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Lutnick B, Murali LK, Ginley B, Rosenberg AZ, Sarder P. Histo-Fetch - On-the-Fly Processing of Gigapixel Whole Slide Images Simplifies and Speeds Neural Network Training. J Pathol Inform 2022; 13:7. [PMID: 35136674 PMCID: PMC8794032 DOI: 10.4103/jpi.jpi_59_20] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Revised: 01/23/2021] [Accepted: 11/11/2021] [Indexed: 02/04/2023] Open
Abstract
BACKGROUND Training convolutional neural networks using pathology whole slide images (WSIs) is traditionally prefaced by the extraction of a training dataset of image patches. While effective, for large datasets of WSIs, this dataset preparation is inefficient. METHODS We created a custom pipeline (histo-fetch) to efficiently extract random patches and labels from pathology WSIs for input to a neural network on-the-fly. We prefetch these patches as needed during network training, avoiding the need for WSI preparation such as chopping/tiling. RESULTS & CONCLUSIONS We demonstrate the utility of this pipeline to perform artificial stain transfer and image generation using the popular networks CycleGAN and ProGAN, respectively. For a large WSI dataset, histo-fetch is 98.6% faster to start training and used 7535x less disk space.
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Affiliation(s)
- Brendon Lutnick
- Department of Pathology and Anatomical Sciences, SUNY Buffalo, Buffalo, NY, USA
| | | | - Brandon Ginley
- Department of Pathology and Anatomical Sciences, SUNY Buffalo, Buffalo, NY, USA
| | - Avi Z. Rosenberg
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Pinaki Sarder
- Department of Pathology and Anatomical Sciences, SUNY Buffalo, Buffalo, NY, USA
- Department of Biomedical Engineering, SUNY Buffalo, Buffalo, NY, USA
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Li X, Davis RC, Xu Y, Wang Z, Souma N, Sotolongo G, Bell J, Ellis M, Howell D, Shen X, Lafata KJ, Barisoni L. Deep learning segmentation of glomeruli on kidney donor frozen sections. J Med Imaging (Bellingham) 2021; 8:067501. [PMID: 34950750 PMCID: PMC8685284 DOI: 10.1117/1.jmi.8.6.067501] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Accepted: 11/08/2021] [Indexed: 10/15/2023] Open
Abstract
Purpose: Recent advances in computational image analysis offer the opportunity to develop automatic quantification of histologic parameters as aid tools for practicing pathologists. We aim to develop deep learning (DL) models to quantify nonsclerotic and sclerotic glomeruli on frozen sections from donor kidney biopsies. Approach: A total of 258 whole slide images (WSI) from cadaveric donor kidney biopsies performed at our institution ( n = 123 ) and at external institutions ( n = 135 ) were used in this study. WSIs from our institution were divided at the patient level into training and validation datasets (ratio: 0.8:0.2), and external WSIs were used as an independent testing dataset. Nonsclerotic ( n = 22767 ) and sclerotic ( n = 1366 ) glomeruli were manually annotated by study pathologists on all WSIs. A nine-layer convolutional neural network based on the common U-Net architecture was developed and tested for the segmentation of nonsclerotic and sclerotic glomeruli. DL-derived, manual segmentation, and reported glomerular count (standard of care) were compared. Results: The average Dice similarity coefficient testing was 0.90 and 0.83. And the F 1 , recall, and precision scores were 0.93, 0.96, and 0.90, and 0.87, 0.93, and 0.81, for nonsclerotic and sclerotic glomeruli, respectively. DL-derived and manual segmentation-derived glomerular counts were comparable, but statistically different from reported glomerular count. Conclusions: DL segmentation is a feasible and robust approach for automatic quantification of glomeruli. We represent the first step toward new protocols for the evaluation of donor kidney biopsies.
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Affiliation(s)
- Xiang Li
- Duke University, Department of Electrical and Computer Engineering, Durham, North Carolina, United States
| | - Richard C. Davis
- Duke University, Department of Pathology, Division of AI and Computational Pathology, Durham, North Carolina, United States
| | - Yuemei Xu
- Duke University, Department of Pathology, Division of AI and Computational Pathology, Durham, North Carolina, United States
- Nanjing Drum Tower Hospital, Department of Pathology, Nanjing, China
| | - Zehan Wang
- Duke University, Department of Biomedical Engineering, Durham, North Carolina, United States
| | - Nao Souma
- Duke University, Department of Medicine, Division of Nephrology, Durham, North Carolina, United States
| | - Gina Sotolongo
- Duke University, Department of Pathology, Division of AI and Computational Pathology, Durham, North Carolina, United States
| | - Jonathan Bell
- Duke University, Department of Pathology, Division of AI and Computational Pathology, Durham, North Carolina, United States
| | - Matthew Ellis
- Duke University, Department of Medicine, Division of Nephrology, Durham, North Carolina, United States
- Duke University, Department of Surgery, Durham, North Carolina, United States
| | - David Howell
- Duke University, Department of Pathology, Division of AI and Computational Pathology, Durham, North Carolina, United States
| | - Xiling Shen
- Duke University, Department of Biomedical Engineering, Durham, North Carolina, United States
| | - Kyle J. Lafata
- Duke University, Department of Electrical and Computer Engineering, Durham, North Carolina, United States
- Duke University, Department of Radiation Oncology, Durham, North Carolina, United States
- Duke University, Department of Radiology, Durham, North Carolina, United States
| | - Laura Barisoni
- Duke University, Department of Pathology, Division of AI and Computational Pathology, Durham, North Carolina, United States
- Duke University, Department of Medicine, Division of Nephrology, Durham, North Carolina, United States
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Huo Y, Deng R, Liu Q, Fogo AB, Yang H. AI applications in renal pathology. Kidney Int 2021; 99:1309-1320. [PMID: 33581198 PMCID: PMC8154730 DOI: 10.1016/j.kint.2021.01.015] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Revised: 01/09/2021] [Accepted: 01/13/2021] [Indexed: 12/20/2022]
Abstract
The explosive growth of artificial intelligence (AI) technologies, especially deep learning methods, has been translated at revolutionary speed to efforts in AI-assisted healthcare. New applications of AI to renal pathology have recently become available, driven by the successful AI deployments in digital pathology. However, synergetic developments of renal pathology and AI require close interdisciplinary collaborations between computer scientists and renal pathologists. Computer scientists should understand that not every AI innovation is translatable to renal pathology, while renal pathologists should capture high-level principles of the relevant AI technologies. Herein, we provide an integrated review on current and possible future applications in AI-assisted renal pathology, by including perspectives from computer scientists and renal pathologists. First, the standard stages, from data collection to analysis, in full-stack AI-assisted renal pathology studies are reviewed. Second, representative renal pathology-optimized AI techniques are introduced. Last, we review current clinical AI applications, as well as promising future applications with the recent advances in AI.
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Affiliation(s)
- Yuankai Huo
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, Tennessee, USA
| | - Ruining Deng
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, Tennessee, USA
| | - Quan Liu
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, Tennessee, USA
| | - Agnes B Fogo
- Department of Pathology, Microbiology and Immunology, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Haichun Yang
- Department of Pathology, Microbiology and Immunology, Vanderbilt University Medical Center, Nashville, Tennessee, USA.
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