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Chu ML, Ge XYM, Eastham J, Nguyen T, Fuji RN, Sullivan R, Ruderman D. Assessment of Color Reproducibility and Mitigation of Color Variation in Whole Slide Image Scanners for Toxicologic Pathology. Toxicol Pathol 2023; 51:313-328. [PMID: 38288712 DOI: 10.1177/01926233231224468] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/17/2024]
Abstract
Digital pathology workflows in toxicologic pathology rely on whole slide images (WSIs) from histopathology slides. Inconsistent color reproduction by WSI scanners of different models and from different manufacturers can result in different color representations and inter-scanner color variation in the WSIs. Although pathologists can accommodate a range of color variation during their evaluation of WSIs, color variability can degrade the performance of computational applications in digital pathology. In particular, color variability can compromise the generalization of artificial intelligence applications to large volumes of data from diverse sources. To address these challenges, we developed a process that includes two modules: (1) assessing the color reproducibility of our scanners and the color variation among them and (2) applying color correction to WSIs to minimize the color deviation and variation. Our process ensures consistent color reproduction across WSI scanners and enhances color homogeneity in WSIs, and its flexibility enables easy integration as a post-processing step following scanning by WSI scanners of different models and from different manufacturers.
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Affiliation(s)
- Mei-Lan Chu
- Genentech Inc., South San Francisco, California, USA
| | - Xing-Yue M Ge
- Genentech Inc., South San Francisco, California, USA
| | | | - Trung Nguyen
- Genentech Inc., South San Francisco, California, USA
| | - Reina N Fuji
- Genentech Inc., South San Francisco, California, USA
| | - Ruth Sullivan
- Genentech Inc., South San Francisco, California, USA
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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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Michielli N, Caputo A, Scotto M, Mogetta A, Pennisi OAM, Molinari F, Balmativola D, Bosco M, Gambella A, Metovic J, Tota D, Carpenito L, Gasparri P, Salvi M. Stain normalization in digital pathology: Clinical multi-center evaluation of image quality. J Pathol Inform 2022; 13:100145. [PMID: 36268060 PMCID: PMC9577129 DOI: 10.1016/j.jpi.2022.100145] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Revised: 09/14/2022] [Accepted: 09/22/2022] [Indexed: 11/20/2022] Open
Abstract
In digital pathology, the final appearance of digitized images is affected by several factors, resulting in stain color and intensity variation. Stain normalization is an innovative solution to overcome stain variability. However, the validation of color normalization tools has been assessed only from a quantitative perspective, through the computation of similarity metrics between the original and normalized images. To the best of our knowledge, no works investigate the impact of normalization on the pathologist's evaluation. The objective of this paper is to propose a multi-tissue (i.e., breast, colon, liver, lung, and prostate) and multi-center qualitative analysis of a stain normalization tool with the involvement of pathologists with different years of experience. Two qualitative studies were carried out for this purpose: (i) a first study focused on the analysis of the perceived image quality and absence of significant image artifacts after the normalization process; (ii) a second study focused on the clinical score of the normalized image with respect to the original one. The results of the first study prove the high quality of the normalized image with a low impact artifact generation, while the second study demonstrates the superiority of the normalized image with respect to the original one in clinical practice. The normalization process can help both to reduce variability due to tissue staining procedures and facilitate the pathologist in the histological examination. The experimental results obtained in this work are encouraging and can justify the use of a stain normalization tool in clinical routine.
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Affiliation(s)
- Nicola Michielli
- Biolab, PolitoMed Lab, Department of Electronics and Telecommunications, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Turin, Italy
| | - Alessandro Caputo
- Department of Medicine and Surgery, University Hospital of Salerno, Salerno, Italy
| | - Manuela Scotto
- Biolab, PolitoMed Lab, Department of Electronics and Telecommunications, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Turin, Italy
| | - Alessandro Mogetta
- Biolab, PolitoMed Lab, Department of Electronics and Telecommunications, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Turin, Italy
| | - Orazio Antonino Maria Pennisi
- Technology Transfer and Industrial Liaison Department, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Turin, Italy
| | - Filippo Molinari
- Biolab, PolitoMed Lab, Department of Electronics and Telecommunications, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Turin, Italy
| | - Davide Balmativola
- Pathology Unit, Humanitas Gradenigo Hospital, Corso Regina Margherita 8, 10153 Turin, Italy
| | - Martino Bosco
- Department of Pathology, Michele and Pietro Ferrero Hospital, 12060 Verduno, Italy
| | - Alessandro Gambella
- Pathology Unit, Department of Medical Sciences, University of Turin, Via Santena 7, 10126 Turin, Italy
| | - Jasna Metovic
- Pathology Unit, Department of Medical Sciences, University of Turin, Via Santena 7, 10126 Turin, Italy
| | - Daniele Tota
- Pathology Unit, Department of Medical Sciences, University of Turin, Via Santena 7, 10126 Turin, Italy
| | - Laura Carpenito
- Department of Pathology, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
- University of Milan, Milan, Italy
| | - Paolo Gasparri
- UOC di Anatomia Patologica, ASP Catania P.O. “Gravina”, Caltagirone, Italy
| | - Massimo Salvi
- Biolab, PolitoMed Lab, Department of Electronics and Telecommunications, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Turin, Italy
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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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He W, Liu T, Han Y, Ming W, Du J, Liu Y, Yang Y, Wang L, Jiang Z, Wang Y, Yuan J, Cao C. A review: The detection of cancer cells in histopathology based on machine vision. Comput Biol Med 2022; 146:105636. [PMID: 35751182 DOI: 10.1016/j.compbiomed.2022.105636] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2021] [Revised: 04/04/2022] [Accepted: 04/28/2022] [Indexed: 12/24/2022]
Abstract
Machine vision is being employed in defect detection, size measurement, pattern recognition, image fusion, target tracking and 3D reconstruction. Traditional cancer detection methods are dominated by manual detection, which wastes time and manpower, and heavily relies on the pathologists' skill and work experience. Therefore, these manual detection approaches are not convenient for the inheritance of domain knowledge, and are not suitable for the rapid development of medical care in the future. The emergence of machine vision can iteratively update and learn the domain knowledge of cancer cell pathology detection to achieve automated, high-precision, and consistent detection. Consequently, this paper reviews the use of machine vision to detect cancer cells in histopathology images, as well as the benefits and drawbacks of various detection approaches. First, we review the application of image preprocessing and image segmentation in histopathology for the detection of cancer cells, and compare the benefits and drawbacks of different algorithms. Secondly, for the characteristics of histopathological cancer cell images, the research progress of shape, color and texture features and other methods is mainly reviewed. Furthermore, for the classification methods of histopathological cancer cell images, the benefits and drawbacks of traditional machine vision approaches and deep learning methods are compared and analyzed. Finally, the above research is discussed and forecasted, with the expected future development tendency serving as a guide for future research.
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Affiliation(s)
- Wenbin He
- Henan Key Lab of Intelligent Manufacturing of Mechanical Equipment, Zhengzhou University of Light Industry, Zhengzhou, 450002, China
| | - Ting Liu
- Henan Key Lab of Intelligent Manufacturing of Mechanical Equipment, Zhengzhou University of Light Industry, Zhengzhou, 450002, China
| | - Yongjie Han
- Henan Key Lab of Intelligent Manufacturing of Mechanical Equipment, Zhengzhou University of Light Industry, Zhengzhou, 450002, China
| | - Wuyi Ming
- Henan Key Lab of Intelligent Manufacturing of Mechanical Equipment, Zhengzhou University of Light Industry, Zhengzhou, 450002, China; Guangdong HUST Industrial Technology Research Institute, Guangdong Provincial Key Laboratory of Digital Manufacturing Equipment, Dongguan, 523808, China.
| | - Jinguang Du
- Henan Key Lab of Intelligent Manufacturing of Mechanical Equipment, Zhengzhou University of Light Industry, Zhengzhou, 450002, China
| | - Yinxia Liu
- Laboratory Medicine of Dongguan Kanghua Hospital, Dongguan, 523808, China
| | - Yuan Yang
- Guangdong Provincial Hospital of Chinese Medicine, Guangzhou, 510120, China.
| | - Leijie Wang
- School of Mechanical Engineering, Dongguan University of Technology Dongguan, 523808, China
| | - Zhiwen Jiang
- Henan Key Lab of Intelligent Manufacturing of Mechanical Equipment, Zhengzhou University of Light Industry, Zhengzhou, 450002, China
| | - Yongqiang Wang
- Zhengzhou Coal Mining Machinery Group Co., Ltd, Zhengzhou, 450016, China
| | - Jie Yuan
- Henan Key Lab of Intelligent Manufacturing of Mechanical Equipment, Zhengzhou University of Light Industry, Zhengzhou, 450002, China
| | - Chen Cao
- Henan Key Lab of Intelligent Manufacturing of Mechanical Equipment, Zhengzhou University of Light Industry, Zhengzhou, 450002, China; Guangdong HUST Industrial Technology Research Institute, Guangdong Provincial Key Laboratory of Digital Manufacturing Equipment, Dongguan, 523808, China
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Liu X, Yuan P, Li R, Zhang D, An J, Ju J, Liu C, Ren F, Hou R, Li Y, Yang J. Predicting breast cancer recurrence and metastasis risk by integrating color and texture features of histopathological images and machine learning technologies. Comput Biol Med 2022; 146:105569. [DOI: 10.1016/j.compbiomed.2022.105569] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Revised: 04/24/2022] [Accepted: 04/25/2022] [Indexed: 12/11/2022]
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Ye Z, Zhang Y, Liang Y, Lang J, Zhang X, Zang G, Yuan D, Tian G, Xiao M, Yang J. Cervical Cancer Metastasis and Recurrence Risk Prediction Based on Deep Convolutional Neural Network. Curr Bioinform 2022. [DOI: 10.2174/1574893616666210708143556] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Background:
Evaluating the risk of metastasis and recurrence of a cervical cancer patient is
critical for appropriate adjuvant therapy. However, current risk assessment models usually involve the
testing of tens to thousands of genes from patients’ tissue samples, which is expensive and timeconsuming.
Therefore, computer-aided diagnosis and prognosis prediction based on Hematoxylin and Eosin
(H&E) pathological images have received much attention recently.
Objective:
The prognosis of whether patients will have metastasis and recurrence can support accurate
treatment for patients in advance and help reduce patient loss. It is also important for guiding treatment
after surgery to be able to quickly and accurately predict the risk of metastasis and recurrence of a cervical
cancer patient.
Method:
To address this problem, we propose a hybrid method. Transfer learning is used to extract features,
and it is combined with traditional machine learning in order to analyze and determine whether
patients have the risks of metastasis and recurrence. First, the proposed model retrieved relevant patches
using a color-based method from H&E pathological images, which were then subjected to image preprocessing
steps such as image normalization and color homogenization. Based on the labeled patched
images, the Xception model with good classification performance was selected, and deep features of
patched pathological images were automatically extracted with transfer learning. After that, the extracted
features were combined to train a random forest model to predict the label of a new patched image.
Finally, a majority voting method was developed to predict the metastasis and recurrence risk of a patient
based on the predictions of patched images from the whole-slide H&E image.
Results:
In our experiment, the proposed model yielded an area under the receiver operating characteristic
curve of 0.82 for the whole-slide image. The experimental results showed that the high-level features
extracted by the deep convolutional neural network from the whole-slide image can be used to predict
the risk of recurrence and metastasis after surgical resection and help identify patients who might receive
additional benefit from adjuvant therapy.
Conclusion:
This paper explored the feasibility of predicting the risk of metastasis and recurrence from
cervical cancer whole slide H&E images through deep learning and random forest methods.
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Affiliation(s)
- Zixuan Ye
- School of Computer, Hunan University of Technology, Zhuzhou Hunan 412007, China
| | | | - Yuebin Liang
- Geneis (Beijing) Co. Ltd., Beijing 100102, China
| | - Jidong Lang
- Geneis (Beijing) Co. Ltd., Beijing 100102, China
| | - Xiaoli Zhang
- School of Computer, Hunan University of Technology, Zhuzhou Hunan 412007, China
| | | | - Dawei Yuan
- Geneis (Beijing) Co. Ltd., Beijing 100102, China
| | - Geng Tian
- School of Computer, Hunan University of Technology, Zhuzhou Hunan 412007, China
- Geneis (Beijing) Co. Ltd., Beijing 100102, China
| | - Mansheng Xiao
- School of Computer, Hunan University of Technology, Zhuzhou Hunan 412007, China
| | - Jialiang Yang
- School of Computer, Hunan University of Technology, Zhuzhou Hunan 412007, China
- Geneis (Beijing) Co. Ltd., Beijing 100102, China
- Academician Workstation, Changsha
Medical University, Changsha Hunan 410219, China
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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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