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The Pilot Challenges sub-project of CTIIP will make a set of integrated data from TCIA and TCGA publicly available to researchers who will participate in three complementary "pilot challenge" projects. 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.
A team from Massachusetts General Hospital will guide the pilot challenges, using the Medical Imaging Challenge Infrastructure (MedICI), a system that supports medical imaging challenges.
The pilot challenges are as follows:
| Discipline | Pilot Challenge |
|---|---|
| Clinical Imaging | 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 | Leverage 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 | Develop open source image analysis algorithms that complement ‘omics omics data sets and provide additional decision support. |
Comparing Algorithms to Ground Truth
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Before a challenge begins, the Pilot Challenge team will work with a pathologist and a radiologist to determine the ground truth for a particular image. Participants will then analyze images they have never seen before and develop algorithms
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to accomplish a certain task. The algorithm that comes closest to ground truth is the winner of the challenge.
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.
Medical Image Computational and computer-assisted Intervention: MICCAI
Interventions in tumors, cardiology, etc that are image-based
Mass General will guide the pilots
Ground truth: find the compatibility of the informatics that we need to run pilots. Take images out of TCIA, CGA, clinical data and compare them.
Jasharee doing MICCAI Challenge in Munich. Segmentation of nuclear imaging in pathology. Combined radiology and pathology classification.
Want to be able to say that these informatics allow us to compare the pathology, rad, co-clinical findings.
Document the approach, technology, application to do a MICCAI challenge the way Jaysharee does it.
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.
Challenge Management System, MedICI
Jaysharee's program: Medical Imaging Challenge Infrastructure: MedICI
- Based on open-source CodaLab
- ePAD (created by Daniel Rubin's group at Stanford): tool for annotating images, creates AIM images
- caMicroscope
http://miccai.cloudapp.net:8000/competitions/28
- Competition #1: MICCAI challenge has a training phase where they train their algorithms. A test phase where they run their algorithms on images they have never seen before. They are compared to the ground truth that is determined beforehand. caMicroscope is used to see what is there before and to visualize the results. Overlap/completeness match determines the winner.
- Competition #2: They are given slides.
From PPT: Use titles of slides
Setting up a competition by an organizer. Organizer creates competition bundle.
Can go to cancerimagingarchive.net and create shared lists. Shared lists are pulled into CodaLab. That is how they get the test and training data.
Next is to create ground truth.
Regions of interest in a tumor for annotations are necrosis, adema, and active cancer. Radiologists create the ground truth.
MedICI
Jaysharee's program: Medical Imaging Challenge Infrastructure: MedICI
- Based on open-source CodaLab
- ePAD (created by Daniel Rubin's group at Stanford): tool for annotating images, creates AIM images
- caMicroscope
http://miccai.cloudapp.net:8000/competitions/28
- Competition #1: MICCAI challenge has a training phase where they train their algorithms. A test phase where they run their algorithms on images they have never seen before. They are compared to the ground truth that is determined beforehand. caMicroscope is used to see what is there before and to visualize the results. Overlap/completeness match determines the winner.
- Competition #2: They are given slides.
From PPT: Use titles of slides
Setting up a competition by an organizer. Organizer creates competition bundle.
Can go to cancerimagingarchive.net and create shared lists. Shared lists are pulled into CodaLab. That is how they get the test and training data.
Next is to create ground truth.
Regions of interest in a tumor for annotations are necrosis, adema, and active cancer. Radiologists create the ground truth.
Once participants upload their results, they can see them in ePad.
Notes
Medical Image Computational and computer-assisted Intervention: MICCAI
Ground truth: find the compatibility of the informatics that we need to run pilots. Take images out of TCIA, TCGA, clinical data and compare them.
Jasharee doing MICCAI Challenge in Munich. Segmentation of nuclear imaging in pathology. Combined radiology and pathology classification.
Want to be able to say that these informatics allow us to compare the pathology, rad, co-clinical findings.
Document the approach, technology, application to do a MICCAI challenge the way Jaysharee does itOnce participants upload their results, they can see them in ePad.
Scenarios
Need to generate proper therapy for a patient. Look at in vivo imaging, radiology and pathology, run a gene panel to look for abnormal. Look at co-clinical trials (model of a tumor in a mouse that is similar to a human. Experiment therapies on mice.) Run an integrative query to develop a sophisticated diagnosis. Search big data.
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