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Put together a primer, examples of data, use cases, how to carry out an integrative query, so that it is understandable.
Introduction
Each domain is at a different step in maturity. proprietary data formats
Pathology problems:1. proprietary data formats that cannot be displayed and manipulated in the same tools. Solution is to integrate caMicroscope with OpenSlide (allows us to read prop. formats without converting images). Makes a large number of image formats accessible. 2. no standard for markups and annotations. So we're creating microAIM.
Co-clinical and animal model images: most imaging machines for animal model imaging do not follow the DICOM standard. We developed a supplement to the DICOM standard to accommodate small animal imaging (standard out for balloting). We want to include co-clinical/animal model data in the integrative queries. For this new standard to be used, equipment manufacturers would need to incorporate this standard when they develop machines/software.
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.
Three separate sections with problem/solution for each aim. Status of the solution.
Imaging-based cancer research is in the beginning phase of an integrative-biology revolution. It is now feasible to extract large sets of quantitative image features relevant to prognosis or treatment across three complementary research domains: in vivo clinical imaging, pre-clinical imaging, and digital pathology. These high-dimensional image feature sets can be used to infer clinical phenotypes or correlate with gene–protein signatures. This type of analysis, however, requires large volumes of image data. In this project, we propose to develop and deploy software that supports a comprehensive and reusable exploration and fusion of imaging, clinical, and molecular data. Within these three research domains, only one, clinical imaging, has made some progress in terms of establishing a framework and standards for informatics solutions. For pre-clinical imaging and digital pathology, there are no such standards that allow for the seamless viewing, integration, and analysis of disparate data sets to produce integrated views of the data, quantitative analysis, data integration, and research or clinical decision support systems.
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Challenge Management System, MedICI
Jaysharee's program: Medical Imaging Challenge Infrastructure: MedICI
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