Abstract
This application is being submitted in response to the Notice of Special Interest (NOSI) identified as NOT-CA-23-073. Time-series microscopy has important research and clinical applications in cancer, such as selecting the best drug for a patient. However, time-series microscopy produces very large amounts of data that is difficult to analyze. It is common that humans need to analyze and review the primary imaging data together with the output of automatic algorithms for segmentation and tracking, to ensure quality and possibly adjust parameters. Also, analyzing images and derived data together provides insights beyond simple metrics. To address these issues, our team has developed Loon, an open source data visualization tool that enables researchers and clinicians to view and analyze such data at scale. Loon integrates algorithmic results with imaging data, and uses novel approaches, such as intelligently chosen exemplars, to address the scale issue. As such, Loon has proven an invaluable resource to our collaborators. However, Loon has been developed as a research project and hence can only be used with a narrow set of input data. The goal of this project is to harden and improve our software, leveraging professional software engineering skills and processes, and make it compatible with a wide range of data formats so that an efficient tool is available to the cancer microscopy community. We will also develop documentation for developers and end-users, and provide community support. Finally, we will take steps to ensure that Loon is easy to deploy both locally and in the cloud. We anticipate that our methods and tools will accelerate and increase the reproducibility of data-driven cancer research.
Project Narrative
Time-series microscopy has important research and clinical applications in cancer, such as selecting the best drug for a patient. We have developed data visualization software that enables researchers and clinicians to view and analyze time-series microscopy data at scale. The goal of this project is to harden and improve our software, and make it compatible with a wide range of data formats so that an efficient tool is available to the cancer microscopy community.
* PI Clarification
This is a supplemental grant to the Huntsman Cancer Institute’s P30 cancer center grant. The PI of the parent grant and the PI of record for all supplements is Cornelia Ulrich.
