Presentation
17 March 2023 PathCAM: Pathology Computer Assisted Microscope for multiscale expert-in-the-loop digital pathology
J. Quincy Brown, Kimberly L. Ashman, Max Cooper, Huimin Zhuge, Sharon E. Fox, Jonathan I. Epstein, Carola Wenk, Brian Summa
Author Affiliations +
Proceedings Volume PC12363, Multiscale Imaging and Spectroscopy IV; PC1236308 (2023) https://doi.org/10.1117/12.2658961
Event: SPIE BiOS, 2023, San Francisco, California, United States
Abstract
Advances in computational image analysis and machine learning in the field of digital pathology seem poised to make transformational changes in disease diagnosis and prognosis, yet currently 99% of all histopathology slides generated in clinical practice are never digitized. We seek to incorporate digital imaging into the standard glass-slide clinical pathology workflow using relatively inexpensive modifications to traditional clinical microscopes. Here we will introduce PathCAM, a pathology computer-assisted microscope for "ambient" digitization of histology slides during the standard pathology workflow. PathCAM leverages high-frequency spatiotemporal sampling and human-machine interaction to deliver complete and accurate digital records of clinical glass-slide analysis. We will discuss the development of the PathCAM system and technical approach, and demonstrate the unique datasets and capabilities afforded by this hybrid digital-analog pathology imaging and analysis platform.
Conference Presentation
© (2023) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
J. Quincy Brown, Kimberly L. Ashman, Max Cooper, Huimin Zhuge, Sharon E. Fox, Jonathan I. Epstein, Carola Wenk, and Brian Summa "PathCAM: Pathology Computer Assisted Microscope for multiscale expert-in-the-loop digital pathology", Proc. SPIE PC12363, Multiscale Imaging and Spectroscopy IV, PC1236308 (17 March 2023); https://doi.org/10.1117/12.2658961
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KEYWORDS
Pathology

Microscopes

Imaging systems

Digital imaging

Image analysis

Machine learning

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