A comprehensive workflow to generate 3D pathology datasets for large-scale clinical studies

Kevin W. Bishop; Lindsey A. Erion Barner; Qinghua Han; Elena Baraznenok; Lydia Lan; Gan Gao; Robert B. Serafin; Sarah S. L. Chow; Jonathan T. C. Liu

doi:10.1117/12.3003339

13 March 2024 A comprehensive workflow to generate 3D pathology datasets for large-scale clinical studies

Kevin W. Bishop, Lindsey A. Erion Barner, Qinghua Han, Elena Baraznenok, Lydia Lan, Gan Gao, Robert B. Serafin, Sarah S. L. Chow, Jonathan T. C. Liu

Author Affiliations +

Proceedings Volume PC12827, Multiscale Imaging and Spectroscopy V; PC128270T (2024) https://doi.org/10.1117/12.3003339
Event: SPIE BiOS, 2024, San Francisco, California, United States

Conference Poster

Abstract

Conventional pathology workflows rely on two-dimensional, slide-based analysis of thin tissue sections. This approach comes with several key limitations including limited sampling, lack of 3D structural information, and destruction of valuable clinical specimens. There is growing interest in nondestructive 3D pathology to address these shortcomings. Existing work has mainly focused on small-scale proof-of-concept studies, due in part to the difficulty of producing consistent, high-quality 3D pathology datasets across hundreds to thousands of specimens. To facilitate large-scale clinical studies, we present an end-to-end workflow for 3D pathology, with an emphasis on data consistency and quality control.

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Kevin W. Bishop, Lindsey A. Erion Barner, Qinghua Han, Elena Baraznenok, Lydia Lan, Gan Gao, Robert B. Serafin, Sarah S. L. Chow, and Jonathan T. C. Liu "A comprehensive workflow to generate 3D pathology datasets for large-scale clinical studies", Proc. SPIE PC12827, Multiscale Imaging and Spectroscopy V, PC128270T (13 March 2024); https://doi.org/10.1117/12.3003339

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