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Yang Y, Sun K, Gao Y, Wang K, Yu G. Preparing Data for Artificial Intelligence in Pathology with Clinical-Grade Performance. Diagnostics (Basel) 2023; 13:3115. [PMID: 37835858 PMCID: PMC10572440 DOI: 10.3390/diagnostics13193115] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Revised: 09/27/2023] [Accepted: 09/28/2023] [Indexed: 10/15/2023] Open
Abstract
The pathology is decisive for disease diagnosis but relies heavily on experienced pathologists. In recent years, there has been growing interest in the use of artificial intelligence in pathology (AIP) to enhance diagnostic accuracy and efficiency. However, the impressive performance of deep learning-based AIP in laboratory settings often proves challenging to replicate in clinical practice. As the data preparation is important for AIP, the paper has reviewed AIP-related studies in the PubMed database published from January 2017 to February 2022, and 118 studies were included. An in-depth analysis of data preparation methods is conducted, encompassing the acquisition of pathological tissue slides, data cleaning, screening, and subsequent digitization. Expert review, image annotation, dataset division for model training and validation are also discussed. Furthermore, we delve into the reasons behind the challenges in reproducing the high performance of AIP in clinical settings and present effective strategies to enhance AIP's clinical performance. The robustness of AIP depends on a randomized collection of representative disease slides, incorporating rigorous quality control and screening, correction of digital discrepancies, reasonable annotation, and sufficient data volume. Digital pathology is fundamental in clinical-grade AIP, and the techniques of data standardization and weakly supervised learning methods based on whole slide image (WSI) are effective ways to overcome obstacles of performance reproduction. The key to performance reproducibility lies in having representative data, an adequate amount of labeling, and ensuring consistency across multiple centers. Digital pathology for clinical diagnosis, data standardization and the technique of WSI-based weakly supervised learning will hopefully build clinical-grade AIP.
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Affiliation(s)
- Yuanqing Yang
- Department of Biomedical Engineering, School of Basic Medical Sciences, Central South University, Changsha 410013, China; (Y.Y.); (K.S.)
- Department of Biomedical Engineering, School of Medical, Tsinghua University, Beijing 100084, China
| | - Kai Sun
- Department of Biomedical Engineering, School of Basic Medical Sciences, Central South University, Changsha 410013, China; (Y.Y.); (K.S.)
- Furong Laboratory, Changsha 410013, China
| | - Yanhua Gao
- Department of Ultrasound, Shaanxi Provincial People’s Hospital, Xi’an 710068, China;
| | - Kuansong Wang
- Department of Pathology, School of Basic Medical Sciences, Central South University, Changsha 410013, China;
- Department of Pathology, Xiangya Hospital, Central South University, Changsha 410013, China
| | - Gang Yu
- Department of Biomedical Engineering, School of Basic Medical Sciences, Central South University, Changsha 410013, China; (Y.Y.); (K.S.)
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2
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Sun G, Yan X, Wang H, Li F, Yang R, Xu J, Liu X, Li X, Zou X. Color restoration based on digital pathology image. PLoS One 2023; 18:e0287704. [PMID: 37379301 DOI: 10.1371/journal.pone.0287704] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Accepted: 06/09/2023] [Indexed: 06/30/2023] Open
Abstract
OBJECTIVE Protective color restoration of faded digital pathology images based on color transfer algorithm. METHODS Twenty fresh tissue samples of invasive breast cancer from the pathology department of Qingdao Central Hospital in 2021 were screened. After HE staining, HE stained sections were irradiated with sunlight to simulate natural fading, and every 7 days was a fading cycle, and a total of 8 cycles were experienced. At the end of each cycle, the sections were digitally scanned to retain clear images, and the color changes of the sections during the fading process were recorded. The color transfer algorithm was applied to restore the color of the faded images; Adobe Lightroom Classic software presented the histogram of the image color distribution; UNet++ cell recognition segmentation model was used to identify the color restored images; Natural Image Quality Evaluator (NIQE), Information Entropy (Entropy), and Average Gradient (AG) were applied to evaluate the quality of the restored images. RESULTS The restored image color met the diagnostic needs of pathologists. Compared with the faded images, the NIQE value decreased (P<0.05), Entropy value increased (P<0.01), and AG value increased (P<0.01). The cell recognition rate of the restored image was significantly improved. CONCLUSION The color transfer algorithm can effectively repair faded pathology images, restore the color contrast between nucleus and cytoplasm, improve the image quality, meet the diagnostic needs and improve the cell recognition rate of the deep learning model.
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Affiliation(s)
- Guoxin Sun
- School of Clinical Medicine, Qingdao University, Qingdao, China
| | - Xiong Yan
- Department of Pathology, Qingdao Central Hospital, Qingdao, China
| | - Huizhe Wang
- School of Clinical Medicine, Qingdao University, Qingdao, China
| | - Fei Li
- School of Computer Engineering and Science Shanghai University, Shanghai, China
| | - Rui Yang
- School of Computer Engineering and Science Shanghai University, Shanghai, China
| | - Jing Xu
- Department of Pathology, Qingdao Central Hospital, Qingdao, China
| | - Xin Liu
- School of Clinical Medicine, Qingdao University, Qingdao, China
| | - Xiaomao Li
- School of Computer Engineering and Science Shanghai University, Shanghai, China
| | - Xiao Zou
- Department of Breast Surgery, Xiangdong Hospital Affiliated to Hunan Normal University, Hunan, China
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Deep-learning based breast cancer detection for cross-staining histopathology images. Heliyon 2023; 9:e13171. [PMID: 36755605 PMCID: PMC9900267 DOI: 10.1016/j.heliyon.2023.e13171] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Revised: 01/13/2023] [Accepted: 01/19/2023] [Indexed: 01/23/2023] Open
Abstract
Hematoxylin and eosin (H&E) staining is the gold standard for tissue characterization in routine pathological diagnoses. However, these visible light dyes do not exclusively label the nuclei and cytoplasm, making clear-cut segmentation of staining signals challenging. Currently, fluorescent staining technology is much more common in clinical research for analyzing tissue morphology and protein distribution owing to its advantages of channel independence, multiplex labeling, and the possibility of enabling 3D tissue labeling. Although both H&E and fluorescent dyes can stain the nucleus and cytoplasm for representative tissue morphology, color variation between these two staining technologies makes cross-analysis difficult, especially with computer-assisted artificial intelligence (AI) algorithms. In this study, we applied color normalization and nucleus extraction methods to overcome the variation between staining technologies. We also developed an available workflow for using an H&E-stained segmentation AI model in the analysis of fluorescent nucleic acid staining images in breast cancer tumor recognition, resulting in 89.6% and 80.5% accuracy in recognizing specific tumor features in H&E- and fluorescent-stained pathological images, respectively. The results show that the cross-staining inference maintained the same precision level as the proposed workflow, providing an opportunity for an expansion of the application of current pathology AI models.
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Kosaraju S, Park J, Lee H, Yang JW, Kang M. Deep learning-based framework for slide-based histopathological image analysis. Sci Rep 2022; 12:19075. [PMID: 36351997 PMCID: PMC9646838 DOI: 10.1038/s41598-022-23166-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2022] [Accepted: 10/26/2022] [Indexed: 11/11/2022] Open
Abstract
Digital pathology coupled with advanced machine learning (e.g., deep learning) has been changing the paradigm of whole-slide histopathological images (WSIs) analysis. Major applications in digital pathology using machine learning include automatic cancer classification, survival analysis, and subtyping from pathological images. While most pathological image analyses are based on patch-wise processing due to the extremely large size of histopathology images, there are several applications that predict a single clinical outcome or perform pathological diagnosis per slide (e.g., cancer classification, survival analysis). However, current slide-based analyses are task-dependent, and a general framework of slide-based analysis in WSI has been seldom investigated. We propose a novel slide-based histopathology analysis framework that creates a WSI representation map, called HipoMap, that can be applied to any slide-based problems, coupled with convolutional neural networks. HipoMap converts a WSI of various shapes and sizes to structured image-type representation. Our proposed HipoMap outperformed existing methods in intensive experiments with various settings and datasets. HipoMap showed the Area Under the Curve (AUC) of 0.96±0.026 (5% improved) in the experiments for lung cancer classification, and c-index of 0.787±0.013 (3.5% improved) and coefficient of determination ([Formula: see text]) of 0.978±0.032 (24% improved) in survival analysis and survival prediction with TCGA lung cancer data respectively, as a general framework of slide-based analysis with a flexible capability. The results showed significant improvement comparing to the current state-of-the-art methods on each task. We further discussed experimental results of HipoMap as pathological viewpoints and verified the performance using publicly available TCGA datasets. A Python package is available at https://pypi.org/project/hipomap , and the package can be easily installed using Python PIP. The open-source codes in Python are available at: https://github.com/datax-lab/HipoMap .
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Affiliation(s)
- Sai Kosaraju
- grid.272362.00000 0001 0806 6926Department of Computer Science, University of Nevada, Las Vegas, Las Vegas, NV 89154 USA
| | - Jeongyeon Park
- grid.412859.30000 0004 0533 4202Department of Computer Science, Sun Moon University, Asan, 336708 South Korea
| | - Hyun Lee
- grid.412859.30000 0004 0533 4202Department of Computer Science, Sun Moon University, Asan, 336708 South Korea
| | - Jung Wook Yang
- grid.256681.e0000 0001 0661 1492Department of Pathology, Gyeongsang National University Hospital, Gyeongsang National University College of Medicine, Jinju, South Korea
| | - Mingon Kang
- grid.272362.00000 0001 0806 6926Department of Computer Science, University of Nevada, Las Vegas, Las Vegas, NV 89154 USA
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5
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H&E Multi-Laboratory Staining Variance Exploration with Machine Learning. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12157511] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
In diagnostic histopathology, hematoxylin and eosin (H&E) staining is a critical process that highlights salient histological features. Staining results vary between laboratories regardless of the histopathological task, although the method does not change. This variance can impair the accuracy of algorithms and histopathologists’ time-to-insight. Investigating this variance can help calibrate stain normalization tasks to reverse this negative potential. With machine learning, this study evaluated the staining variance between different laboratories on three tissue types. We received H&E-stained slides from 66 different laboratories. Each slide contained kidney, skin, and colon tissue samples stained by the method routinely used in each laboratory. The samples were digitized and summarized as red, green, and blue channel histograms. Dimensions were reduced using principal component analysis. The data projected by principal components were inserted into the k-means clustering algorithm and the k-nearest neighbors classifier with the laboratories as the target. The k-means silhouette index indicated that K = 2 clusters had the best separability in all tissue types. The supervised classification result showed laboratory effects and tissue-type bias. Both supervised and unsupervised approaches suggested that tissue type also affected inter-laboratory variance. We suggest tissue type to also be considered upon choosing the staining and color-normalization approach.
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6
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Nan Y, Ser JD, Walsh S, Schönlieb C, Roberts M, Selby I, Howard K, Owen J, Neville J, Guiot J, Ernst B, Pastor A, Alberich-Bayarri A, Menzel MI, Walsh S, Vos W, Flerin N, Charbonnier JP, van Rikxoort E, Chatterjee A, Woodruff H, Lambin P, Cerdá-Alberich L, Martí-Bonmatí L, Herrera F, Yang G. Data harmonisation for information fusion in digital healthcare: A state-of-the-art systematic review, meta-analysis and future research directions. AN INTERNATIONAL JOURNAL ON INFORMATION FUSION 2022; 82:99-122. [PMID: 35664012 PMCID: PMC8878813 DOI: 10.1016/j.inffus.2022.01.001] [Citation(s) in RCA: 40] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/24/2021] [Revised: 12/22/2021] [Accepted: 01/07/2022] [Indexed: 05/13/2023]
Abstract
Removing the bias and variance of multicentre data has always been a challenge in large scale digital healthcare studies, which requires the ability to integrate clinical features extracted from data acquired by different scanners and protocols to improve stability and robustness. Previous studies have described various computational approaches to fuse single modality multicentre datasets. However, these surveys rarely focused on evaluation metrics and lacked a checklist for computational data harmonisation studies. In this systematic review, we summarise the computational data harmonisation approaches for multi-modality data in the digital healthcare field, including harmonisation strategies and evaluation metrics based on different theories. In addition, a comprehensive checklist that summarises common practices for data harmonisation studies is proposed to guide researchers to report their research findings more effectively. Last but not least, flowcharts presenting possible ways for methodology and metric selection are proposed and the limitations of different methods have been surveyed for future research.
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Affiliation(s)
- Yang Nan
- National Heart and Lung Institute, Imperial College London, London, Northern Ireland UK
| | - Javier Del Ser
- Department of Communications Engineering, University of the Basque Country UPV/EHU, Bilbao 48013, Spain
- TECNALIA, Basque Research and Technology Alliance (BRTA), Derio 48160, Spain
| | - Simon Walsh
- National Heart and Lung Institute, Imperial College London, London, Northern Ireland UK
| | - Carola Schönlieb
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, Northern Ireland UK
| | - Michael Roberts
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, Northern Ireland UK
- Oncology R&D, AstraZeneca, Cambridge, Northern Ireland UK
| | - Ian Selby
- Department of Radiology, University of Cambridge, Cambridge, Northern Ireland UK
| | - Kit Howard
- Clinical Data Interchange Standards Consortium, Austin, TX, United States of America
| | - John Owen
- Clinical Data Interchange Standards Consortium, Austin, TX, United States of America
| | - Jon Neville
- Clinical Data Interchange Standards Consortium, Austin, TX, United States of America
| | - Julien Guiot
- University Hospital of Liège (CHU Liège), Respiratory medicine department, Liège, Belgium
- University of Liege, Department of clinical sciences, Pneumology-Allergology, Liège, Belgium
| | - Benoit Ernst
- University Hospital of Liège (CHU Liège), Respiratory medicine department, Liège, Belgium
- University of Liege, Department of clinical sciences, Pneumology-Allergology, Liège, Belgium
| | | | | | - Marion I. Menzel
- Technische Hochschule Ingolstadt, Ingolstadt, Germany
- GE Healthcare GmbH, Munich, Germany
| | - Sean Walsh
- Radiomics (Oncoradiomics SA), Liège, Belgium
| | - Wim Vos
- Radiomics (Oncoradiomics SA), Liège, Belgium
| | - Nina Flerin
- Radiomics (Oncoradiomics SA), Liège, Belgium
| | | | | | - Avishek Chatterjee
- Department of Precision Medicine, Maastricht University, Maastricht, The Netherlands
| | - Henry Woodruff
- Department of Precision Medicine, Maastricht University, Maastricht, The Netherlands
| | - Philippe Lambin
- Department of Precision Medicine, Maastricht University, Maastricht, The Netherlands
| | - Leonor Cerdá-Alberich
- Medical Imaging Department, Hospital Universitari i Politècnic La Fe, Valencia, Spain
| | - Luis Martí-Bonmatí
- Medical Imaging Department, Hospital Universitari i Politècnic La Fe, Valencia, Spain
| | - Francisco Herrera
- Department of Computer Sciences and Artificial Intelligence, Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI) University of Granada, Granada, Spain
- Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Guang Yang
- National Heart and Lung Institute, Imperial College London, London, Northern Ireland UK
- Cardiovascular Research Centre, Royal Brompton Hospital, London, Northern Ireland UK
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, Northern Ireland UK
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7
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Luz DS, Lima TJ, Silva RR, Magalhães DM, Araujo FH. Automatic detection metastasis in breast histopathological images based on ensemble learning and color adjustment. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103564] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
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8
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Abbet C, Studer L, Fischer A, Dawson H, Zlobec I, Bozorgtabar B, Thiran JP. Self-Rule to Multi-Adapt: Generalized Multi-source Feature Learning Using Unsupervised Domain Adaptation for Colorectal Cancer Tissue Detection. Med Image Anal 2022; 79:102473. [DOI: 10.1016/j.media.2022.102473] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Revised: 03/07/2022] [Accepted: 05/03/2022] [Indexed: 10/18/2022]
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9
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Wu Y, Cheng M, Huang S, Pei Z, Zuo Y, Liu J, Yang K, Zhu Q, Zhang J, Hong H, Zhang D, Huang K, Cheng L, Shao W. Recent Advances of Deep Learning for Computational Histopathology: Principles and Applications. Cancers (Basel) 2022; 14:1199. [PMID: 35267505 PMCID: PMC8909166 DOI: 10.3390/cancers14051199] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Revised: 02/16/2022] [Accepted: 02/22/2022] [Indexed: 01/10/2023] Open
Abstract
With the remarkable success of digital histopathology, we have witnessed a rapid expansion of the use of computational methods for the analysis of digital pathology and biopsy image patches. However, the unprecedented scale and heterogeneous patterns of histopathological images have presented critical computational bottlenecks requiring new computational histopathology tools. Recently, deep learning technology has been extremely successful in the field of computer vision, which has also boosted considerable interest in digital pathology applications. Deep learning and its extensions have opened several avenues to tackle many challenging histopathological image analysis problems including color normalization, image segmentation, and the diagnosis/prognosis of human cancers. In this paper, we provide a comprehensive up-to-date review of the deep learning methods for digital H&E-stained pathology image analysis. Specifically, we first describe recent literature that uses deep learning for color normalization, which is one essential research direction for H&E-stained histopathological image analysis. Followed by the discussion of color normalization, we review applications of the deep learning method for various H&E-stained image analysis tasks such as nuclei and tissue segmentation. We also summarize several key clinical studies that use deep learning for the diagnosis and prognosis of human cancers from H&E-stained histopathological images. Finally, online resources and open research problems on pathological image analysis are also provided in this review for the convenience of researchers who are interested in this exciting field.
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Affiliation(s)
- Yawen Wu
- MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China; (Y.W.); (S.H.); (Z.P.); (Y.Z.); (J.L.); (K.Y.); (Q.Z.); (D.Z.)
| | - Michael Cheng
- Department of Medicine, Indiana University School of Medicine, Indianapolis, IN 46202, USA; (M.C.); (J.Z.); (K.H.)
- Regenstrief Institute, Indiana University, Indianapolis, IN 46202, USA
| | - Shuo Huang
- MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China; (Y.W.); (S.H.); (Z.P.); (Y.Z.); (J.L.); (K.Y.); (Q.Z.); (D.Z.)
| | - Zongxiang Pei
- MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China; (Y.W.); (S.H.); (Z.P.); (Y.Z.); (J.L.); (K.Y.); (Q.Z.); (D.Z.)
| | - Yingli Zuo
- MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China; (Y.W.); (S.H.); (Z.P.); (Y.Z.); (J.L.); (K.Y.); (Q.Z.); (D.Z.)
| | - Jianxin Liu
- MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China; (Y.W.); (S.H.); (Z.P.); (Y.Z.); (J.L.); (K.Y.); (Q.Z.); (D.Z.)
| | - Kai Yang
- MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China; (Y.W.); (S.H.); (Z.P.); (Y.Z.); (J.L.); (K.Y.); (Q.Z.); (D.Z.)
| | - Qi Zhu
- MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China; (Y.W.); (S.H.); (Z.P.); (Y.Z.); (J.L.); (K.Y.); (Q.Z.); (D.Z.)
| | - Jie Zhang
- Department of Medicine, Indiana University School of Medicine, Indianapolis, IN 46202, USA; (M.C.); (J.Z.); (K.H.)
- Regenstrief Institute, Indiana University, Indianapolis, IN 46202, USA
| | - Honghai Hong
- Department of Clinical Laboratory, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou 510006, China;
| | - Daoqiang Zhang
- MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China; (Y.W.); (S.H.); (Z.P.); (Y.Z.); (J.L.); (K.Y.); (Q.Z.); (D.Z.)
| | - Kun Huang
- Department of Medicine, Indiana University School of Medicine, Indianapolis, IN 46202, USA; (M.C.); (J.Z.); (K.H.)
- Regenstrief Institute, Indiana University, Indianapolis, IN 46202, USA
| | - Liang Cheng
- Departments of Pathology and Laboratory Medicine, Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Wei Shao
- MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China; (Y.W.); (S.H.); (Z.P.); (Y.Z.); (J.L.); (K.Y.); (Q.Z.); (D.Z.)
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Rashmi R, Prasad K, Udupa CBK. Breast histopathological image analysis using image processing techniques for diagnostic puposes: A methodological review. J Med Syst 2021; 46:7. [PMID: 34860316 PMCID: PMC8642363 DOI: 10.1007/s10916-021-01786-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Accepted: 10/21/2021] [Indexed: 12/24/2022]
Abstract
Breast cancer in women is the second most common cancer worldwide. Early detection of breast cancer can reduce the risk of human life. Non-invasive techniques such as mammograms and ultrasound imaging are popularly used to detect the tumour. However, histopathological analysis is necessary to determine the malignancy of the tumour as it analyses the image at the cellular level. Manual analysis of these slides is time consuming, tedious, subjective and are susceptible to human errors. Also, at times the interpretation of these images are inconsistent between laboratories. Hence, a Computer-Aided Diagnostic system that can act as a decision support system is need of the hour. Moreover, recent developments in computational power and memory capacity led to the application of computer tools and medical image processing techniques to process and analyze breast cancer histopathological images. This review paper summarizes various traditional and deep learning based methods developed to analyze breast cancer histopathological images. Initially, the characteristics of breast cancer histopathological images are discussed. A detailed discussion on the various potential regions of interest is presented which is crucial for the development of Computer-Aided Diagnostic systems. We summarize the recent trends and choices made during the selection of medical image processing techniques. Finally, a detailed discussion on the various challenges involved in the analysis of BCHI is presented along with the future scope.
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Affiliation(s)
- R Rashmi
- Manipal School of Information Sciences, Manipal Academy of Higher Education, Manipal, India
| | - Keerthana Prasad
- Manipal School of Information Sciences, Manipal Academy of Higher Education, Manipal, India
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11
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Lee HN, Seo HD, Kim EM, Han BS, Kang JS. Classification of Mouse Lung Metastatic Tumor with Deep Learning. Biomol Ther (Seoul) 2021; 30:179-183. [PMID: 34725310 PMCID: PMC8902456 DOI: 10.4062/biomolther.2021.130] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Revised: 08/31/2021] [Accepted: 09/13/2021] [Indexed: 11/26/2022] Open
Abstract
Traditionally, pathologists microscopically examine tissue sections to detect pathological lesions; the many slides that must be evaluated impose severe work burdens. Also, diagnostic accuracy varies by pathologist training and experience; better diagnostic tools are required. Given the rapid development of computer vision, automated deep learning is now used to classify microscopic images, including medical images. Here, we used a Inception-v3 deep learning model to detect mouse lung metastatic tumors via whole slide imaging (WSI); we cropped the images to 151 by 151 pixels. The images were divided into training (53.8%) and test (46.2%) sets (21,017 and 18,016 images, respectively). When images from lung tissue containing tumor tissues were evaluated, the model accuracy was 98.76%. When images from normal lung tissue were evaluated, the model accuracy (“no tumor”) was 99.87%. Thus, the deep learning model distinguished metastatic lesions from normal lung tissue. Our approach will allow the rapid and accurate analysis of various tissues.
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Affiliation(s)
- Ha Neul Lee
- Department of Biomedical, Laboratory Science, Namseoul University, Cheonan 31020, Republic of Korea
| | - Hong-Deok Seo
- Department of Industrial Promotion, Spatial Information Industry Promotion Agency, Seongnam 13487, Republic of Korea
| | - Eui-Myoung Kim
- Department of Spatial Information Engineering, Namseoul University, Cheonan 31020, Republic of Korea
| | - Beom Seok Han
- Department of Pharmaceutical Engineering, Hoseo University, Asan 31499, Republic of Korea
| | - Jin Seok Kang
- Department of Biomedical, Laboratory Science, Namseoul University, Cheonan 31020, Republic of Korea
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12
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Amgad M, Stovgaard ES, Balslev E, Thagaard J, Chen W, Dudgeon S, Sharma A, Kerner JK, Denkert C, Yuan Y, AbdulJabbar K, Wienert S, Savas P, Voorwerk L, Beck AH, Madabhushi A, Hartman J, Sebastian MM, Horlings HM, Hudeček J, Ciompi F, Moore DA, Singh R, Roblin E, Balancin ML, Mathieu MC, Lennerz JK, Kirtani P, Chen IC, Braybrooke JP, Pruneri G, Demaria S, Adams S, Schnitt SJ, Lakhani SR, Rojo F, Comerma L, Badve SS, Khojasteh M, Symmans WF, Sotiriou C, Gonzalez-Ericsson P, Pogue-Geile KL, Kim RS, Rimm DL, Viale G, Hewitt SM, Bartlett JMS, Penault-Llorca F, Goel S, Lien HC, Loibl S, Kos Z, Loi S, Hanna MG, Michiels S, Kok M, Nielsen TO, Lazar AJ, Bago-Horvath Z, Kooreman LFS, van der Laak JAWM, Saltz J, Gallas BD, Kurkure U, Barnes M, Salgado R, Cooper LAD. Report on computational assessment of Tumor Infiltrating Lymphocytes from the International Immuno-Oncology Biomarker Working Group. NPJ Breast Cancer 2020; 6:16. [PMID: 32411818 PMCID: PMC7217824 DOI: 10.1038/s41523-020-0154-2] [Citation(s) in RCA: 87] [Impact Index Per Article: 21.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2019] [Accepted: 02/18/2020] [Indexed: 02/07/2023] Open
Abstract
Assessment of tumor-infiltrating lymphocytes (TILs) is increasingly recognized as an integral part of the prognostic workflow in triple-negative (TNBC) and HER2-positive breast cancer, as well as many other solid tumors. This recognition has come about thanks to standardized visual reporting guidelines, which helped to reduce inter-reader variability. Now, there are ripe opportunities to employ computational methods that extract spatio-morphologic predictive features, enabling computer-aided diagnostics. We detail the benefits of computational TILs assessment, the readiness of TILs scoring for computational assessment, and outline considerations for overcoming key barriers to clinical translation in this arena. Specifically, we discuss: 1. ensuring computational workflows closely capture visual guidelines and standards; 2. challenges and thoughts standards for assessment of algorithms including training, preanalytical, analytical, and clinical validation; 3. perspectives on how to realize the potential of machine learning models and to overcome the perceptual and practical limits of visual scoring.
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Affiliation(s)
- Mohamed Amgad
- Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, GA USA
| | | | - Eva Balslev
- Department of Pathology, Herlev and Gentofte Hospital, University of Copenhagen, Herlev, Denmark
| | - Jeppe Thagaard
- DTU Compute, Department of Applied Mathematics, Technical University of Denmark, Lyngby, Denmark
- Visiopharm A/S, Hørsholm, Denmark
| | - Weijie Chen
- FDA/CDRH/OSEL/Division of Imaging, Diagnostics, and Software Reliability, Silver Spring, MD USA
| | - Sarah Dudgeon
- FDA/CDRH/OSEL/Division of Imaging, Diagnostics, and Software Reliability, Silver Spring, MD USA
| | - Ashish Sharma
- Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, GA USA
| | | | - Carsten Denkert
- Institut für Pathologie, Universitätsklinikum Gießen und Marburg GmbH, Standort Marburg, Philipps-Universität Marburg, Marburg, Germany
- Institute of Pathology, Philipps-University Marburg, Marburg, Germany
- German Cancer Consortium (DKTK), Partner Site Charité, Berlin, Germany
| | - Yinyin Yuan
- Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK
- Division of Molecular Pathology, The Institute of Cancer Research, London, UK
| | - Khalid AbdulJabbar
- Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK
- Division of Molecular Pathology, The Institute of Cancer Research, London, UK
| | - Stephan Wienert
- Institut für Pathologie, Universitätsklinikum Gießen und Marburg GmbH, Standort Marburg, Philipps-Universität Marburg, Marburg, Germany
| | - Peter Savas
- Division of Research and Cancer Medicine, Peter MacCallum Cancer Centre, University of Melbourne, Victoria, Australia
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Parkville, Australia
| | - Leonie Voorwerk
- Department of Tumor Biology & Immunology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | | | - Anant Madabhushi
- Case Western Reserve University, Department of Biomedical Engineering, Cleveland, OH USA
- Louis Stokes Cleveland Veterans Administration Medical Center, Cleveland, OH USA
| | - Johan Hartman
- Department of Oncology and Pathology, Karolinska Institutet and University Hospital, Solna, Sweden
| | - Manu M. Sebastian
- Departments of Epigenetics and Molecular Carcinogenesis, The University of Texas MD Anderson Cancer Center, Houston, TX USA
| | - Hugo M. Horlings
- Division of Molecular Pathology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Jan Hudeček
- Department of Research IT, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Francesco Ciompi
- Department of Pathology, Radboud University Medical Center, Nijmegen, The Netherlands
| | - David A. Moore
- Department of Pathology, UCL Cancer Institute, London, UK
| | - Rajendra Singh
- Department of Pathology and Laboratory Medicine, Icahn School of Medicine at Mount Sinai, New York, NY USA
| | - Elvire Roblin
- Université Paris-Saclay, Univ. Paris-Sud, Villejuif, France
| | - Marcelo Luiz Balancin
- Department of Pathology, Faculty of Medicine, University of São Paulo, São Paulo, Brazil
| | - Marie-Christine Mathieu
- Department of Medical Biology and Pathology, Gustave Roussy Cancer Campus, Villejuif, France
| | - Jochen K. Lennerz
- Department of Pathology, Massachusetts General Hospital, Boston, MA USA
| | - Pawan Kirtani
- Department of Histopathology, Manipal Hospitals Dwarka, New Delhi, India
| | - I-Chun Chen
- Department of Oncology, National Taiwan University Cancer Center, Taipei, Taiwan
| | - Jeremy P. Braybrooke
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Department of Medical Oncology, University Hospitals Bristol NHS Foundation Trust, Bristol, UK
| | - Giancarlo Pruneri
- Pathology Department, Fondazione IRCCS Istituto Nazionale Tumori and University of Milan, School of Medicine, Milan, Italy
| | | | - Sylvia Adams
- Laura and Isaac Perlmutter Cancer Center, NYU Langone Medical Center, New York, NY USA
| | - Stuart J. Schnitt
- Department of Pathology, Brigham and Women’s Hospital, Boston, MA USA
| | - Sunil R. Lakhani
- The University of Queensland Centre for Clinical Research and Pathology Queensland, Brisbane, Australia
| | - Federico Rojo
- Pathology Department, CIBERONC-Instituto de Investigación Sanitaria Fundación Jiménez Díaz (IIS-FJD), Madrid, Spain
- GEICAM-Spanish Breast Cancer Research Group, Madrid, Spain
| | - Laura Comerma
- Pathology Department, CIBERONC-Instituto de Investigación Sanitaria Fundación Jiménez Díaz (IIS-FJD), Madrid, Spain
- GEICAM-Spanish Breast Cancer Research Group, Madrid, Spain
| | - Sunil S. Badve
- Department of Pathology and Laboratory Medicine, Indiana University School of Medicine, Indianapolis, IN USA
| | | | - W. Fraser Symmans
- Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX USA
| | - Christos Sotiriou
- Breast Cancer Translational Research Laboratory, Institut Jules Bordet, Université Libre de Bruxelles (ULB), Brussels, Belgium
- ULB-Cancer Research Center (U-CRC) Université Libre de Bruxelles, Brussels, Belgium
| | - Paula Gonzalez-Ericsson
- Breast Cancer Program, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN USA
| | | | | | - David L. Rimm
- Department of Pathology, Yale University School of Medicine, New Haven, CT USA
| | - Giuseppe Viale
- Department of Pathology, IEO, European Institute of Oncology IRCCS & State University of Milan, Milan, Italy
| | - Stephen M. Hewitt
- Laboratory of Pathology, National Cancer Institute, National Institutes of Health, Bethesda, MD USA
| | - John M. S. Bartlett
- Ontario Institute for Cancer Research, Toronto, ON Canada
- Edinburgh Cancer Research Centre, Western General Hospital, Edinburgh, UK
| | - Frédérique Penault-Llorca
- Department of Pathology and Molecular Pathology, Centre Jean Perrin, Clermont-Ferrand, France
- UMR INSERM 1240, Universite Clermont Auvergne, Clermont-Ferrand, France
| | - Shom Goel
- Victorian Comprehensive Cancer Centre building, Peter MacCallum Cancer Centre, Melbourne, Victoria Australia
| | - Huang-Chun Lien
- Department of Pathology, National Taiwan University Hospital, Taipei, Taiwan
| | - Sibylle Loibl
- German Breast Group, c/o GBG-Forschungs GmbH, Neu-Isenburg, Germany
| | - Zuzana Kos
- Department of Pathology, BC Cancer, Vancouver, British Columbia Canada
| | - Sherene Loi
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Parkville, Australia
- Peter MacCallum Cancer Centre, Melbourne, Australia
| | - Matthew G. Hanna
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY USA
| | - Stefan Michiels
- Gustave Roussy, Universite Paris-Saclay, Villejuif, France
- Université Paris-Sud, Institut National de la Santé et de la Recherche Médicale, Villejuif, France
| | - Marleen Kok
- Division of Molecular Oncology & Immunology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
- Department of Medical Oncology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | | | - Alexander J. Lazar
- Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX USA
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX USA
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX USA
- Department of Dermatology, The University of Texas MD Anderson Cancer Center, Houston, TX USA
| | | | - Loes F. S. Kooreman
- GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
- Department of Pathology, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Jeroen A. W. M. van der Laak
- Department of Pathology, Radboud University Medical Center, Nijmegen, The Netherlands
- Center for Medical Image Science and Visualization, Linköping University, Linköping, Sweden
| | - Joel Saltz
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY USA
| | - Brandon D. Gallas
- FDA/CDRH/OSEL/Division of Imaging, Diagnostics, and Software Reliability, Silver Spring, MD USA
| | - Uday Kurkure
- Roche Tissue Diagnostics, Digital Pathology, Santa Clara, CA USA
| | - Michael Barnes
- Roche Diagnostics Information Solutions, Belmont, CA USA
| | - Roberto Salgado
- Division of Research and Cancer Medicine, Peter MacCallum Cancer Centre, University of Melbourne, Victoria, Australia
- Department of Pathology, GZA-ZNA Ziekenhuizen, Antwerp, Belgium
| | - Lee A. D. Cooper
- Department of Pathology, Northwestern University Feinberg School of Medicine, Chicago, IL USA
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13
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Nam S, Chong Y, Jung CK, Kwak TY, Lee JY, Park J, Rho MJ, Go H. Introduction to digital pathology and computer-aided pathology. J Pathol Transl Med 2020; 54:125-134. [PMID: 32045965 PMCID: PMC7093286 DOI: 10.4132/jptm.2019.12.31] [Citation(s) in RCA: 60] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2019] [Accepted: 12/31/2019] [Indexed: 12/13/2022] Open
Abstract
Digital pathology (DP) is no longer an unfamiliar term for pathologists, but it is still difficult for many pathologists to understand the engineering and mathematics concepts involved in DP. Computer-aided pathology (CAP) aids pathologists in diagnosis. However, some consider CAP a threat to the existence of pathologists and are skeptical of its clinical utility. Implementation of DP is very burdensome for pathologists because technical factors, impact on workflow, and information technology infrastructure must be considered. In this paper, various terms related to DP and computer-aided pathologic diagnosis are defined, current applications of DP are discussed, and various issues related to implementation of DP are outlined. The development of computer-aided pathologic diagnostic tools and their limitations are also discussed.
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Affiliation(s)
- Soojeong Nam
- Department of Pathology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Yosep Chong
- Department of Hospital Pathology, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Chan Kwon Jung
- Department of Hospital Pathology, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | | | - Ji Youl Lee
- Department of Urology, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Jihwan Park
- Catholic Cancer Research Institute, College of Medicine, The Catholic University of Korea, Seoul, Korea.,Department of Biomedicine & Health Sciences, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Mi Jung Rho
- Catholic Cancer Research Institute, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Heounjeong Go
- Department of Pathology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
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14
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Jung H, Lodhi B, Kang J. An automatic nuclei segmentation method based on deep convolutional neural networks for histopathology images. BMC Biomed Eng 2019; 1:24. [PMID: 32903361 PMCID: PMC7422516 DOI: 10.1186/s42490-019-0026-8] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2019] [Accepted: 09/02/2019] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND Since nuclei segmentation in histopathology images can provide key information for identifying the presence or stage of a disease, the images need to be assessed carefully. However, color variation in histopathology images, and various structures of nuclei are two major obstacles in accurately segmenting and analyzing histopathology images. Several machine learning methods heavily rely on hand-crafted features which have limitations due to manual thresholding. RESULTS To obtain robust results, deep learning based methods have been proposed. Deep convolutional neural networks (DCNN) used for automatically extracting features from raw image data have been proven to achieve great performance. Inspired by such achievements, we propose a nuclei segmentation method based on DCNNs. To normalize the color of histopathology images, we use a deep convolutional Gaussian mixture color normalization model which is able to cluster pixels while considering the structures of nuclei. To segment nuclei, we use Mask R-CNN which achieves state-of-the-art object segmentation performance in the field of computer vision. In addition, we perform multiple inference as a post-processing step to boost segmentation performance. We evaluate our segmentation method on two different datasets. The first dataset consists of histopathology images of various organ while the other consists histopathology images of the same organ. Performance of our segmentation method is measured in various experimental setups at the object-level and the pixel-level. In addition, we compare the performance of our method with that of existing state-of-the-art methods. The experimental results show that our nuclei segmentation method outperforms the existing methods. CONCLUSIONS We propose a nuclei segmentation method based on DCNNs for histopathology images. The proposed method which uses Mask R-CNN with color normalization and multiple inference post-processing provides robust nuclei segmentation results. Our method also can facilitate downstream nuclei morphological analyses as it provides high-quality features extracted from histopathology images.
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Affiliation(s)
- Hwejin Jung
- Department of Computer Science and Engineering, Korea University, Seoul, Republic of Korea
| | - Bilal Lodhi
- Department of Computer Science and Engineering, Korea University, Seoul, Republic of Korea
| | - Jaewoo Kang
- Department of Computer Science and Engineering, Korea University, Seoul, Republic of Korea
- Interdisciplinary Graduate Program in Bioinformatics, Korea University, Seoul, Republic of Korea
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15
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Abels E, Pantanowitz L, Aeffner F, Zarella MD, van der Laak J, Bui MM, Vemuri VN, Parwani AV, Gibbs J, Agosto-Arroyo E, Beck AH, Kozlowski C. Computational pathology definitions, best practices, and recommendations for regulatory guidance: a white paper from the Digital Pathology Association. J Pathol 2019; 249:286-294. [PMID: 31355445 PMCID: PMC6852275 DOI: 10.1002/path.5331] [Citation(s) in RCA: 213] [Impact Index Per Article: 42.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2019] [Revised: 07/18/2019] [Accepted: 07/26/2019] [Indexed: 12/27/2022]
Abstract
In this white paper, experts from the Digital Pathology Association (DPA) define terminology and concepts in the emerging field of computational pathology, with a focus on its application to histology images analyzed together with their associated patient data to extract information. This review offers a historical perspective and describes the potential clinical benefits from research and applications in this field, as well as significant obstacles to adoption. Best practices for implementing computational pathology workflows are presented. These include infrastructure considerations, acquisition of training data, quality assessments, as well as regulatory, ethical, and cyber-security concerns. Recommendations are provided for regulators, vendors, and computational pathology practitioners in order to facilitate progress in the field. © 2019 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of Pathological Society of Great Britain and Ireland.
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Affiliation(s)
- Esther Abels
- Regulatory and Clinical Affairs, PathAI, Boston, MA, USA
| | - Liron Pantanowitz
- Department of Pathology, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Famke Aeffner
- Amgen Research, Comparative Biology and Safety Sciences, Amgen Inc., South San Francisco, CA, USA
| | - Mark D Zarella
- Department of Pathology and Laboratory Medicine, Drexel University College of Medicine, Philadelphia, PA, USA
| | - Jeroen van der Laak
- Department of Pathology, Radboud University Medical Center, Nijmegen, The Netherlands.,Center for Medical Image Science and Visualization, Linköping University, Linköping, Sweden
| | - Marilyn M Bui
- Department of Pathology, Moffitt Cancer Center, Tampa, FL, USA
| | - Venkata Np Vemuri
- Data Science Department, Chan Zuckerberg Biohub, San Francisco, CA, USA
| | - Anil V Parwani
- Department of Pathology, The Ohio State University, Columbus, OH, USA
| | - Jeff Gibbs
- Hyman, Phelps & McNamara, P.C, Washington, DC, USA
| | | | | | - Cleopatra Kozlowski
- Department of Development Sciences, Genentech Inc., South San Francisco, CA, USA
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16
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Choudhary A, Wu H, Tong L, Wang MD. Learning to Evaluate Color Similarity for Histopathology Images using Triplet Networks. ACM-BCB ... ... : THE ... ACM CONFERENCE ON BIOINFORMATICS, COMPUTATIONAL BIOLOGY AND BIOMEDICINE. ACM CONFERENCE ON BIOINFORMATICS, COMPUTATIONAL BIOLOGY AND BIOMEDICINE 2019; 2019:466-474. [PMID: 32558828 PMCID: PMC7302047 DOI: 10.1145/3307339.3342170] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Stain normalization is a crucial pre-processing step for histopathological image processing, and can help improve the accuracy of downstream tasks such as segmentation and classification. To evaluate the effectiveness of stain normalization methods, various metrics based on color-perceptual similarity and stain color evaluation have been proposed. However, there still exists a huge gap between metric evaluation and human perception, given the limited explainability power of existing metrics and inability to combine color and semantic information efficiently. Inspired by the effectiveness of deep neural networks in evaluating perceptual similarity of natural images, in this paper, we propose TriNet-P, a color-perceptual similarity metric for whole slide images, based on deep metric embeddings. We evaluate the proposed approach using four publicly available breast cancer histological datasets. The benefit of our approach is its representation efficiency of the perceptual factors associated with H&E stained images with minimal human intervention. We show that our metric can capture the semantic similarities, both at subject (patient) and laboratory levels, and leads to better performance in image retrieval and clustering tasks.
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Affiliation(s)
| | - Hang Wu
- Georgia Institute of Technology Atlanta, GA
| | - Li Tong
- Georgia Institute of Technology and Emory University Atlanta, GA
| | - May D Wang
- Georgia Institute of Technology and Emory University Atlanta, GA
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17
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Ren J, Hacihaliloglu I, Singer EA, Foran DJ, Qi X. Unsupervised Domain Adaptation for Classification of Histopathology Whole-Slide Images. Front Bioeng Biotechnol 2019; 7:102. [PMID: 31158269 PMCID: PMC6529804 DOI: 10.3389/fbioe.2019.00102] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2018] [Accepted: 04/23/2019] [Indexed: 11/13/2022] Open
Abstract
Computational image analysis is one means for evaluating digitized histopathology specimens that can increase the reproducibility and reliability with which cancer diagnoses are rendered while simultaneously providing insight as to the underlying mechanisms of disease onset and progression. A major challenge that is confronted when analyzing samples that have been prepared at disparate laboratories and institutions is that the algorithms used to assess the digitized specimens often exhibit heterogeneous staining characteristics because of slight differences in incubation times and the protocols used to prepare the samples. Unfortunately, such variations can render a prediction model learned from one batch of specimens ineffective for characterizing an ensemble originating from another site. In this work, we propose to adopt unsupervised domain adaptation to effectively transfer the discriminative knowledge obtained from any given source domain to the target domain without requiring any additional labeling or annotation of images at the target site. In this paper, our team investigates the use of two approaches for performing the adaptation: (1) color normalization and (2) adversarial training. The adversarial training strategy is implemented through the use of convolutional neural networks to find an invariant feature space and Siamese architecture within the target domain to add a regularization that is appropriate for the entire set of whole-slide images. The adversarial adaptation results in significant classification improvement compared with the baseline models under a wide range of experimental settings.
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Affiliation(s)
- Jian Ren
- Department of Electrical and Computer Engineering, Rutgers University, Piscataway, NJ, United States
| | - Ilker Hacihaliloglu
- Department of Biomedical Engineering, Rutgers University, Piscataway, NJ, United States
| | - Eric A. Singer
- Section of Urologic Oncology, Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, United States
- Center for Biomedical Imaging and Informatics, Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, United States
| | - David J. Foran
- Center for Biomedical Imaging and Informatics, Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, United States
| | - Xin Qi
- Center for Biomedical Imaging and Informatics, Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, United States
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18
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Aeffner F, Zarella MD, Buchbinder N, Bui MM, Goodman MR, Hartman DJ, Lujan GM, Molani MA, Parwani AV, Lillard K, Turner OC, Vemuri VNP, Yuil-Valdes AG, Bowman D. Introduction to Digital Image Analysis in Whole-slide Imaging: A White Paper from the Digital Pathology Association. J Pathol Inform 2019; 10:9. [PMID: 30984469 PMCID: PMC6437786 DOI: 10.4103/jpi.jpi_82_18] [Citation(s) in RCA: 186] [Impact Index Per Article: 37.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2018] [Accepted: 12/11/2018] [Indexed: 12/22/2022] Open
Abstract
The advent of whole-slide imaging in digital pathology has brought about the advancement of computer-aided examination of tissue via digital image analysis. Digitized slides can now be easily annotated and analyzed via a variety of algorithms. This study reviews the fundamentals of tissue image analysis and aims to provide pathologists with basic information regarding the features, applications, and general workflow of these new tools. The review gives an overview of the basic categories of software solutions available, potential analysis strategies, technical considerations, and general algorithm readouts. Advantages and limitations of tissue image analysis are discussed, and emerging concepts, such as artificial intelligence and machine learning, are introduced. Finally, examples of how digital image analysis tools are currently being used in diagnostic laboratories, translational research, and drug development are discussed.
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Affiliation(s)
- Famke Aeffner
- Amgen Inc., Amgen Research, Comparative Biology and Safety Sciences, South San Francisco, CA, USA
| | - Mark D Zarella
- Department of Pathology and Laboratory Medicine, Drexel University, College of Medicine, Philadelphia, PA, USA
| | | | - Marilyn M Bui
- Department of Pathology, Moffitt Cancer Center, Tampa, FL, USA
| | | | | | | | - Mariam A Molani
- Department of Pathology and Microbiology, University of Nebraska Medical Center, Omaha, NE, USA
| | - Anil V Parwani
- The Ohio State University Medical Center, Columbus, OH, USA
| | | | - Oliver C Turner
- Novartis, Novartis Institutes for BioMedical Research, Preclinical Safety, East Hannover, NJ, USA
| | | | - Ana G Yuil-Valdes
- Department of Pathology and Microbiology, University of Nebraska Medical Center, Omaha, NE, USA
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19
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Zarella MD, Quaschnick MR, Breen DE, Garcia FU. Estimation of Fine-Scale Histologic Features at Low Magnification. Arch Pathol Lab Med 2018; 142:1394-1402. [PMID: 29911887 DOI: 10.5858/arpa.2017-0380-oa] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
CONTEXT.— Whole-slide imaging has ushered in a new era of technology that has fostered the use of computational image analysis for diagnostic support and has begun to transfer the act of analyzing a slide to computer monitors. Due to the overwhelming amount of detail available in whole-slide images, analytic procedures-whether computational or visual-often operate at magnifications lower than the magnification at which the image was acquired. As a result, a corresponding reduction in image resolution occurs. It is unclear how much information is lost when magnification is reduced, and whether the rich color attributes of histologic slides can aid in reconstructing some of that information. OBJECTIVE.— To examine the correspondence between the color and spatial properties of whole-slide images to elucidate the impact of resolution reduction on the histologic attributes of the slide. DESIGN.— We simulated image resolution reduction and modeled its effect on classification of the underlying histologic structure. By harnessing measured histologic features and the intrinsic spatial relationships between histologic structures, we developed a predictive model to estimate the histologic composition of tissue in a manner that exceeds the resolution of the image. RESULTS.— Reduction in resolution resulted in a significant loss of the ability to accurately characterize histologic components at magnifications less than ×10. By utilizing pixel color, this ability was improved at all magnifications. CONCLUSIONS.— Multiscale analysis of histologic images requires an adequate understanding of the limitations imposed by image resolution. Our findings suggest that some of these limitations may be overcome with computational modeling.
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Affiliation(s)
| | | | | | - Fernando U Garcia
- From the Departments of Pathology & Laboratory Medicine (Dr Zarella) and Computer Science (Mr Quaschnick and Dr Breen), Drexel University, Philadelphia, Pennsylvania; and the Department of Pathology & Laboratory Medicine, Cancer Treatment Centers of America, Eastern Regional Medical Center, Philadelphia, Pennsylvania (Dr Garcia)
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