value of Kaggle competitions for advancing research., Results from a Kaggle competition and expanded analysis of the winning algorithms are presented for segmentation of functional tissue units as part of the Human BioMolecular Atlas Program (HuBMAP).","urldate":"2023-07-25","journal":"Communications Biology","author":[{"propositions":[],"lastnames":["Jain"],"firstnames":["Yashvardhan"],"suffixes":[]},{"propositions":[],"lastnames":["Godwin"],"firstnames":["Leah","L."],"suffixes":[]},{"propositions":[],"lastnames":["Ju"],"firstnames":["Yingnan"],"suffixes":[]},{"propositions":[],"lastnames":["Sood"],"firstnames":["Naveksha"],"suffixes":[]},{"propositions":[],"lastnames":["Quardokus"],"firstnames":["Ellen","M."],"suffixes":[]},{"propositions":[],"lastnames":["Bueckle"],"firstnames":["Andreas"],"suffixes":[]},{"propositions":[],"lastnames":["Longacre"],"firstnames":["Teri"],"suffixes":[]},{"propositions":[],"lastnames":["Horning"],"firstnames":["Aaron"],"suffixes":[]},{"propositions":[],"lastnames":["Lin"],"firstnames":["Yiing"],"suffixes":[]},{"propositions":[],"lastnames":["Esplin"],"firstnames":["Edward","D."],"suffixes":[]},{"propositions":[],"lastnames":["Hickey"],"firstnames":["John","W."],"suffixes":[]},{"propositions":[],"lastnames":["Snyder"],"firstnames":["Michael","P."],"suffixes":[]},{"propositions":[],"lastnames":["Patterson"],"firstnames":["Nathan","Heath"],"suffixes":[]},{"propositions":[],"lastnames":["Spraggins"],"firstnames":["Jeffrey","M."],"suffixes":[]},{"propositions":[],"lastnames":["Börner"],"firstnames":["Katy"],"suffixes":[]}],"month":"July","year":"2023","pmid":"37468557","pmcid":"PMC10356924","pages":"717","bibtex":"@article{jain_segmentation_2023,\n\ttitle = {Segmentation of human functional tissue units in support of a {Human} {Reference} {Atlas}},\n\tvolume = {6},\n\tissn = {2399-3642},\n\turl = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10356924/},\n\tdoi = {10.1038/s42003-023-04848-5},\n\tabstract = {The Human BioMolecular Atlas Program (HuBMAP) aims to compile a Human Reference Atlas (HRA) for the healthy adult body at the cellular level. Functional tissue units (FTUs), relevant for HRA construction, are of pathobiological significance. Manual segmentation of FTUs does not scale; highly accurate and performant, open-source machine-learning algorithms are needed. We designed and hosted a Kaggle competition that focused on development of such algorithms and 1200 teams from 60 countries participated. We present the competition outcomes and an expanded analysis of the winning algorithms on additional kidney and colon tissue data, and conduct a pilot study to understand spatial location and density of FTUs across the kidney. The top algorithm from the competition, Tom, outperforms other algorithms in the expanded study, while using fewer computational resources. 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