A game for crowdsourcing the segmentation of BigBrain data

Arno Klein
2016 Research Ideas and Outcomes  
The BigBrain, a high-resolution 3-D model of a human brain at nearly cellular resolution, is the best brain imaging data set in the world to establish a canonical space at both microscopic and macroscopic resolutions. However, for the cell-stained microstructural data to be truly useful, it needs to be segmented into cytoarchitectonic regions, a challenge no single lab could undertake. The principal aim of this proposal is to crowdsource the segmentation of cytoarchitectonic regions by means of
more » ... a computer game, to transform an arduous, isolated task performed by experts into an engaging, collective activity of nonexperts. The principal aim of this proposal is to crowdsource the segmentation of brain histological data, specifically the cytoarchitectonic regions of the hippocampi of the human brain, by ‡ © Klein A. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. means of a computer game. Currently, only human experts perform reliable cytoarchitectonic labeling at a very slow and small scale, whereas we propose to enlist many human non-experts to engage in a distributed version of this task at a quick and large scale. By turning this arduous, isolated task into an engaging, collective activity, we hope to radically change the way anatomists approach segmentation/labeling. To support this aim, we must (1) prepare expert (gold standard) labels to a subset of the hippocampal sections to evaluate crowdsourced results, and (2) aggregate the crowdsourced results to label the hippocampi. For our first exploratory aim, will explore how our approach generalizes to all other brain regions, and for our second exploratory aim, we will train a supervised learning algorithm on the crowdsourced results and evaluate how closely the automated approach matches human assessments. We will use the BigBrain, a high-resolution 3-D model of a human brain at nearly cellular resolution (20μm isotropic) based on reconstruction of 7,404 histological sections stained for cell bodies. The BigBrain is extremely important to the neuroscience community because it represents whole-brain histological data with accompanying magnetic resonance imaging data. It is the best brain imaging data set in the world to establish a canonical space at both microscopic and macroscopic resolutions. However, for the cellstained microstructural data to be truly useful, it needs to be segmented into cytoarchitectonic regions, which makes it the perfect focus for this project. Labeling of the BigBrain will establish an ex vivo atlas, a common space for neuroimaging data whose labels will provide a consistent, convenient, and meaningful way to communicate, classify, and analyze biomedical research data set in that space. Facilities & Other Resources Sage Bionetworks Laboratory: N/A Clinical: N/A Animal: N/A High Performance Computing Resources: Sage Bionetworks uses a combination of scalable cloud-based storage and analytical computational resources and its own computational facilities. The cloud-based services are procured from Amazon Web services on a fee for service basis and provide a cost-effective solution to variable needs, technology upgrades and support. Sage Bionetworks develops and operates two software as a service platforms, Bridge and Synapse, as resources for the broader scientific community. Both these systems opperate on cloud-based infrastructure. Internal research projects also have access to the Sage Bionetworks high performance computing cluster, maintained through a partnership agreement with the University of Miami. Additional servers used by Sage scientific staff are co-located at the Fred Hutchinson Cancer Research Center computing facilities. All networked file systems, databases, and 2 Klein A
doi:10.3897/rio.2.e8816 fatcat:jmilpqohufdtndugsmdqyp65py