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Fatemi MY, Lu Y, Diallo AB, Srinivasan G, Azher ZL, Christensen BC, Salas LA, Tsongalis GJ, Palisoul SM, Perreard L, Kolling FW, Vaickus LJ, Levy JJ. The Overlooked Role of Specimen Preparation in Bolstering Deep Learning-Enhanced Spatial Transcriptomics Workflows. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.10.09.23296700. [PMID: 37873287 PMCID: PMC10593052 DOI: 10.1101/2023.10.09.23296700] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
The application of deep learning methods to spatial transcriptomics has shown promise in unraveling the complex relationships between gene expression patterns and tissue architecture as they pertain to various pathological conditions. Deep learning methods that can infer gene expression patterns directly from tissue histomorphology can expand the capability to discern spatial molecular markers within tissue slides. However, current methods utilizing these techniques are plagued by substantial variability in tissue preparation and characteristics, which can hinder the broader adoption of these tools. Furthermore, training deep learning models using spatial transcriptomics on small study cohorts remains a costly endeavor. Necessitating novel tissue preparation processes enhance assay reliability, resolution, and scalability. This study investigated the impact of an enhanced specimen processing workflow for facilitating a deep learning-based spatial transcriptomics assessment. The enhanced workflow leveraged the flexibility of the Visium CytAssist assay to permit automated H&E staining (e.g., Leica Bond) of tissue slides, whole-slide imaging at 40x-resolution, and multiplexing of tissue sections from multiple patients within individual capture areas for spatial transcriptomics profiling. Using a cohort of thirteen pT3 stage colorectal cancer (CRC) patients, we compared the efficacy of deep learning models trained on slide prepared using an enhanced workflow as compared to the traditional workflow which leverages manual tissue staining and standard imaging of tissue slides. Leveraging Inceptionv3 neural networks, we aimed to predict gene expression patterns across matched serial tissue sections, each stemming from a distinct workflow but aligned based on persistent histological structures. Findings indicate that the enhanced workflow considerably outperformed the traditional spatial transcriptomics workflow. Gene expression profiles predicted from enhanced tissue slides also yielded expression patterns more topologically consistent with the ground truth. This led to enhanced statistical precision in pinpointing biomarkers associated with distinct spatial structures. These insights can potentially elevate diagnostic and prognostic biomarker detection by broadening the range of spatial molecular markers linked to metastasis and recurrence. Future endeavors will further explore these findings to enrich our comprehension of various diseases and uncover molecular pathways with greater nuance. Combining deep learning with spatial transcriptomics provides a compelling avenue to enrich our understanding of tumor biology and improve clinical outcomes. For results of the highest fidelity, however, effective specimen processing is crucial, and fostering collaboration between histotechnicians, pathologists, and genomics specialists is essential to herald this new era in spatial transcriptomics-driven cancer research.
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Haghighat M, Browning L, Sirinukunwattana K, Malacrino S, Khalid Alham N, Colling R, Cui Y, Rakha E, Hamdy FC, Verrill C, Rittscher J. Automated quality assessment of large digitised histology cohorts by artificial intelligence. Sci Rep 2022; 12:5002. [PMID: 35322056 PMCID: PMC8943120 DOI: 10.1038/s41598-022-08351-5] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Accepted: 03/03/2022] [Indexed: 02/07/2023] Open
Abstract
Research using whole slide images (WSIs) of histopathology slides has increased exponentially over recent years. Glass slides from retrospective cohorts, some with patient follow-up data are digitised for the development and validation of artificial intelligence (AI) tools. Such resources, therefore, become very important, with the need to ensure that their quality is of the standard necessary for downstream AI development. However, manual quality control of large cohorts of WSIs by visual assessment is unfeasible, and whilst quality control AI algorithms exist, these focus on bespoke aspects of image quality, e.g. focus, or use traditional machine-learning methods, which are unable to classify the range of potential image artefacts that should be considered. In this study, we have trained and validated a multi-task deep neural network to automate the process of quality control of a large retrospective cohort of prostate cases from which glass slides have been scanned several years after production, to determine both the usability of the images at the diagnostic level (considered in this study to be the minimal standard for research) and the common image artefacts present. Using a two-layer approach, quality overlays of WSIs were generated from a quality assessment (QA) undertaken at patch-level at \documentclass[12pt]{minimal}
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\begin{document}$$5\times$$\end{document}5× magnification. From these quality overlays the slide-level quality scores were predicted and then compared to those generated by three specialist urological pathologists, with a Pearson correlation of 0.89 for overall ‘usability’ (at a diagnostic level), and 0.87 and 0.82 for focus and H&E staining quality scores respectively. To demonstrate its wider potential utility, we subsequently applied our QA pipeline to the TCGA prostate cancer cohort and to a colorectal cancer cohort, for comparison. Our model, designated as PathProfiler, indicates comparable predicted usability of images from the cohorts assessed (86–90% of WSIs predicted to be usable), and perhaps more significantly is able to predict WSIs that could benefit from an intervention such as re-scanning or re-staining for quality improvement. We have shown in this study that AI can be used to automate the process of quality control of large retrospective WSI cohorts to maximise their utility for research.
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Affiliation(s)
- Maryam Haghighat
- Department of Engineering Science, Institute of Biomedical Engineering (IBME), University of Oxford, Oxford, UK. .,CSIRO, Brisbane, QLD, Australia.
| | - Lisa Browning
- Department of Cellular Pathology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK.,NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, Oxfordshire, UK
| | - Korsuk Sirinukunwattana
- Department of Engineering Science, Institute of Biomedical Engineering (IBME), University of Oxford, Oxford, UK
| | - Stefano Malacrino
- Nuffield Department of Surgical Sciences, University of Oxford, John Radcliffe Hospital, Oxford, UK
| | - Nasullah Khalid Alham
- Department of Engineering Science, Institute of Biomedical Engineering (IBME), University of Oxford, Oxford, UK
| | - Richard Colling
- Department of Cellular Pathology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK.,Nuffield Department of Surgical Sciences, University of Oxford, John Radcliffe Hospital, Oxford, UK
| | - Ying Cui
- Nuffield Department of Surgical Sciences, University of Oxford, John Radcliffe Hospital, Oxford, UK
| | - Emad Rakha
- School of Medicine, University of Nottingham, Nottingham, UK
| | - Freddie C Hamdy
- NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, Oxfordshire, UK.,Nuffield Department of Surgical Sciences, University of Oxford, John Radcliffe Hospital, Oxford, UK
| | - Clare Verrill
- Department of Cellular Pathology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK.,NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, Oxfordshire, UK.,Nuffield Department of Surgical Sciences, University of Oxford, John Radcliffe Hospital, Oxford, UK
| | - Jens Rittscher
- Department of Engineering Science, Institute of Biomedical Engineering (IBME), University of Oxford, Oxford, UK. .,NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, Oxfordshire, UK.
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Pérez-Bueno F, Serra JG, Vega M, Mateos J, Molina R, Katsaggelos AK. Bayesian K-SVD for H and E blind color deconvolution. Applications to stain normalization, data augmentation and cancer classification. Comput Med Imaging Graph 2022; 97:102048. [DOI: 10.1016/j.compmedimag.2022.102048] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Revised: 11/04/2021] [Accepted: 02/05/2022] [Indexed: 12/17/2022]
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4
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Agraz JL, Grenko CM, Chen AA, Viaene AN, Nasrallah MD, Pati S, Kurc T, Saltz J, Feldman MD, Akbari H, Sharma P, Shinohara RT, Bakas S. Robust Image Population Based Stain Color Normalization: How Many Reference Slides Are Enough? IEEE OPEN JOURNAL OF ENGINEERING IN MEDICINE AND BIOLOGY 2022; 3:218-226. [PMID: 36860498 PMCID: PMC9970045 DOI: 10.1109/ojemb.2023.3234443] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Revised: 11/08/2022] [Accepted: 01/01/2023] [Indexed: 01/06/2023] Open
Abstract
Histopathologic evaluation of Hematoxylin & Eosin (H&E) stained slides is essential for disease diagnosis, revealing tissue morphology, structure, and cellular composition. Variations in staining protocols and equipment result in images with color nonconformity. Although pathologists compensate for color variations, these disparities introduce inaccuracies in computational whole slide image (WSI) analysis, accentuating data domain shift and degrading generalization. Current state-of-the-art normalization methods employ a single WSI as reference, but selecting a single WSI representative of a complete WSI-cohort is infeasible, inadvertently introducing normalization bias. We seek the optimal number of slides to construct a more representative reference based on composite/aggregate of multiple H&E density histograms and stain-vectors, obtained from a randomly selected WSI population (WSI-Cohort-Subset). We utilized 1,864 IvyGAP WSIs as a WSI-cohort, and built 200 WSI-Cohort-Subsets varying in size (from 1 to 200 WSI-pairs) using randomly selected WSIs. The WSI-pairs' mean Wasserstein Distances and WSI-Cohort-Subsets' standard deviations were calculated. The Pareto Principle defined the optimal WSI-Cohort-Subset size. The WSI-cohort underwent structure-preserving color normalization using the optimal WSI-Cohort-Subset histogram and stain-vector aggregates. Numerous normalization permutations support WSI-Cohort-Subset aggregates as representative of a WSI-cohort through WSI-cohort CIELAB color space swift convergence, as a result of the law of large numbers and shown as a power law distribution. We show normalization at the optimal (Pareto Principle) WSI-Cohort-Subset size and corresponding CIELAB convergence: a) Quantitatively, using 500 WSI-cohorts; b) Quantitatively, using 8,100 WSI-regions; c) Qualitatively, using 30 cellular tumor normalization permutations. Aggregate-based stain normalization may contribute in increasing computational pathology robustness, reproducibility, and integrity.
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Affiliation(s)
- Jose L Agraz
- Center for Biomedical Image Computing and Analytics (CBICA) Philaldelphia PA 19139 USA.,Department of Pathology and Laboratory Medicine, Perelman School of Medicine Philaldelphia PA 19139 USA.,Department of Radiology at Perelman School of MedicineUniversity of Pennsylvania Philaldelphia PA 19139 USA
| | - Caleb M Grenko
- Department of Pathology and Laboratory Medicine, Perelman School of MedicineUniversity of Pennsylvania and the Center for Interdisciplinary Studies Davidson College NC 28035 USA
| | - Andrew A Chen
- Penn Statistical Imaging and Visualization Endeavor (PennSIVE)University of Pennsylvania Philaldelphia PA 19139 USA
| | - Angela N Viaene
- Department of Pathology and Laboratory Medicine, Children's Hospital of PhiladelphiaUniversity of Pennsylvania Philaldelphia PA 19139 USA
| | - MacLean D Nasrallah
- Department of Pathology and Laboratory Medicine, Perelman School of MedicineUniversity of Pennsylvania Philaldelphia PA 19139 USA
| | - Sarthak Pati
- CBICA and Department of Pathology and Laboratory Medicine, Perelman School of MedicineUniversity of Pennsylvania Philaldelphia PA 19139 USA.,Department of Radiology at Perelman School of MedicineUniversity of Pennsylvania Philaldelphia PA 19139 USA
| | - Tahsin Kurc
- Department of Biomedical InformaticsStony Brook University Stony Brook NY 11794-0751 USA
| | - Joel Saltz
- Department of Biomedical InformaticsStony Brook University Stony Brook NY 11794-0751 USA
| | - Michael D Feldman
- Department of Pathology and Laboratory Medicine, Perelman School of MedicineUniversity of Pennsylvania Philaldelphia PA 19139 USA
| | - Hamed Akbari
- CBICA and the Department of Radiology, Perelman School of MedicineUniversity of Pennsylvania Philaldelphia PA 19139 USA
| | | | - Russell T Shinohara
- CBICA and the Penn Statistical Imaging and Visualization Endeavor (PennSIVE)University of Pennsylvania Philaldelphia PA 19139 USA
| | - Spyridon Bakas
- CBICA, and the Department of Pathology and Laboratory Medicine, Perelman School of MedicineUniversity of Pennsylvania Philaldelphia PA 19139 USA.,Department of Radiology, Perelman School of MedicineUniversity of Pennsylvania Philaldelphia PA 19139 USA
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5
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Runz M, Rusche D, Schmidt S, Weihrauch MR, Hesser J, Weis CA. Normalization of HE-stained histological images using cycle consistent generative adversarial networks. Diagn Pathol 2021; 16:71. [PMID: 34362386 PMCID: PMC8349020 DOI: 10.1186/s13000-021-01126-y] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2021] [Accepted: 07/05/2021] [Indexed: 02/05/2023] Open
Abstract
Background Histological images show strong variance (e.g. illumination, color, staining quality) due to differences in image acquisition, tissue processing, staining, etc. This can impede downstream image analysis such as staining intensity evaluation or classification. Methods to reduce these variances are called image normalization techniques. Methods In this paper, we investigate the potential of CycleGAN (cycle consistent Generative Adversarial Network) for color normalization in hematoxylin-eosin stained histological images using daily clinical data with consideration of the variability of internal staining protocol variations. The network consists of a generator network GB that learns to map an image X from a source domain A to a target domain B, i.e. GB:XA→XB. In addition, a discriminator network DB is trained to distinguish whether an image from domain B is real or generated. The same process is applied to another generator-discriminator pair (GA,DA), for the inverse mapping GA:XB→XA. Cycle consistency ensures that a generated image is close to its original when being mapped backwards (GA(GB(XA))≈XA and vice versa). We validate the CycleGAN approach on a breast cancer challenge and a follicular thyroid carcinoma data set for various stain variations. We evaluate the quality of the generated images compared to the original images using similarity measures. In addition, we apply stain normalization on pathological lymph node data from our institute and test the gain from normalization on a ResNet classifier pre-trained on the Camelyon16 data set. Results Qualitative results of the images generated by our network are compared to original color distributions. Our evaluation indicates that by mapping images to a target domain, the similarity training images from that domain improves up to 96%. We also achieve a high cycle consistency for the generator networks by obtaining similarity indices greater than 0.9. When applying the CycleGAN normalization to HE-stain images from our institute the kappa-value of the ResNet-model that is only trained on Camelyon16 data is increased more than 50%. Conclusions CycleGANs have proven to efficiently normalize HE-stained images. The approach compensates for deviations resulting from image acquisition (e.g. different scanning devices) as well as from tissue staining (e.g. different staining protocols), and thus overcomes the staining variations in images from various institutions.The code is publicly available at https://github.com/m4ln/stainTransfer_CycleGAN_pytorch. The data set supporting the solutions is available at 10.11588/data/8LKEZF.
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Affiliation(s)
- Marlen Runz
- Institute of Pathology, University Medical Centre Mannheim, Heidelberg University, Mannheim, Germany. .,Mannheim Institute for Intelligent Systems in Medicine (MIISM), Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany.
| | - Daniel Rusche
- Institute of Pathology, University Medical Centre Mannheim, Heidelberg University, Mannheim, Germany
| | - Stefan Schmidt
- Center for Mass Spectrometry and Optical Spectroscopy (CeMOS), Mannheim University of Applied Sciences, Mannheim, Germany
| | | | - Jürgen Hesser
- Mannheim Institute for Intelligent Systems in Medicine (MIISM), Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany.,Interdisciplinary Center for Scientific Computing (IWR), Heidelberg University, Heidelberg, Germany.,Central Institute for Computer Engineering (ZITI), Heidelberg University, Heidelberg, Germany
| | - Cleo-Aron Weis
- Institute of Pathology, University Medical Centre Mannheim, Heidelberg University, Mannheim, Germany
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6
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Katare P, Gorthi SS. Recent technical advances in whole slide imaging instrumentation. J Microsc 2021; 284:103-117. [PMID: 34254690 DOI: 10.1111/jmi.13049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2021] [Revised: 07/05/2021] [Accepted: 07/06/2021] [Indexed: 11/28/2022]
Abstract
Microscopic observation of biological specimen smears is the mainstay of diagnostic pathology, as defined by the Digital Pathology Association. Though automated systems for this are commercially available, their bulky size and high cost renders them unusable for remote areas. The research community is investing much effort towards building equivalent but portable, low-cost systems. An overview of such research is presented here, including a comparative analysis of recent reports. This paper also reviews recently reported systems for automated staining and smear formation, including microfluidic devices; and optical and computational automated microscopy systems including smartphone-based devices. Image pre-processing and analysis methods for automated diagnosis are also briefly discussed. It concludes with a set of foreseeable research directions that could lead to affordable, integrated and accurate whole slide imaging systems.
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Affiliation(s)
- Prateek Katare
- Department of Instrumentation and Applied Physics, Indian Institute of Science, Bangalore, India
| | - Sai Siva Gorthi
- Department of Instrumentation and Applied Physics, Indian Institute of Science, Bangalore, India
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7
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Ayyad SM, Shehata M, Shalaby A, Abou El-Ghar M, Ghazal M, El-Melegy M, Abdel-Hamid NB, Labib LM, Ali HA, El-Baz A. Role of AI and Histopathological Images in Detecting Prostate Cancer: A Survey. SENSORS (BASEL, SWITZERLAND) 2021; 21:2586. [PMID: 33917035 PMCID: PMC8067693 DOI: 10.3390/s21082586] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Revised: 03/29/2021] [Accepted: 04/04/2021] [Indexed: 02/07/2023]
Abstract
Prostate cancer is one of the most identified cancers and second most prevalent among cancer-related deaths of men worldwide. Early diagnosis and treatment are substantial to stop or handle the increase and spread of cancer cells in the body. Histopathological image diagnosis is a gold standard for detecting prostate cancer as it has different visual characteristics but interpreting those type of images needs a high level of expertise and takes too much time. One of the ways to accelerate such an analysis is by employing artificial intelligence (AI) through the use of computer-aided diagnosis (CAD) systems. The recent developments in artificial intelligence along with its sub-fields of conventional machine learning and deep learning provide new insights to clinicians and researchers, and an abundance of research is presented specifically for histopathology images tailored for prostate cancer. However, there is a lack of comprehensive surveys that focus on prostate cancer using histopathology images. In this paper, we provide a very comprehensive review of most, if not all, studies that handled the prostate cancer diagnosis using histopathological images. The survey begins with an overview of histopathological image preparation and its challenges. We also briefly review the computing techniques that are commonly applied in image processing, segmentation, feature selection, and classification that can help in detecting prostate malignancies in histopathological images.
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Affiliation(s)
- Sarah M. Ayyad
- Computers and Systems Department, Faculty of Engineering, Mansoura University, Mansoura 35511, Egypt; (S.M.A.); (N.B.A.-H.); (L.M.L.); (H.A.A.)
| | - Mohamed Shehata
- BioImaging Laboratory, Bioengineering Department, University of Louisville, Louisville, KY 40292, USA; (M.S.); (A.S.)
| | - Ahmed Shalaby
- BioImaging Laboratory, Bioengineering Department, University of Louisville, Louisville, KY 40292, USA; (M.S.); (A.S.)
| | - Mohamed Abou El-Ghar
- Department of Radiology, Urology and Nephrology Center, Mansoura University, Mansoura 35516, Egypt;
| | - Mohammed Ghazal
- Department of Electrical and Computer Engineering, College of Engineering, Abu Dhabi University, Abu Dhabi 59911, United Arab Emirates;
| | - Moumen El-Melegy
- Department of Electrical Engineering, Assiut University, Assiut 71511, Egypt;
| | - Nahla B. Abdel-Hamid
- Computers and Systems Department, Faculty of Engineering, Mansoura University, Mansoura 35511, Egypt; (S.M.A.); (N.B.A.-H.); (L.M.L.); (H.A.A.)
| | - Labib M. Labib
- Computers and Systems Department, Faculty of Engineering, Mansoura University, Mansoura 35511, Egypt; (S.M.A.); (N.B.A.-H.); (L.M.L.); (H.A.A.)
| | - H. Arafat Ali
- Computers and Systems Department, Faculty of Engineering, Mansoura University, Mansoura 35511, Egypt; (S.M.A.); (N.B.A.-H.); (L.M.L.); (H.A.A.)
| | - Ayman El-Baz
- BioImaging Laboratory, Bioengineering Department, University of Louisville, Louisville, KY 40292, USA; (M.S.); (A.S.)
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8
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Zheng Y, Jiang Z, Zhang H, Xie F, Hu D, Sun S, Shi J, Xue C. Stain Standardization Capsule for Application-Driven Histopathological Image Normalization. IEEE J Biomed Health Inform 2021; 25:337-347. [PMID: 32248128 DOI: 10.1109/jbhi.2020.2983206] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Color consistency is crucial to developing robust deep learning methods for histopathological image analysis. With the increasing application of digital histopathological slides, the deep learning methods are probably developed based on the data from multiple medical centers. This requirement makes it a challenging task to normalize the color variance of histopathological images from different medical centers. In this paper, we propose a novel color standardization module named stain standardization capsule based on the capsule network and the corresponding dynamic routing algorithm. The proposed module can learn and generate uniform stain separation outputs for histopathological images in various color appearance without the reference to manually selected template images. The proposed module is light and can be jointly trained with the application-driven CNN model. The proposed method was validated on three histopathology datasets and a cytology dataset, and was compared with state-of-the-art methods. The experimental results have demonstrated that the SSC module is effective in improving the performance of histopathological image analysis and has achieved the best performance in the compared methods.
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9
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Ma B, Guo Y, Hu W, Yuan F, Zhu Z, Yu Y, Zou H. Artificial Intelligence-Based Multiclass Classification of Benign or Malignant Mucosal Lesions of the Stomach. Front Pharmacol 2020; 11:572372. [PMID: 33132910 PMCID: PMC7562716 DOI: 10.3389/fphar.2020.572372] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2020] [Accepted: 09/08/2020] [Indexed: 12/23/2022] Open
Abstract
Gastric cancer (GC) is one of the leading causes of cancer-related death worldwide. It takes some time from chronic gastritis to develop in GC. Early detection of GC will help patients obtain timely treatment. Understanding disease evolution is crucial for the prevention and treatment of GC. Here, we present a convolutional neural network (CNN)-based system to detect abnormalities in the gastric mucosa. We identified normal mucosa, chronic gastritis, and intestinal-type GC: this is the most common route of gastric carcinogenesis. We integrated digitalizing histopathology of whole-slide images (WSIs), stain normalization, a deep CNN, and a random forest classifier. The staining variability of WSIs was reduced significantly through stain normalization, and saved the cost and time of preparing new slides. Stain normalization improved the effect of the CNN model. The accuracy rate at the patch-level reached 98.4%, and 94.5% for discriminating normal → chronic gastritis → GC. The accuracy rate at the WSIs-level for discriminating normal tissue and cancerous tissue reached 96.0%, which is a state-of-the-art result. Survival analyses indicated that the features extracted from the CNN exerted a significant impact on predicting the survival of cancer patients. Our CNN model disclosed significant potential for adjuvant diagnosis of gastric diseases, especially GC, and usefulness for predicting the prognosis.
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Affiliation(s)
- Bowei Ma
- Center for Intelligent Medical Imaging & Health, Research Institute of Tsinghua University in Shenzhen, Shenzhen, China.,Tsimage Medical Technology, Yantian Modern Industry Service Center, Shenzhen, China
| | - Yucheng Guo
- Center for Intelligent Medical Imaging & Health, Research Institute of Tsinghua University in Shenzhen, Shenzhen, China.,Tsimage Medical Technology, Yantian Modern Industry Service Center, Shenzhen, China
| | - Weian Hu
- Tsimage Medical Technology, Yantian Modern Industry Service Center, Shenzhen, China
| | - Fei Yuan
- Department of Pathology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Zhenggang Zhu
- Department of General Surgery, Ruijin Hospital, Shanghai Institute of Digestive Surgery, Shanghai Key Lab for Gastric Neoplasms, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yingyan Yu
- Department of General Surgery, Ruijin Hospital, Shanghai Institute of Digestive Surgery, Shanghai Key Lab for Gastric Neoplasms, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Hao Zou
- Center for Intelligent Medical Imaging & Health, Research Institute of Tsinghua University in Shenzhen, Shenzhen, China.,Tsimage Medical Technology, Yantian Modern Industry Service Center, Shenzhen, China
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10
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Abstract
Deep learning has pushed the scope of digital pathology beyond simple digitization and telemedicine. The incorporation of these algorithms in routine workflow is on the horizon and maybe a disruptive technology, reducing processing time, and increasing detection of anomalies. While the newest computational methods enjoy much of the press, incorporating deep learning into standard laboratory workflow requires many more steps than simply training and testing a model. Image analysis using deep learning methods often requires substantial pre- and post-processing order to improve interpretation and prediction. Similar to any data processing pipeline, images must be prepared for modeling and the resultant predictions need further processing for interpretation. Examples include artifact detection, color normalization, image subsampling or tiling, removal of errant predictions, etc. Once processed, predictions are complicated by image file size - typically several gigabytes when unpacked. This forces images to be tiled, meaning that a series of subsamples from the whole-slide image (WSI) are used in modeling. Herein, we review many of these methods as they pertain to the analysis of biopsy slides and discuss the multitude of unique issues that are part of the analysis of very large images.
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11
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Tosta TAA, de Faria PR, Servato JPS, Neves LA, Roberto GF, Martins AS, do Nascimento MZ. Unsupervised method for normalization of hematoxylin-eosin stain in histological images. Comput Med Imaging Graph 2019; 77:101646. [DOI: 10.1016/j.compmedimag.2019.101646] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2018] [Revised: 06/27/2019] [Accepted: 08/01/2019] [Indexed: 11/24/2022]
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12
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Tosta TAA, de Faria PR, Neves LA, do Nascimento MZ. Color normalization of faded H&E-stained histological images using spectral matching. Comput Biol Med 2019; 111:103344. [PMID: 31279982 DOI: 10.1016/j.compbiomed.2019.103344] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2019] [Revised: 06/24/2019] [Accepted: 06/24/2019] [Indexed: 11/16/2022]
Abstract
Histological samples stained with hematoxylin-eosin (H&E) are commonly used by pathologists in cancer diagnoses. However, the preparation, digitization, and storage of tissue samples can lead to color variations that produce poor performance when using histological image processing techniques. Thus, normalization methods have been proposed to adjust the color of the image. This can be achieved through the use of a spectral matching technique, where it is first necessary to estimate the H&E representation and the stain concentration in the image pixels by means of the RGB model. This study presents an estimation method for H&E stain representation for the normalization of faded histological samples. This application has been explored only to a limited extent in the literature, but has the capacity to expand the use of faded samples. To achieve this, the normalized images must have a coherent color representation of the H&E stain with no introduction of noise, which was realized by applying the methodology described in this proposal. The estimation method presented here aims to normalize histological samples with different degrees of fading using a combination of fuzzy theory and the Cuckoo search algorithm, and dictionary learning with an initialization method for optimization. In visual and quantitative comparisons of estimates of H&E stain representation from the literature, our proposed method achieved very good results, with a high feature similarity between the original and normalized images.
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Affiliation(s)
- Thaína A Azevedo Tosta
- Center of Mathematics, Computing and Cognition, Federal University of ABC, Av. dos Estados, 5001, 09210-580, Santo André, São Paulo, Brazil.
| | - Paulo Rogério de Faria
- Department of Histology and Morphology, Institute of Biomedical Science, Federal University of Uberlândia, Av. Amazonas, S/N, 38405-320, Uberlândia, Minas Gerais, Brazil.
| | - Leandro Alves Neves
- Department of Computer Science and Statistics, São Paulo State University, R. Cristóvão Colombo, 2265, 15054-000, São José do Rio Preto, São Paulo, Brazil.
| | - Marcelo Zanchetta do Nascimento
- Center of Mathematics, Computing and Cognition, Federal University of ABC, Av. dos Estados, 5001, 09210-580, Santo André, São Paulo, Brazil; Faculty of Computer Science, Federal University of Uberlândia, Av. João Naves de Ávila, 2121, 38400-902, Uberlândia, Minas Gerais, Brazil.
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Azevedo Tosta TA, de Faria PR, Neves LA, do Nascimento MZ. Computational normalization of H&E-stained histological images: Progress, challenges and future potential. Artif Intell Med 2019; 95:118-132. [DOI: 10.1016/j.artmed.2018.10.004] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2018] [Revised: 09/13/2018] [Accepted: 10/20/2018] [Indexed: 01/13/2023]
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14
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Zheng Y, Jiang Z, Zhang H, Xie F, Shi J, Xue C. Adaptive color deconvolution for histological WSI normalization. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2019; 170:107-120. [PMID: 30712599 DOI: 10.1016/j.cmpb.2019.01.008] [Citation(s) in RCA: 38] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/21/2018] [Revised: 12/31/2018] [Accepted: 01/15/2019] [Indexed: 06/09/2023]
Abstract
BACKGROUND AND OBJECTIVE Color consistency of histological images is significant for developing reliable computer-aided diagnosis (CAD) systems. However, the color appearance of digital histological images varies across different specimen preparations, staining, and scanning situations. This variability affects the diagnosis and decreases the accuracy of CAD approaches. It is important and challenging to develop effective color normalization methods for digital histological images. METHODS We proposed a novel adaptive color deconvolution (ACD) algorithm for stain separation and color normalization of hematoxylin-eosin-stained whole slide images (WSIs). To avoid artifacts and reduce the failure rate of normalization, multiple prior knowledges of staining are considered and embedded in the ACD model. To improve the capacity of color normalization for various WSIs, an integrated optimization is designed to simultaneously estimate the parameters of the stain separation and color normalization. The solving of ACD model and application of the proposed method involves only pixel-wise operation, which makes it very efficient and applicable to WSIs. RESULTS The proposed method was evaluated on four WSI-datasets including breast, lung and cervix cancers and was compared with 6 state-of-the-art methods. The proposed method achieved the most consistent performance in color normalization according to the quantitative metrics. Through a qualitative assessment for 500 WSIs, the failure rate of normalization was 0.4% and the structure and color artifacts were effectively avoided. Applied to CAD methods, the area under receiver operating characteristic curve for cancer image classification was improved from 0.842 to 0.914. The average time of solving the ACD model is 2.97 s. CONCLUSIONS The proposed ACD model has prone effective for color normalization of hematoxylin-eosin-stained WSIs in various color appearances. The model is robust and can be applied to WSIs containing different lesions. The proposed model can be efficiently solved and is effective to improve the performance of cancer image recognition, which is adequate for developing automatic CAD programs and systems based on WSIs.
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Affiliation(s)
- Yushan Zheng
- Image Processing Center, School of Astronautics, Beihang University, Beijing, 100191, China; Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, 100191, China; Beijing Key Laboratory of Digital Media, Beihang University, Beijing, 100191, China.
| | - Zhiguo Jiang
- Image Processing Center, School of Astronautics, Beihang University, Beijing, 100191, China; Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, 100191, China; Beijing Key Laboratory of Digital Media, Beihang University, Beijing, 100191, China.
| | - Haopeng Zhang
- Image Processing Center, School of Astronautics, Beihang University, Beijing, 100191, China; Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, 100191, China; Beijing Key Laboratory of Digital Media, Beihang University, Beijing, 100191, China.
| | - Fengying Xie
- Image Processing Center, School of Astronautics, Beihang University, Beijing, 100191, China; Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, 100191, China; Beijing Key Laboratory of Digital Media, Beihang University, Beijing, 100191, China.
| | - Jun Shi
- School of Software, Hefei University of Technology, Hefei 230601, China
| | - Chenghai Xue
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, China
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Pichat J, Iglesias JE, Yousry T, Ourselin S, Modat M. A Survey of Methods for 3D Histology Reconstruction. Med Image Anal 2018; 46:73-105. [DOI: 10.1016/j.media.2018.02.004] [Citation(s) in RCA: 55] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2017] [Revised: 02/02/2018] [Accepted: 02/14/2018] [Indexed: 02/08/2023]
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Nguyen L, Tosun AB, Fine JL, Lee AV, Taylor DL, Chennubhotla SC. Spatial Statistics for Segmenting Histological Structures in H&E Stained Tissue Images. IEEE TRANSACTIONS ON MEDICAL IMAGING 2017; 36:1522-1532. [PMID: 28328502 PMCID: PMC5498226 DOI: 10.1109/tmi.2017.2681519] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Segmenting a broad class of histological structures in transmitted light and/or fluorescence-based images is a prerequisite for determining the pathological basis of cancer, elucidating spatial interactions between histological structures in tumor microenvironments (e.g., tumor infiltrating lymphocytes), facilitating precision medicine studies with deep molecular profiling, and providing an exploratory tool for pathologists. This paper focuses on segmenting histological structures in hematoxylin- and eosin-stained images of breast tissues, e.g., invasive carcinoma, carcinoma in situ, atypical and normal ducts, adipose tissue, and lymphocytes. We propose two graph-theoretic segmentation methods based on local spatial color and nuclei neighborhood statistics. For benchmarking, we curated a data set of 232 high-power field breast tissue images together with expertly annotated ground truth. To accurately model the preference for histological structures (ducts, vessels, tumor nets, adipose, etc.) over the remaining connective tissue and non-tissue areas in ground truth annotations, we propose a new region-based score for evaluating segmentation algorithms. We demonstrate the improvement of our proposed methods over the state-of-the-art algorithms in both region- and boundary-based performance measures.
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Abstract
Immunohistochemical (IHC) biomarkers in breast tissue microarray (TMA) samples are used daily in pathology departments. In recent years, automatic methods to evaluate positive staining have been investigated since they may save time and reduce errors in the diagnosis. These errors are mostly due to subjective evaluation. The aim of this work is to develop a density tool able to automatically quantify the positive brown IHC stain in breast TMA for different biomarkers. To avoid the problem of colour variation and make a robust tool independent of the staining process, several colour standardization methods have been analysed. Four colour standardization methods have been compared against colour model segmentation. The standardization methods have been compared by means of NBS colour distance. The use of colour standardization helps to reduce noise due to stain and histological sample preparation. However, the most reliable and robust results have been obtained by combining the HSV and RGB colour models for segmentation with the HSB channels. The segmentation provides three outputs based on three saturation values for weak, medium and strong staining. Each output image can be combined according to the type of biomarker staining. The results with 12 biomarkers were evaluated and compared to the segmentation and density calculation done by expert pathologists. The Hausdorff distance, sensitivity and specificity have been used to quantitative validate the results. The tests carried out with 8000 TMA images provided an average of 95.94% accuracy applied to the total tissue cylinder area. Colour standardization was used only when the tissue core had blurring and fading stain and the expert could not evaluate them without a pre-processing.
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18
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Van Eycke YR, Allard J, Salmon I, Debeir O, Decaestecker C. Image processing in digital pathology: an opportunity to solve inter-batch variability of immunohistochemical staining. Sci Rep 2017; 7:42964. [PMID: 28220842 PMCID: PMC5318955 DOI: 10.1038/srep42964] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2016] [Accepted: 01/17/2017] [Indexed: 12/18/2022] Open
Abstract
Immunohistochemistry (IHC) is a widely used technique in pathology to evidence protein expression in tissue samples. However, this staining technique is known for presenting inter-batch variations. Whole slide imaging in digital pathology offers a possibility to overcome this problem by means of image normalisation techniques. In the present paper we propose a methodology to objectively evaluate the need of image normalisation and to identify the best way to perform it. This methodology uses tissue microarray (TMA) materials and statistical analyses to evidence the possible variations occurring at colour and intensity levels as well as to evaluate the efficiency of image normalisation methods in correcting them. We applied our methodology to test different methods of image normalisation based on blind colour deconvolution that we adapted for IHC staining. These tests were carried out for different IHC experiments on different tissue types and targeting different proteins with different subcellular localisations. Our methodology enabled us to establish and to validate inter-batch normalization transforms which correct the non-relevant IHC staining variations. The normalised image series were then processed to extract coherent quantitative features characterising the IHC staining patterns.
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Affiliation(s)
- Yves-Rémi Van Eycke
- DIAPath, Center for Microscopy and Molecular Imaging, Université Libre de Bruxelles (ULB), Gosselies, Belgium.,Laboratories of Image, Signal processing &Acoustics, Université Libre de Bruxelles (ULB), Brussels, Belgium
| | - Justine Allard
- DIAPath, Center for Microscopy and Molecular Imaging, Université Libre de Bruxelles (ULB), Gosselies, Belgium
| | - Isabelle Salmon
- DIAPath, Center for Microscopy and Molecular Imaging, Université Libre de Bruxelles (ULB), Gosselies, Belgium.,Department of Pathology, Erasme Hospital, Université Libre de Bruxelles (ULB), Brussels, Belgium
| | - Olivier Debeir
- Laboratories of Image, Signal processing &Acoustics, Université Libre de Bruxelles (ULB), Brussels, Belgium.,MIP, Center for Microscopy and Molecular Imaging, Université Libre de Bruxelles (ULB), Gosselies, Belgium
| | - Christine Decaestecker
- DIAPath, Center for Microscopy and Molecular Imaging, Université Libre de Bruxelles (ULB), Gosselies, Belgium.,Laboratories of Image, Signal processing &Acoustics, Université Libre de Bruxelles (ULB), Brussels, Belgium
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Stain Deconvolution Using Statistical Analysis of Multi-Resolution Stain Colour Representation. PLoS One 2017; 12:e0169875. [PMID: 28076381 PMCID: PMC5226799 DOI: 10.1371/journal.pone.0169875] [Citation(s) in RCA: 49] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2016] [Accepted: 12/23/2016] [Indexed: 01/16/2023] Open
Abstract
Stain colour estimation is a prominent factor of the analysis pipeline in most of histology image processing algorithms. Providing a reliable and efficient stain colour deconvolution approach is fundamental for robust algorithm. In this paper, we propose a novel method for stain colour deconvolution of histology images. This approach statistically analyses the multi-resolutional representation of the image to separate the independent observations out of the correlated ones. We then estimate the stain mixing matrix using filtered uncorrelated data. We conducted an extensive set of experiments to compare the proposed method to the recent state of the art methods and demonstrate the robustness of this approach using three different datasets of scanned slides, prepared in different labs using different scanners.
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