Non-destructive 3D pathology with analysis of nuclear features for prostate cancer risk assessment

Robert Serafin; Weisi Xie; Can Koyuncu; Jonathan T. C. Liu; Jonathan T. C. Liu; Jonathan T. C. Liu

doi:10.1364/TRANSLATIONAL.2022.TM2B.3

Biophotonics Congress: Biomedical Optics 2022 (Translational, Microscopy, OCT, OTS, BRAIN)
Technical Digest Series (Optica Publishing Group, 2022),
paper TM2B.3
•https://doi.org/10.1364/TRANSLATIONAL.2022.TM2B.3

Non-destructive 3D pathology with analysis of nuclear features for prostate cancer risk assessment

Robert Serafin, Weisi Xie, Can Koyuncu, and Jonathan T. C. Liu

Clinical and Translational Biophotonics 2022

Fort Lauderdale, Florida United States
24–27 April 2022
ISBN: 978-1-957171-03-6

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Urology and Cancer (TM2B)

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R. Serafin, W. Xie, C. Koyuncu, and J. T. C. Liu, "Non-destructive 3D pathology with analysis of nuclear features for prostate cancer risk assessment," in Biophotonics Congress: Biomedical Optics 2022 (Translational, Microscopy, OCT, OTS, BRAIN), Technical Digest Series (Optica Publishing Group, 2022), paper TM2B.3.

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Abstract

We implement a deep learning-based 3D nuclear segmentation workflow on 3D pathology datasets of simulated biopsies extracted from prostatectomy specimens. A machine classifier demonstrates the value of 3D nuclear features for prostate cancer risk assessment.

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