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

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 showcase how 1,175 teams from 78 countries engaged in community-driven, open-science code development that resulted in machine learning models which successfully segment anatomical structures across five organs using histology images from two consortia and that will be productized in the HuBMAP data portal to process large datasets at scale in support of Human Reference Atlas construction. We discuss the benchmark data created for the competition, major challenges faced by the participants, and the winning models and strategies.

Human BioMolecular Atlas Program (HuBMAP): 3D Human Reference Atlas Construction and Usage

“…Functional tissue unit segmentation algorithms for histology data 81,82 and hierarchical cell community analysis 74 for paired spatial data (see Methods for both) can be combined to analyze and communicate the structure and function of FTUs across scales. FTU segmentation algorithms are run as part of the standard HuBMAP workflows (currently limited to glomeruli in kidneys, soon to be expanded to crypts in the large intestine and white pulp in spleen).…”

Functional Tissue Units in the Human Reference Atlas

“…Within the HRA effort, we are using this catalog of FTUs for several use cases. For example, we ran Kaggle competitions to develop scalable and generalizable segmentation code to identify FTUs in images 2,14 . We use the segmentation code to automatically count the number of FTUs per unit area and to compute general biomarker expression values for genes, proteins, lipids, and metabolites.…”

“…The human body serves as the root node in the center, the largest anatomical structures (organs) are placed further out, and smaller sub-structures and tissue types of branch outwards from the organs' leaf nodes denoting cell types. (2) The second graph contains all the blood vessels in the HRA, with the chambers of the heart in the center, and increasing smaller vessels more distal to the heart again branching outwards from the center. Nodes in the two radial tree graphs meet at points where the HRA indicates a vessel supplies or drains the corresponding anatomical structure.…”

“…Therefore, tissues and organs in the body consist of many repeating FTUs to perform the overall function of the organ. We previously generalized this original definition by describing an FTU as "the smallest tissue organization that performs a unique physiologic function and is replicated multiple times in a whole organ" 2 . Here, we extend the definition of FTUs by allowing them to form a nested hierarchy to accomplish various functions.…”

“…In this perspective, we present a catalog of the first 22 FTUs for the HRA. The current version of this atlas contains (1) the length and diameter of each FTU; (2) the vasculature pathways that connect FTUs to the heart and amongst each other; (3) the cell types within each FTU; and (4) 2D illustrations of each FTU showing their prototypical cell types, cell shapes, sizes, and spatial arrangement. We map all anatomical structures to their corresponding Uber-anatomy Ontology (UBERON) ID 3 and all cell types to their Cell Ontology (CL) ID 4 to enable linkage to other datasets.…”