Whole slide image data utilization informed by digital diagnosis patterns

Ashman, Kimberly; Zhuge, Huimin; Shanley, Erin; Fox, Sharon; Halat, Shams; Sholl, Andrew B.; Summa, Brian; Brown, J. Quincy

doi:10.1016/j.jpi.2022.100113

Cited by 8 publications

(4 citation statements)

References 21 publications

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“…We also visualized the dwell time of the pathologist’s digital and glass review by summing the cumulative time interval spent at each pixel and applying a color map to the images based on the maximum and minimum time intervals for each session (Supplementary Figure 6). 47…”

Section: Methodsmentioning

confidence: 99%

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

Ashman¹,

Cooper²,

Zhuge³

et al. 2022

Preprint

Self Cite

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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.

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Section: Methodsmentioning

confidence: 99%

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

Ashman¹,

Cooper²,

Zhuge³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…The analysis of a larger number of images is advisable to further validate the procedure and further efforts are needed to extend the quality analysis to WSIs. 34 In this study, the artifacts generated by the normalization process were evaluated by the pathologists on 2000 x 2000 pixels image fields. In future, the most common artifacts generated in routine histology, such as dust or dark spots, synthetic threads or tissue folds, scratches, and unfocused regions, 23 could be clinically assessed.…”

Section: Discussionmentioning

confidence: 99%

Stain normalization in digital pathology: Clinical multi-center evaluation of image quality

Michielli¹,

Caputo

Scotto³

et al. 2022

Journal of Pathology Informatics

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“…It happens often that a WSI sample can have a resolution up to 100,000 pixel × 100,000 pixel [23,24]. Digitization has 4 essential parts, which are as follows: image acquisition, data storage, editing, and display [25][26][27][28].…”

Section: Whole Slide Images (Wsi)mentioning

confidence: 99%

3D Visualization in Digital Medicine Using XR Technology

Vincze,

Molnar,

Kozlovszky

2023

Future Internet

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Nowadays, virtual reality is a new and rapidly developing technology that provides the opportunity for a new, more immersive form of data visualization. Evaluating digitized pathological serial sections and establishing the appropriate diagnosis is one of the key task of the pathologist in the daily work. The type of tools used by pathologists in the evaluation of samples has not changed much in recent decades. On the other hand, the amount of information required to establish an accurate diagnosis has been significantly increased. Nowadays, pathologists are working with the help of multiple high-resolution desktop monitors. Instead of the large screens, the use of virtual reality can serve as an alternative solution, which provides virtualized working space for pathologists during routine sample evaluation. In our research, we defined a new immersive working environment for pathologists. In our proposed solution we visualize several type of digitized medical image data with the corresponding meta data in 3D, and we also defined virtualized functions that support the evaluation process. The main aim of this paper is to present the new possibilities provided by 3D visualization and virtual reality in digital pathology. The paper presents a new virtual reality-based examination environment, as well as software functionalities that are essential for 3D pathological tissue evaluation.

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Whole slide image data utilization informed by digital diagnosis patterns

Cited by 8 publications

References 21 publications

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

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

Stain normalization in digital pathology: Clinical multi-center evaluation of image quality

3D Visualization in Digital Medicine Using XR Technology

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