Weisi Xie,1 Adam K. Glaser,1 Nicholas Reder,1 Nadia Postupna,1 Chenyi Mao,1 Can Koyuncu,2 Patrick Leo,2 Robert Serafin,1 Hongyi Huang,1 Anant Madabhushi,2 Lawrence True,1 Jonathan T. C. Liuhttps://orcid.org/0000-0001-5650-30861
1Univ. of Washington (United States) 2Case Western Reserve Univ. (United States)
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Glandular architecture is currently the basis for the Gleason grading of prostate biopsies. To visualize and computationally analyze glandular features in large 3D pathology datasets, we developed an annotation-free segmentation method for 3D prostate glands that relies upon synthetic 3D immunofluorescence (IF) enabled by generative adversarial networks. By using a fluorescent analog of H and E (cheap and fast stain) as an input, our strategy allows for accurate glandular segmentation that does not rely upon subjective and tedious human annotations or slow and expensive 3D immunolabeling. We aim to demonstrate that this 3D segmentation will enable improved prostate cancer prognostication.
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Weisi Xie, Adam K. Glaser, Nicholas Reder, Nadia Postupna, Chenyi Mao, Can Koyuncu, Patrick Leo, Robert Serafin, Hongyi Huang, Anant Madabhushi, Lawrence True, Jonathan T. C. Liu, "Annotation-free 3D segmentation of prostate glands enabled with deep-learning image translation," Proc. SPIE 11631, Advanced Biomedical and Clinical Diagnostic and Surgical Guidance Systems XIX, 116310M (5 March 2021); https://doi.org/10.1117/12.2576965