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The Pilot Challenges sub-project of CTIIP seeks to will make available a set of integrated data from TCIA and TCGA for publicly available to researchers who will participate in three complementary "pilot challenge" projects from . These pilot challenges proactively address research questions that compare the decision support systems for clinical imaging, co-clinical imaging, and digital pathology. As opposed to a more rigorous "grand" challenge, each pilot challenge will function as a proof of concept to learn how to scale challenges up in the future. Each challenge will use the informatics infrastructure created in the Digital Pathology and Integrated Query System sub-project and allow participants to validate and share algorithms on a software clearinghouse platform such as HUBZero.

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• Clinical Imaging: QIN image data for several modalities/organ systems are already hosted on TCIA. Pilot challenge projects are being explored for X-ray CT, DWI MRI and PET CT similar to the HUBzero pilot CT challenge project.
• Pre-clinical / Co-clinical Imaging: Leveraging the Mouse Models of Human Cancer Consortium (MHHCC) Glioblastoma co-clinical trials with associated ’omics data sets from the Human Brain Consortium. This proof of concept will focus on bringing together ‘omics and imaging data into a single platform.
• Digital Pathology Clinical Support: Leveraging Aims1-3 develop Develop open source image analysis algorithms which that complement ‘omics data sets and provide additional decision support.

and support precision medicine and clinical decision making tools, including correlation of imaging phenotypes with genomics signatures.

develop knowledge extraction tools

Medical Imaging Challenge Infrastructure (MedICI), a system to support medical imaging challenges.

Comparing Algorithms to Ground Truth

The tool used to display the markup and annotations (for the pathology images) is caMicroscope. There will be a challenge in which animal model data will be used. Give people images they have never seen before and develop algorithms (like to circle all the nuclei). Ground truth decided by a pathologist and a radiologist. The algorithm that comes closest to ground truth is the winner.

Notes

and support precision medicine and clinical decision making tools, including correlation of imaging phenotypes with genomics signatures.

develop knowledge extraction tools

Medical Imaging Challenge Infrastructure (MedICI), a system to support medical imaging challenges.and comparing the decision support systems for clinical imaging, co-clinical imaging, and digital pathology,

Medical Image Computational and computer-assisted Intervention: MICCAI

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Document the approach, technology, application to do a MICCAI challenge the way Jaysharee does it. See their order of march.

Challenges: read one-page document. We want to use pathology images in the challenges. The tool used to display the markup and annotations (for the pathology images) is caMicroscope. There will be a challenge in which animal model data will be used. Give people images they have never seen before and develop algorithms (like to circle all the nuclei). Ground truth decided by a pathologist and a radiologist. The algorithm that comes closest to ground truth is the winner.

Compare the decision support systems for three imaging research domains: Clinical Imaging, Pre-clinical Imaging, and Digital Pathology

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and develop algorithms (like to circle all the nuclei). Ground truth decided by a pathologist and a radiologist. The algorithm that comes closest to ground truth is the winner.

Challenge Management System, MedICI

Jaysharee's program: Medical Imaging Challenge Infrastructure: MedICI

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