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Bándi P, Balkenhol M, van Dijk M, Kok M, van Ginneken B, van der Laak J, Litjens G. Continual learning strategies for cancer-independent detection of lymph node metastases. Med Image Anal 2023; 85:102755. [PMID: 36724605 DOI: 10.1016/j.media.2023.102755] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Revised: 01/08/2023] [Accepted: 01/18/2023] [Indexed: 01/26/2023]
Abstract
Recently, large, high-quality public datasets have led to the development of convolutional neural networks that can detect lymph node metastases of breast cancer at the level of expert pathologists. Many cancers, regardless of the site of origin, can metastasize to lymph nodes. However, collecting and annotating high-volume, high-quality datasets for every cancer type is challenging. In this paper we investigate how to leverage existing high-quality datasets most efficiently in multi-task settings for closely related tasks. Specifically, we will explore different training and domain adaptation strategies, including prevention of catastrophic forgetting, for breast, colon and head-and-neck cancer metastasis detection in lymph nodes. Our results show state-of-the-art performance on colon and head-and-neck cancer metastasis detection tasks. We show the effectiveness of adaptation of networks from one cancer type to another to obtain multi-task metastasis detection networks. Furthermore, we show that leveraging existing high-quality datasets can significantly boost performance on new target tasks and that catastrophic forgetting can be effectively mitigated.Last, we compare different mitigation strategies.
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2
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Eymael J, van den Broek M, Miesen L, Monge VV, van den Berge BT, Mooren F, Velez VL, Dijkstra J, Hermsen M, Bándi P, Vermeulen M, de Wildt S, Willemsen B, Florquin S, Wetzels R, Steenbergen E, Kramann R, Moeller M, Schreuder MF, Wetzels JF, van der Vlag J, Jansen J, Smeets B. Human scattered tubular cells represent a heterogeneous population of glycolytic dedifferentiated proximal tubule cells. J Pathol 2023; 259:149-162. [PMID: 36373978 PMCID: PMC10107692 DOI: 10.1002/path.6029] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Revised: 09/22/2022] [Accepted: 10/31/2022] [Indexed: 11/16/2022]
Abstract
Scattered tubular cells (STCs) are a phenotypically distinct cell population in the proximal tubule that increase in number after acute kidney injury. We aimed to characterize the human STC population. Three-dimensional human tissue analysis revealed that STCs are preferentially located within inner bends of the tubule and are barely present in young kidney tissue (<2 years), and their number increases with age. Increased STC numbers were associated with acute tubular injury (kidney injury molecule 1) and interstitial fibrosis (alpha smooth muscle actin). Isolated CD13+ CD24- CD133- proximal tubule epithelial cells (PTECs) and CD13+ CD24+ and CD13+ CD133+ STCs were analyzed using RNA sequencing. Transcriptome analysis revealed an upregulation of nuclear factor κB, tumor necrosis factor alpha, and inflammatory pathways in STCs, whereas metabolism, especially the tricarboxylic acid cycle and oxidative phosphorylation, was downregulated, without showing signs of cellular senescence. Using immunostaining and a publicly available single-cell sequencing database of human kidneys, we demonstrate that STCs represent a heterogeneous population in a transient state. In conclusion, STCs are dedifferentiated PTECs showing a metabolic shift toward glycolysis, which could facilitate cellular survival after kidney injury. © 2022 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.
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Affiliation(s)
- Jennifer Eymael
- Department of Pathology, Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Martijn van den Broek
- Department of Pathology, Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, Nijmegen, The Netherlands.,Department of Pediatric Nephrology, Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, Amalia Children's Hospital, Nijmegen, The Netherlands
| | - Laura Miesen
- Department of Pathology, Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Valerie Villacorta Monge
- Department of Pathology, Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Bartholomeus T van den Berge
- Department of Pathology, Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, Nijmegen, The Netherlands.,Department of Nephrology, Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Fieke Mooren
- Department of Pathology, Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Vicky Luna Velez
- Department of Molecular Biology, Radboud Institute for Molecular Life Science, Nijmegen, The Netherlands
| | - Jelmer Dijkstra
- Department of Molecular Biology, Radboud Institute for Molecular Life Science, Nijmegen, The Netherlands
| | - Meyke Hermsen
- Department of Pathology, Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Péter Bándi
- Department of Pathology, Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Michiel Vermeulen
- Department of Molecular Biology, Radboud Institute for Molecular Life Science, Nijmegen, The Netherlands
| | - Saskia de Wildt
- Department of Pharmacology and Toxicology, Radboud Institute for Molecular Life Science, Nijmegen, The Netherlands
| | - Brigith Willemsen
- Department of Pathology, Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Sandrine Florquin
- Department of Pathology, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands.,Amsterdam Institute for Infection and Immunology, Amsterdam, The Netherlands
| | - Roy Wetzels
- Department of Pathology, Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Eric Steenbergen
- Department of Pathology, Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Rafael Kramann
- Division of Nephrology and Clinical Immunology, RWTH Aachen University, Aachen, Germany.,Institute of Experimental Medicine and Systems Biology, RWTH Aachen University, Aachen, Germany.,Department of Internal Medicine, Nephrology and Transplantation, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Marcus Moeller
- Division of Nephrology and Clinical Immunology, RWTH Aachen University, Aachen, Germany
| | - Michiel F Schreuder
- Department of Pediatric Nephrology, Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, Amalia Children's Hospital, Nijmegen, The Netherlands
| | - Jack Fm Wetzels
- Department of Nephrology, Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, Nijmegen, The Netherlands.,Department of Nephrology, Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Johan van der Vlag
- Department of Nephrology, Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Jitske Jansen
- Department of Pathology, Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, Nijmegen, The Netherlands.,Department of Pediatric Nephrology, Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, Amalia Children's Hospital, Nijmegen, The Netherlands.,Institute of Experimental Medicine and Systems Biology, RWTH Aachen University, Aachen, Germany
| | - Bart Smeets
- Department of Pathology, Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, Nijmegen, The Netherlands
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3
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Jarkman S, Karlberg M, Pocevičiūtė M, Bodén A, Bándi P, Litjens G, Lundström C, Treanor D, van der Laak J. Generalization of Deep Learning in Digital Pathology: Experience in Breast Cancer Metastasis Detection. Cancers (Basel) 2022; 14:cancers14215424. [PMID: 36358842 PMCID: PMC9659028 DOI: 10.3390/cancers14215424] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Revised: 10/13/2022] [Accepted: 10/28/2022] [Indexed: 11/06/2022] Open
Abstract
Simple Summary Pathology is a cornerstone in cancer diagnostics, and digital pathology and artificial intelligence-driven image analysis could potentially save time and enhance diagnostic accuracy. For clinical implementation of artificial intelligence, a major question is whether the computer models maintain high performance when applied to new settings. We tested the generalizability of a highly accurate deep learning model for breast cancer metastasis detection in sentinel lymph nodes from, firstly, unseen sentinel node data and, secondly, data with a small change in surgical indication, in this case lymph nodes from axillary dissections. Model performance dropped in both settings, particularly on axillary dissection nodes. Retraining of the model was needed to mitigate the performance drop. The study highlights the generalization challenge of clinical implementation of AI models, and the possibility that retraining might be necessary. Abstract Poor generalizability is a major barrier to clinical implementation of artificial intelligence in digital pathology. The aim of this study was to test the generalizability of a pretrained deep learning model to a new diagnostic setting and to a small change in surgical indication. A deep learning model for breast cancer metastases detection in sentinel lymph nodes, trained on CAMELYON multicenter data, was used as a base model, and achieved an AUC of 0.969 (95% CI 0.926–0.998) and FROC of 0.838 (95% CI 0.757–0.913) on CAMELYON16 test data. On local sentinel node data, the base model performance dropped to AUC 0.929 (95% CI 0.800–0.998) and FROC 0.744 (95% CI 0.566–0.912). On data with a change in surgical indication (axillary dissections) the base model performance indicated an even larger drop with a FROC of 0.503 (95%CI 0.201–0.911). The model was retrained with addition of local data, resulting in about a 4% increase for both AUC and FROC for sentinel nodes, and an increase of 11% in AUC and 49% in FROC for axillary nodes. Pathologist qualitative evaluation of the retrained model´s output showed no missed positive slides. False positives, false negatives and one previously undetected micro-metastasis were observed. The study highlights the generalization challenge even when using a multicenter trained model, and that a small change in indication can considerably impact the model´s performance.
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Affiliation(s)
- Sofia Jarkman
- Department of Clinical Pathology, and Department of Biomedical and Clinical Sciences, Linköping University, 581 83 Linköping, Sweden
- Center for Medical Image Science and Visualization (CMIV), Linköping University, 581 85 Linköping, Sweden
- Correspondence:
| | - Micael Karlberg
- Center for Medical Image Science and Visualization (CMIV), Linköping University, 581 85 Linköping, Sweden
- Department of Pathology, Radboud University Medical Center, P.O. Box 9101, 6500 HB Nijmegen, The Netherlands
| | - Milda Pocevičiūtė
- Center for Medical Image Science and Visualization (CMIV), Linköping University, 581 85 Linköping, Sweden
| | - Anna Bodén
- Department of Clinical Pathology, and Department of Biomedical and Clinical Sciences, Linköping University, 581 83 Linköping, Sweden
- Center for Medical Image Science and Visualization (CMIV), Linköping University, 581 85 Linköping, Sweden
| | - Péter Bándi
- Department of Pathology, Radboud University Medical Center, P.O. Box 9101, 6500 HB Nijmegen, The Netherlands
| | - Geert Litjens
- Department of Pathology, Radboud University Medical Center, P.O. Box 9101, 6500 HB Nijmegen, The Netherlands
| | - Claes Lundström
- Center for Medical Image Science and Visualization (CMIV), Linköping University, 581 85 Linköping, Sweden
- Sectra AB, Teknikringen 20, 583 30 Linköping, Sweden
| | - Darren Treanor
- Department of Clinical Pathology, and Department of Biomedical and Clinical Sciences, Linköping University, 581 83 Linköping, Sweden
- Center for Medical Image Science and Visualization (CMIV), Linköping University, 581 85 Linköping, Sweden
- Leeds Teaching Hospitals NHS Trust, St James´s University Hospital, Beckett Street, Leeds LS9 7TF, UK
- Department of Pathology, University of Leeds, Woodhouse Lane, Leeds LS2 9JT, UK
| | - Jeroen van der Laak
- Center for Medical Image Science and Visualization (CMIV), Linköping University, 581 85 Linköping, Sweden
- Department of Pathology, Radboud University Medical Center, P.O. Box 9101, 6500 HB Nijmegen, The Netherlands
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4
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Hermsen M, Ciompi F, Adefidipe A, Denic A, Dendooven A, Smith BH, van Midden D, Bräsen JH, Kers J, Stegall MD, Bándi P, Nguyen T, Swiderska-Chadaj Z, Smeets B, Hilbrands LB, van der Laak JAWM. Convolutional Neural Networks for the Evaluation of Chronic and Inflammatory Lesions in Kidney Transplant Biopsies. Am J Pathol 2022; 192:1418-1432. [PMID: 35843265 DOI: 10.1016/j.ajpath.2022.06.009] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/21/2021] [Revised: 06/13/2022] [Accepted: 06/22/2022] [Indexed: 06/15/2023]
Abstract
In kidney transplant biopsies, both inflammation and chronic changes are important features that predict long-term graft survival. Quantitative scoring of these features is important for transplant diagnostics and kidney research. However, visual scoring is poorly reproducible and labor intensive. The goal of this study was to investigate the potential of convolutional neural networks (CNNs) to quantify inflammation and chronic features in kidney transplant biopsies. A structure segmentation CNN and a lymphocyte detection CNN were applied on 125 whole-slide image pairs of periodic acid-Schiff- and CD3-stained slides. The CNN results were used to quantify healthy and sclerotic glomeruli, interstitial fibrosis, tubular atrophy, and inflammation within both nonatrophic and atrophic tubuli, and in areas of interstitial fibrosis. The computed tissue features showed high correlation with Banff lesion scores of five pathologists (A.A., A.Dend., J.H.B., J.K., and T.N.). Analyses on a small subset showed a moderate correlation toward higher CD3+ cell density within scarred regions and higher CD3+ cell count inside atrophic tubuli correlated with long-term change of estimated glomerular filtration rate. The presented CNNs are valid tools to yield objective quantitative information on glomeruli number, fibrotic tissue, and inflammation within scarred and non-scarred kidney parenchyma in a reproducible manner. CNNs have the potential to improve kidney transplant diagnostics and will benefit the community as a novel method to generate surrogate end points for large-scale clinical studies.
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Affiliation(s)
- Meyke Hermsen
- Department of Pathology, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Francesco Ciompi
- Department of Pathology, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Adeyemi Adefidipe
- Department of Pathology, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, the Netherlands
| | - Aleksandar Denic
- Division of Nephrology and Hypertension, Mayo Clinic, Rochester, Minnesota
| | - Amélie Dendooven
- Department of Pathology, Ghent University Hospital, Ghent, Belgium; Faculty of Medicine, University of Antwerp, Wilrijk, Antwerp, Belgium
| | - Byron H Smith
- William J. von Liebig Center for Transplantation and Clinical Regeneration, Mayo Clinic, Rochester, Minnesota; Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota
| | - Dominique van Midden
- Department of Pathology, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Jan Hinrich Bräsen
- Nephropathology Unit, Institute of Pathology, Hannover Medical School, Hannover, Germany
| | - Jesper Kers
- Department of Pathology, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, the Netherlands; Department of Pathology, Leiden University Medical Center, Leiden, the Netherlands; Center for Analytical Sciences Amsterdam, Van 't Hoff Institute for Molecular Sciences, University of Amsterdam, Amsterdam, the Netherlands
| | - Mark D Stegall
- Division of Transplantation Surgery, Mayo Clinic, Rochester, Minnesota
| | - Péter Bándi
- Department of Pathology, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Tri Nguyen
- Department of Pathology, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Zaneta Swiderska-Chadaj
- Department of Pathology, Radboud University Medical Center, Nijmegen, the Netherlands; Faculty of Electrical Engineering, Warsaw University of Technology, Warsaw, Poland
| | - Bart Smeets
- Department of Pathology, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Luuk B Hilbrands
- Department of Nephrology, Radboud University Medical Center, Nijmegen, 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.
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5
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Miesen L, Bándi P, Willemsen B, Mooren F, Strieder T, Boldrini E, Drenic V, Eymael J, Wetzels R, Lotz J, Weiss N, Steenbergen E, van Kuppevelt TH, van Erp M, van der Laak J, Endlich N, Moeller MJ, Wetzels JF, Jansen J, Smeets B. Parietal epithelial cells maintain the epithelial cell continuum forming Bowman's space in focal segmental glomerulosclerosis. Dis Model Mech 2021; 15:273803. [PMID: 34927672 PMCID: PMC8938403 DOI: 10.1242/dmm.046342] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2020] [Accepted: 11/24/2021] [Indexed: 12/02/2022] Open
Abstract
In the glomerulus, Bowman's space is formed by a continuum of glomerular epithelial cells. In focal segmental glomerulosclerosis (FSGS), glomeruli show segmental scarring, a result of activated parietal epithelial cells (PECs) invading the glomerular tuft. The segmental scars interrupt the epithelial continuum. However, non-sclerotic segments seem to be preserved even in glomeruli with advanced lesions. We studied the histology of the segmental pattern in Munich Wistar Frömter rats, a model for secondary FSGS. Our results showed that matrix layers lined with PECs cover the sclerotic lesions. These PECs formed contacts with podocytes of the uninvolved tuft segments, restoring the epithelial continuum. Formed Bowman's spaces were still connected to the tubular system. In biopsies of patients with secondary FSGS, we also detected matrix layers formed by PECs, separating the uninvolved from the sclerotic glomerular segments. PECs have a major role in the formation of glomerulosclerosis; we show here that in FSGS they also restore the glomerular epithelial cell continuum that surrounds Bowman's space. This process may be beneficial and indispensable for glomerular filtration in the uninvolved segments of sclerotic glomeruli. Summary: Histological analysis of rat and human kidneys reveals a novel role for parietal epithelial cells (PECs) in glomerulosclerosis. PECs seem to restore the glomerular epithelial continuum, which may avert further loss of glomerular function.
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Affiliation(s)
- Laura Miesen
- Department of Pathology, Radboud Institute for Molecular Life Sciences, Radboud Institute for Health Sciences, Radboud university medical center, Nijmegen, the Netherlands
| | - Péter Bándi
- Department of Pathology, Radboud Institute for Molecular Life Sciences, Radboud Institute for Health Sciences, Radboud university medical center, Nijmegen, the Netherlands
| | - Brigith Willemsen
- Department of Pathology, Radboud Institute for Molecular Life Sciences, Radboud Institute for Health Sciences, Radboud university medical center, Nijmegen, the Netherlands
| | - Fieke Mooren
- Department of Pathology, Radboud Institute for Molecular Life Sciences, Radboud Institute for Health Sciences, Radboud university medical center, Nijmegen, the Netherlands
| | - Thiago Strieder
- Division of Nephrology and Immunology, University hospital of the RWTH Aachen, Germany
| | - Eva Boldrini
- Department of Pathology, Radboud Institute for Molecular Life Sciences, Radboud Institute for Health Sciences, Radboud university medical center, Nijmegen, the Netherlands
| | | | - Jennifer Eymael
- Department of Pathology, Radboud Institute for Molecular Life Sciences, Radboud Institute for Health Sciences, Radboud university medical center, Nijmegen, the Netherlands
| | - Roy Wetzels
- Department of Pathology, Radboud Institute for Molecular Life Sciences, Radboud Institute for Health Sciences, Radboud university medical center, Nijmegen, the Netherlands
| | - Johannes Lotz
- Fraunhofer Institute for Digital Medicine MEVIS, Lübeck, Germany
| | - Nick Weiss
- Fraunhofer Institute for Digital Medicine MEVIS, Lübeck, Germany
| | - Eric Steenbergen
- Department of Pathology, Radboud Institute for Molecular Life Sciences, Radboud Institute for Health Sciences, Radboud university medical center, Nijmegen, the Netherlands
| | - Toin H. van Kuppevelt
- Department of Biochemistry, Radboud Institute for Molecular Life Sciences, Radboud university medical center, Nijmegen, the Netherlands
| | - Merijn van Erp
- Department of Pathology, Radboud Institute for Molecular Life Sciences, Radboud Institute for Health Sciences, Radboud university medical center, Nijmegen, the Netherlands
| | - Jeroen van der Laak
- Department of Pathology, Radboud Institute for Molecular Life Sciences, Radboud Institute for Health Sciences, Radboud university medical center, Nijmegen, the Netherlands
| | - Nicole Endlich
- NIPOKA GmbH, 17489 Greifswald, Germany
- Department of Anatomy and Cell Biology, University Medicine Greifswald, 17489 Greifswald, Germany
| | - Marcus J. Moeller
- Division of Nephrology and Immunology, University hospital of the RWTH Aachen, Germany
| | - Jack F.M. Wetzels
- Department of Nephrology, Radboud Institute for Health Sciences, Radboud univerity medical center, Nijmegen, the Netherlands
| | - Jitske Jansen
- Department of Pathology, Radboud Institute for Molecular Life Sciences, Radboud Institute for Health Sciences, Radboud university medical center, Nijmegen, the Netherlands
- Department of Pediatric Nephrology, Radboud Institute for Molecular Life Sciences, Amalia Children's Hospital, Nijmegen, the Netherlands
| | - Bart Smeets
- Department of Pathology, Radboud Institute for Molecular Life Sciences, Radboud Institute for Health Sciences, Radboud university medical center, Nijmegen, the Netherlands
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6
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Bándi P, Balkenhol M, van Ginneken B, van der Laak J, Litjens G. Resolution-agnostic tissue segmentation in whole-slide histopathology images with convolutional neural networks. PeerJ 2019; 7:e8242. [PMID: 31871843 PMCID: PMC6924324 DOI: 10.7717/peerj.8242] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2019] [Accepted: 11/19/2019] [Indexed: 12/20/2022] Open
Abstract
Modern pathology diagnostics is being driven toward large scale digitization of microscopic tissue sections. A prerequisite for its safe implementation is the guarantee that all tissue present on a glass slide can also be found back in the digital image. Whole-slide scanners perform a tissue segmentation in a low resolution overview image to prevent inefficient high-resolution scanning of empty background areas. However, currently applied algorithms can fail in detecting all tissue regions. In this study, we developed convolutional neural networks to distinguish tissue from background. We collected 100 whole-slide images of 10 tissue samples—staining categories from five medical centers for development and testing. Additionally, eight more images of eight unfamiliar categories were collected for testing only. We compared our fully-convolutional neural networks to three traditional methods on a range of resolution levels using Dice score and sensitivity. We also tested whether a single neural network can perform equivalently to multiple networks, each specialized in a single resolution. Overall, our solutions outperformed the traditional methods on all the tested resolutions. The resolution-agnostic network achieved average Dice scores between 0.97 and 0.98 across the tested resolution levels, only 0.0069 less than the resolution-specific networks. Finally, its excellent generalization performance was demonstrated by achieving averages of 0.98 Dice score and 0.97 sensitivity on the eight unfamiliar images. A future study should test this network prospectively.
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Affiliation(s)
- Péter Bándi
- Department of Pathology, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Maschenka Balkenhol
- Department of Pathology, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Bram van Ginneken
- Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Jeroen van der Laak
- Department of Pathology, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Geert Litjens
- Department of Pathology, Radboud University Medical Center, Nijmegen, The Netherlands
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7
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Tellez D, Litjens G, Bándi P, Bulten W, Bokhorst JM, Ciompi F, van der Laak J. Quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathology. Med Image Anal 2019; 58:101544. [DOI: 10.1016/j.media.2019.101544] [Citation(s) in RCA: 179] [Impact Index Per Article: 35.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2019] [Revised: 06/25/2019] [Accepted: 08/19/2019] [Indexed: 11/17/2022]
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8
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Bulten W, Bándi P, Hoven J, Loo RVD, Lotz J, Weiss N, Laak JVD, Ginneken BV, Hulsbergen-van de Kaa C, Litjens G. Epithelium segmentation using deep learning in H&E-stained prostate specimens with immunohistochemistry as reference standard. Sci Rep 2019; 9:864. [PMID: 30696866 PMCID: PMC6351532 DOI: 10.1038/s41598-018-37257-4] [Citation(s) in RCA: 75] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2018] [Accepted: 12/03/2018] [Indexed: 12/24/2022] Open
Abstract
Given the importance of gland morphology in grading prostate cancer (PCa), automatically differentiating between epithelium and other tissues is an important prerequisite for the development of automated methods for detecting PCa. We propose a new deep learning method to segment epithelial tissue in digitised hematoxylin and eosin (H&E) stained prostatectomy slides using immunohistochemistry (IHC) as reference standard. We used IHC to create a precise and objective ground truth compared to manual outlining on H&E slides, especially in areas with high-grade PCa. 102 tissue sections were stained with H&E and subsequently restained with P63 and CK8/18 IHC markers to highlight epithelial structures. Afterwards each pair was co-registered. First, we trained a U-Net to segment epithelial structures in IHC using a subset of the IHC slides that were preprocessed with color deconvolution. Second, this network was applied to the remaining slides to create the reference standard used to train a second U-Net on H&E. Our system accurately segmented both intact glands and individual tumour epithelial cells. The generalisation capacity of our system is shown using an independent external dataset from a different centre. We envision this segmentation as the first part of a fully automated prostate cancer grading pipeline.
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Affiliation(s)
- Wouter Bulten
- Radboud University Medical Center, Diagnostic Image Analysis Group and the Department of Pathology, 6500HB, Nijmegen, The Netherlands.
| | - Péter Bándi
- Radboud University Medical Center, Diagnostic Image Analysis Group and the Department of Pathology, 6500HB, Nijmegen, The Netherlands
| | - Jeffrey Hoven
- Radboud University Medical Center, Department of Pathology, 6500HB, Nijmegen, The Netherlands
| | - Rob van de Loo
- Radboud University Medical Center, Department of Pathology, 6500HB, Nijmegen, The Netherlands
| | | | - Nick Weiss
- Fraunhofer MEVIS, 23562, Lübeck, Germany
| | - Jeroen van der Laak
- Radboud University Medical Center, Diagnostic Image Analysis Group and the Department of Pathology, 6500HB, Nijmegen, The Netherlands
| | - Bram van Ginneken
- Radboud University Medical Center, Diagnostic Image Analysis Group and the Department of Radiology and Nuclear Medicine, 6500HB, Nijmegen, The Netherlands
| | | | - Geert Litjens
- Radboud University Medical Center, Diagnostic Image Analysis Group and the Department of Pathology, 6500HB, Nijmegen, The Netherlands
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