Real-time multiresolution digitization and computational analysis of pathology slides for closed-loop hybrid analog-digital clinical pathology review

We have designed a computer-assisted pathology microscope (PathCAM) which aims to overcome the current limitations of digital imaging in pathology, and to enable a new paradigm of real-time human-computer interaction in digital pathology. PathCAM is a system for real-time slide digitization during clinical glass slide review, and real-time “expert-in-the-loop” applications including assessment of completeness of review, annotation, segmentation, and computational analysis. High resolution and high frame-rate image acquisition of pathologist review of pathology slides paired with concurrent parallelized image registration, pair-wise matching, and global alignment of mosaics collected through various objective lenses are combined to create multi-resolution slide images for the purpose of clinical decision support and assisted diagnosis using emerging AI foundation models.

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Publication Details

Published
2025-03-19
DOI
https://doi.org/10.1117/12.3043492
Primary Topic
AI in cancer detection
Type
article
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Real-time multiresolution digitization and computational analysis of pathology slides for closed-loop hybrid analog-digital clinical pathology review

Kimberly Ashman, Max S. Cooper, Shams Halat, J. Quincy Brown et al.
AI in cancer detection
article

Real-time multiresolution digitization and computational analysis of pathology slides for closed-loop hybrid analog-digital clinical pathology review

Kimberly Ashman, Max S. Cooper, Shams Halat, J. Quincy Brown, Carola Wenk, Brian Summa, Andrew B. Sholl, Sharon Fox, Cooper Maira
article en

Abstract

We have designed a computer-assisted pathology microscope (PathCAM) which aims to overcome the current limitations of digital imaging in pathology, and to enable a new paradigm of real-time human-computer interaction in digital pathology. PathCAM is a system for real-time slide digitization during clinical glass slide review, and real-time “expert-in-the-loop” applications including assessment of completeness of review, annotation, segmentation, and computational analysis. High resolution and high frame-rate image acquisition of pathologist review of pathology slides paired with concurrent parallelized image registration, pair-wise matching, and global alignment of mosaics collected through various objective lenses are combined to create multi-resolution slide images for the purpose of clinical decision support and assisted diagnosis using emerging AI foundation models.

Tulane University (US), Southeast Louisiana Veterans Health Care System (US)
Openalex Percentile: Top 98%
AI in cancer detection
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