ArticleNature communications2023
Segmenting functional tissue units across human organs using community-driven development of generalizable machine learning algorithms.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
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Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Vasculature segmentation in 3D hierarchical phase-contrast tomography images of human kidneys.Nature communications · 2026Pooled it
- Cell Type Populations for 3D Anatomical Structures of the Human Reference Atlas.Scientific data · 2026Article
- The Human Organ Atlas.Science advances · 2026Article
- Label Noise in Pathological Segmentation Is Overlooked, Leading to Potential Overestimation of Artificial Intelligence.Cancer science · 2026Article
- Integrative genomic profiling identifies MLPH as a candidate gene in prostate cancer.Frontiers in medicine · 2026Article
- Spatial Dependence and Heterogeneity in Molecular Imaging: Moran Quadrant Maps Enable Advanced Spatial-Statistical Analysis.bioRxiv : the preprint server for biology · 2025Article
- Constructing and Using Cell Type Populations of the Human Reference Atlas.bioRxiv : the preprint server for biology · 2025Article
- TrimNN: characterizing cellular community motifs for studying multicellular topological organization in complex tissues.Nature communications · 2025Article
- The Human Organ Atlas.bioRxiv : the preprint server for biology · 2025Article
- MiroSCOPE: An AI-driven digital pathology platform for annotating functional tissue units.bioRxiv : the preprint server for biology · 2025Article
- Construction, Deployment, and Usage of the Human Reference Atlas Knowledge Graph.Scientific data · 2025Article
- Human BioMolecular Atlas Program (HuBMAP): 3D Human Reference Atlas construction and usage.Nature methods · 2025Article
- Machine learning in healthcare citizen science: A scoping review.International journal of medical informatics · 2025Article
- Construction, Deployment, and Usage of the Human Reference Atlas Knowledge Graph for Linked Open Data.bioRxiv : the preprint server for biology · 2025Article
- Functional tissue units in the Human Reference Atlas.Nature communications · 2025Article
- Deep learning for 3D vascular segmentation in hierarchical phase contrast tomography: a case study on kidney.Scientific reports · 2024Article
- Human BioMolecular Atlas Program (HuBMAP): 3D Human Reference Atlas Construction and Usage.bioRxiv : the preprint server for biology · 2024Article
- Deep Learning for 3D Vascular Segmentation in Phase Contrast Tomography.Research square · 2024Article
- Functional Tissue Units in the Human Reference Atlas.bioRxiv : the preprint server for biology · 2023Article
- Segmenting functional tissue units across human organs using community-driven development of generalizable machine learning algorithms.Nature communications · 2023Article
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8 authors.
Funding
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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.