A Camera-Assisted Pathology Microscope to Capture the Lost Data in Clinical Glass Slide Diagnosis

Abstract

Digital pathology, or the practice of acquiring, managing, and interpreting high-resolution digital images from glass pathology slides, holds much promise in precision medicine, potentially transforming diagnosis and prognosis based on computational image biomarkers derived from digital tissue images. However, for all its promise, digital imaging in pathology has not yet become an integral part of the clinical workflow as it has in radiology due to high cost, workflow disruptions, burdensome data sizes and IT requirements, and additional dedicated personnel requirements. Consequently, pathology retains the 150-year-old analog workflow, and the vast majority of slides used in clinical diagnosis are never digitized. Furthermore, there is a missed opportunity to capture the image information and associated data on search processes that led to the clinical diagnosis, which could serve as the foundation for computational clinical decision support. This paper describes an approach for slide digitization during clinical review using a camera attached to a standard brightfield pathology microscope. While a pathologist reviews a glass slide using the eyepiece oculars, the continuously running camera digitizes a complete record of the slide review, resulting in multi-resolution slide images and spatiotemporal saliency maps of the slide review. Unlike other approaches, the pathologist does not stop to review the video stream or monitor the acquisition of video frames but performs the diagnostic review at the microscope using the standard clinical protocol. This hybrid analog-digital approach combines the benefits of digital slide analysis, including annotation, computation, and the ability to confirm the completeness and quality of the glass slide review with the ease of using the microscope for primary diagnosis. Furthermore, a record of the pathologist's attention during the review, including their search path, magnification level, and dwell times at each location on the slide, is obtained. In the future, this approach could enable the development and application of new and emerging computational decision-support algorithms in real-time to provide feedback to the pathologist, reduce diagnostic errors, and improve disease diagnosis and prognosis.

DeepDOF-SE: affordable deep-learning microscopy platform for slide-free histology

“…DeepDOF-SE is specifically designed to provide a slide-free histology platform for use in low-resource settings to support immediate biopsy assessment and/or rapid intraoperative assessment of margin status. The 4×, 0.13 NA system can resolve subcellular features needed to diagnose precancer and cancer and is consistent with pathologists' use of 2× and 4× objectives for the vast majority of diagnoses [5][6][7][8] and recent studies demonstrating that deep learning models can accurately classify the presence of cancer with significant image compression 9 and NA as low as 0.05 10 .…”