Fluorescence imaging analysis software tools for high throughput automated workflow solution in digital pathology

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In collaboration with Huron Digital Pathology Inc., of Waterloo, Ontario, the University of Toronto (UofT)team aims to develop an innovative image processing framework for the custom-tailored fluorescence imagingworkflow of Huron's next generation TissueScope. Using tools from signal & image processing, computervision, and machine learning, the applicants plan to research, develop, and test new, cutting-edge fluorescenceimaging algorithmic solutions.Despite effective utilization of existing virtual microscopes, the goal of delivering a fully integrated, scalable,certified for clinical use, digital pathology framework has not yet materialized. According to early adopters ofintegrated digital pathology the main reason for the delay is the lack of progress in integrating the digitalscanners with advanced image processing modules. The delay is attributed to hardware related issues, such asthose pertinent to the automation of the image acquisition process, and software related issues such as the useof proprietary protocols in the processing pipeline which result in a diverse set of image navigation, contrastcorrection, spectral mapping and enhancement, segmentation, and information retrieval solutions.The proposed framework will greatly decrease the time and computational complexity of the image acquisitionand scanning process while improving accuracy and reliability, and enhancing the end-user experience.