The capacity to distinguish among subclasses of disease affects how patients are treated, which medications are appropriate, and what levels of risk are justified. Tissue microarray (TMA) technology makes it possible to investigate and confirm clinico-pathologic correlations which have been postulated based upon the evaluation of whole histology sections. Unfortunately, inconsistencies often arise during the evaluation process as a result of subjective impressions and inter- and intra-observer variability. Advanced imaging and computational tools make it possible to detect and track subtle changes in measurable parameters leading to insight regarding the underlying mechanisms of disease progression and the discovery of novel diagnostic and prognostic clues which are not apparent by human inspection alone. The overarching goals of this renewal application are to build upon progress made in the first phase of the project and design, develop and evaluate new capabilities by meeting the objectives of the following specific aims: (1) Develop and evaluate a new family of multi-stage, searching algorithms to facilitate quick, reliable interrogation of larg-scale, clinical and research, microscopy applications including whole-slide imaging and tissue microarray;(2) Develop and evaluate a suite of high-throughput services capable of automatically detecting, archiving and indexing user-specified objects (e.g. tissues, cells) in large collections of images and implement extensions to the data models and support for optimized pipeline selection. These capabilities will enable large-scale correlative outcomes studies and support expansion of the """"""""gold standard"""""""" image archives and correlated clinical repositories. The services will take advantage of state-of-the-art parallel CPU-GPU machines and the searching algorithms described in Aim 1;(3) Optimize the imaging, computational and content-based image retrieval algorithms and tools using a wide range of different tissues, cancer types and biomarkers to support clinical and research experiments and studies involving patient stratification, quality-control, and outcomes assessment;and (4) Deploy the analytical tools, data models, user-centered interfaces and reference libraries of imaged specimens to participating adopter sites to conduct open-set usability and performance studies and make these resources available to the clinical and research communities as open source software and resources to support future development and testing of new hypotheses, algorithms and methods.
During the first phase of this research project our team successfully designed, developed and evaluated (a) a library of analytical and computational tools for performing automated registration, segmentation, feature extraction, and classification of imaged microtissue samples;(b) data models and grid-enabled tools for performing large-scale indexing, organizing, and archiving imaged microscopy specimens and experimental results;(c) query capabilities to support reliable identification and retrieval of those imaged tissue samples from within distributed, gold-standard archives of consensus-graded cases which exhibit staining and expression signatures which are most similar to those of a given query;and (d) methods and resources to support seamless, high-throughput analyses of specimens. The overarching goals of this renewal application are to build upon progress made in the first phase of the project by developing and evaluating a new family of multi-stage, searching algorithms to facilitate quick, reliable interrogation of large-scale, clinical and research, microscopy applications including whole-slide imaging and tissue microarray;developing and evaluating a suite of high-throughput services capable of automatically detecting, archiving and indexing user-specified objects (e.g. tissues, cells) in large collections of images and implement extensions to the data models and support for optimized pipeline selection. These capabilities will enable large-scale correlative outcomes studies and support expansion of the gold-standard image archives and correlated clinical repositories. The services will take advantage of state-of-the-art parallel CPU-GPU machines and the searching algorithms described in Aim 1;optimizing the imaging, computational and content-based image retrieval algorithms and tools using a wide range of different tissues, cancer types and biomarkers to support clinical and research experiments and studies involving patient stratification, quality-control, and outcomes assessment;and deploying the analytical tools, data models, user-centered interfaces and reference libraries of imaged specimens to participating adopter sites to conduct open-set usability and performance studies and make these resources available to the clinical and research communities as open source software and resources to support future development and testing of new hypotheses, algorithms and methods.
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