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

多伦多大学(UofT)团队与安大略省滑铁卢的休伦数字病理公司合作，致力于为休伦下一代TIseScope的定制荧光成像**工作流程开发一种创新的图像处理框架。申请者计划使用信号与图像处理、计算机**视觉和机器学习的工具，研究、开发和测试新的、尖端的荧光**成像算法解决方案。**尽管有效利用了现有的虚拟显微镜，但提供完全集成、可扩展、**经临床使用认证的数字病理学框架的目标尚未实现。根据**集成数字病理学的早期采用者的说法，延迟的主要原因是在将数字**扫描仪与先进的图像处理模块集成方面缺乏进展。延迟的原因是硬件相关的问题，例如与图像采集过程自动化有关的问题，以及软件相关的问题，例如在处理管道中使用专有协议**，这导致了一套不同的图像导航、对比度校正、光谱映射和增强、分割和信息检索解决方案。**建议的框架将大大减少图像采集和扫描过程的时间和计算复杂性**，同时提高准确性和可靠性，并增强最终用户体验。

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.