Department of Intelligent Systems Engineering, Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, 47408, USA.
Department of Pathology, Stanford University School of Medicine, Stanford, CA, 94305, USA.
Commun Biol. 2023 Jul 19;6(1):717. doi: 10.1038/s42003-023-04848-5.
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.
人类生物分子图谱计划（HuBMAP）旨在以细胞水平编制健康成年人体的人类参考图谱（HRA）。对于 HRA 构建具有重要意义的功能组织单位（FTU）。手动分割 FTU 无法扩展；需要高度准确和高性能的开源机器学习算法。我们设计并主办了一场 Kaggle 竞赛，重点是开发此类算法，来自 60 个国家的 1200 个团队参加了竞赛。我们展示了竞赛结果，以及对额外的肾脏和结肠组织数据的获奖算法的扩展分析，并进行了一项初步研究，以了解 FTU 在肾脏中的空间位置和密度。竞赛中的顶级算法 Tom 在扩展研究中表现优于其他算法，同时使用的计算资源更少。Tom 被添加到 HuBMAP 基础设施中，以大规模运行肾脏 FTU 分割，展示了 Kaggle 竞赛在推进研究方面的价值。
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