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 imaging**workflow of Huron's next generation TissueScope. Using tools from signal & image processing, computer**vision, and machine learning, the applicants plan to research, develop, and test new, cutting-edge fluorescence**imaging 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 of**integrated digital pathology the main reason for the delay is the lack of progress in integrating the digital**scanners with advanced image processing modules. The delay is attributed to hardware related issues, such as**those pertinent to the automation of the image acquisition process, and software related issues such as the use**of proprietary protocols in the processing pipeline which result in a diverse set of image navigation, contrast**correction, spectral mapping and enhancement, segmentation, and information retrieval solutions.**The proposed framework will greatly decrease the time and computational complexity of the image acquisition**and scanning process while improving accuracy and reliability, and enhancing the end-user experience.