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Ayorinde JO, Loizeau X, Bardsley V, Thomas SA, Romanchikova M, Samoshkin A, Pettigrew GJ. Measurement Matters: A Metrological Approach to Renal Preimplantation Biopsy Evaluation to Address Uncertainty in Organ Selection. Transplant Direct 2024; 10:e1708. [PMID: 39399062 PMCID: PMC11469905 DOI: 10.1097/txd.0000000000001708] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2024] [Revised: 07/07/2024] [Accepted: 07/23/2024] [Indexed: 10/15/2024] Open
Abstract
Background Preimplantation biopsy combines measurements of injury into a composite index to inform organ acceptance. The uncertainty in these measurements remains poorly characterized, raising concerns variability may contribute to inappropriate clinical decisions. Methods We adopted a metrological approach to evaluate biopsy score reliability. Variability was assessed by performing repeat biopsies (n = 293) on discarded allografts (n = 16) using 3 methods (core, punch, and wedge). Uncertainty was quantified using a bootstrapping analysis. Observer effects were controlled by semi-blinded scoring, and the findings were validated by comparison with standard glass evaluation. Results The surgical method strongly determined the size (core biopsy area 9.04 mm2, wedge 37.9 mm2) and, therefore, yield (glomerular yield r = 0.94, arterial r = 0.62) of each biopsy. Core biopsies yielded inadequate slides most frequently. Repeat biopsy of the same kidney led to marked variation in biopsy scores. In 10 of 16 cases, scores were contradictory, crossing at least 1 decision boundary (ie, to transplant or to discard). Bootstrapping demonstrated significant uncertainty associated with single-slide assessment; however, scores were similar for paired kidneys from the same donor. Conclusions Our investigation highlights the risks of relying on single-slide assessment to quantify organ injury. Biopsy evaluation is subject to uncertainty, meaning each slide is better conceptualized as providing an estimate of the kidney's condition rather than a definitive result. Pooling multiple assessments could improve the reliability of biopsy analysis, enhancing confidence. Where histological quantification is necessary, clinicians should seek to develop new protocols using more tissue and consider automated methods to assist pathologists in delivering analysis within clinical time frames.
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Affiliation(s)
- John O.O. Ayorinde
- Department of Surgery, University of Cambridge, Addenbrooke’s Hospital, Cambridge, United Kingdom
| | - Xavier Loizeau
- National Physical Laboratory, Teddington, United Kingdom
| | - Victoria Bardsley
- Department of Histopathology, Addenbrooke’s Hospital, Cambridge, United Kingdom
| | | | | | - Alex Samoshkin
- Office for Translational Research, School of Clinical Medicine, University of Cambridge, Cambridge, United Kingdom
| | - Gavin J. Pettigrew
- Department of Surgery, University of Cambridge, Addenbrooke’s Hospital, Cambridge, United Kingdom
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2
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Farhat I, Maréchal E, Calmo D, Ansart M, Paindavoine M, Bard P, Tarris G, Ducloux D, Felix SA, Martin L, Tinel C, Gibier JB, Funes de la Vega M, Rebibou JM, Bamoulid J, Legendre M. Recognition of intraglomerular histological features with deep learning in protocol transplant biopsies and their association with kidney function and prognosis. Clin Kidney J 2024; 17:sfae019. [PMID: 38370429 PMCID: PMC10873504 DOI: 10.1093/ckj/sfae019] [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: 10/31/2023] [Indexed: 02/20/2024] Open
Abstract
Background The Banff Classification may not adequately address protocol transplant biopsies categorized as normal in patients experiencing unexplained graft function deterioration. This study seeks to employ convolutional neural networks to automate the segmentation of glomerular cells and capillaries and assess their correlation with transplant function. Methods A total of 215 patients were categorized into three groups. In the Training cohort, glomerular cells and capillaries from 37 patients were manually annotated to train the networks. The Test cohort (24 patients) compared manual annotations vs automated predictions, while the Application cohort (154 protocol transplant biopsies) examined predicted factors in relation to kidney function and prognosis. Results In the Test cohort, the networks recognized histological structures with Precision, Recall, F-score and Intersection Over Union exceeding 0.92, 0.85, 0.89 and 0.74, respectively. Univariate analysis revealed associations between the estimated glomerular filtration rate (eGFR) at biopsy and relative endothelial area (r = 0.19, P = .027), endothelial cell density (r = 0.20, P = .017), mean parietal epithelial cell area (r = -0.38, P < .001), parietal epithelial cell density (r = 0.29, P < .001) and mesangial cell density (r = 0.22, P = .010). Multivariate analysis retained only endothelial cell density as associated with eGFR (Beta = 0.13, P = .040). Endothelial cell density (r = -0.22, P = .010) and mean podocyte area (r = 0.21, P = .016) were linked to proteinuria at biopsy. Over 44 ± 29 months, 25 patients (16%) reached the primary composite endpoint (dialysis initiation, or 30% eGFR sustained decline), with relative endothelial area, mean endothelial cell area and parietal epithelial cell density below medians linked to this endpoint [hazard ratios, respectively, of 2.63 (P = .048), 2.60 (P = .039) and 3.23 (P = .019)]. Conclusion This study automated the measurement of intraglomerular cells and capillaries. Our results suggest that the precise segmentation of endothelial and epithelial cells may serve as a potential future marker for the risk of graft loss.
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Affiliation(s)
- Imane Farhat
- Department of Nephrology, CHU Dijon, Dijon, France
| | | | - Doris Calmo
- Department of Nephrology, CHU Besançon, Besançon, France
| | - Manon Ansart
- LEAD-CNRS, UMR 5022, Université de Bourgogne, Dijon, France
| | | | - Patrick Bard
- LEAD-CNRS, UMR 5022, Université de Bourgogne, Dijon, France
| | | | - Didier Ducloux
- Department of Nephrology, CHU Besançon, Besançon, France
- Etablissement Français du sang, Besançon, France
| | | | | | - Claire Tinel
- Department of Nephrology, CHU Dijon, Dijon, France
- Etablissement Français du sang, Besançon, France
| | | | | | - Jean-Michel Rebibou
- Department of Nephrology, CHU Dijon, Dijon, France
- Etablissement Français du sang, Besançon, France
| | - Jamal Bamoulid
- Department of Nephrology, CHU Besançon, Besançon, France
- Etablissement Français du sang, Besançon, France
| | - Mathieu Legendre
- Department of Nephrology, CHU Dijon, Dijon, France
- LEAD-CNRS, UMR 5022, Université de Bourgogne, Dijon, France
- Etablissement Français du sang, Besançon, France
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3
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Feng C, Ong K, Young DM, Chen B, Li L, Huo X, Lu H, Gu W, Liu F, Tang H, Zhao M, Yang M, Zhu K, Huang L, Wang Q, Marini GPL, Gui K, Han H, Sanders SJ, Li L, Yu W, Mao J. Artificial intelligence-assisted quantification and assessment of whole slide images for pediatric kidney disease diagnosis. Bioinformatics 2024; 40:btad740. [PMID: 38058211 PMCID: PMC10796177 DOI: 10.1093/bioinformatics/btad740] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Revised: 11/13/2023] [Accepted: 12/05/2023] [Indexed: 12/08/2023] Open
Abstract
MOTIVATION Pediatric kidney disease is a widespread, progressive condition that severely impacts growth and development of children. Chronic kidney disease is often more insidious in children than in adults, usually requiring a renal biopsy for diagnosis. Biopsy evaluation requires copious examination by trained pathologists, which can be tedious and prone to human error. In this study, we propose an artificial intelligence (AI) method to assist pathologists in accurate segmentation and classification of pediatric kidney structures, named as AI-based Pediatric Kidney Diagnosis (APKD). RESULTS We collected 2935 pediatric patients diagnosed with kidney disease for the development of APKD. The dataset comprised 93 932 histological structures annotated manually by three skilled nephropathologists. APKD scored an average accuracy of 94% for each kidney structure category, including 99% in the glomerulus. We found strong correlation between the model and manual detection in detected glomeruli (Spearman correlation coefficient r = 0.98, P < .001; intraclass correlation coefficient ICC = 0.98, 95% CI = 0.96-0.98). Compared to manual detection, APKD was approximately 5.5 times faster in segmenting glomeruli. Finally, we show how the pathological features extracted by APKD can identify focal abnormalities of the glomerular capillary wall to aid in the early diagnosis of pediatric kidney disease. AVAILABILITY AND IMPLEMENTATION https://github.com/ChunyueFeng/Kidney-DataSet.
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Affiliation(s)
- Chunyue Feng
- Department of Nephrology, Children’s Hospital, Zhejiang University School of Medicine, Hangzhou 310000, China
- National Clinical Research Center for Child Health, Hangzhou 310000, China
| | - Kokhaur Ong
- Bioinformatics Institute, A*STAR, Singapore 138673, Singapore
| | - David M Young
- Institute of Molecular and Cell Biology, A*STAR, Singapore 138673, Singapore
- Department of Psychiatry and Behavioral Sciences, UCSF Weill Institute for Neurosciences, University of California, San Francisco, CA, 94143, United States
| | - Bingxian Chen
- Ningbo Konfoong Bioinformation Tech Co., Ltd., Ningbo 315000, China
| | - Longjie Li
- Bioinformatics Institute, A*STAR, Singapore 138673, Singapore
| | - Xinmi Huo
- Bioinformatics Institute, A*STAR, Singapore 138673, Singapore
| | - Haoda Lu
- Bioinformatics Institute, A*STAR, Singapore 138673, Singapore
- Institute for AI in Medicine, Nanjing University of Information Science and Technology, Nanjing 210044, China
| | - Weizhong Gu
- National Clinical Research Center for Child Health, Hangzhou 310000, China
- Department of Pathology, Children’s Hospital, Zhejiang University School of Medicine, Hangzhou, 310000, China
| | - Fei Liu
- Department of Nephrology, Children’s Hospital, Zhejiang University School of Medicine, Hangzhou 310000, China
- National Clinical Research Center for Child Health, Hangzhou 310000, China
| | - Hongfeng Tang
- National Clinical Research Center for Child Health, Hangzhou 310000, China
- Department of Pathology, Children’s Hospital, Zhejiang University School of Medicine, Hangzhou, 310000, China
| | - Manli Zhao
- National Clinical Research Center for Child Health, Hangzhou 310000, China
- Department of Pathology, Children’s Hospital, Zhejiang University School of Medicine, Hangzhou, 310000, China
| | - Min Yang
- National Clinical Research Center for Child Health, Hangzhou 310000, China
- Department of Pathology, Children’s Hospital, Zhejiang University School of Medicine, Hangzhou, 310000, China
| | - Kun Zhu
- National Clinical Research Center for Child Health, Hangzhou 310000, China
- Department of Pathology, Children’s Hospital, Zhejiang University School of Medicine, Hangzhou, 310000, China
| | - Limin Huang
- Department of Nephrology, Children’s Hospital, Zhejiang University School of Medicine, Hangzhou 310000, China
- National Clinical Research Center for Child Health, Hangzhou 310000, China
| | - Qiang Wang
- Ningbo Konfoong Bioinformation Tech Co., Ltd., Ningbo 315000, China
| | | | - Kun Gui
- Ningbo Konfoong Bioinformation Tech Co., Ltd., Ningbo 315000, China
| | - Hao Han
- Institute of Molecular and Cell Biology, A*STAR, Singapore 138673, Singapore
| | - Stephan J Sanders
- Department of Psychiatry and Behavioral Sciences, UCSF Weill Institute for Neurosciences, University of California, San Francisco, CA, 94143, United States
| | - Lin Li
- Department of Nephrology, Shanghai Changzheng Hospital, Naval Medical University, Shanghai 200003, China
| | - Weimiao Yu
- Bioinformatics Institute, A*STAR, Singapore 138673, Singapore
- Institute of Molecular and Cell Biology, A*STAR, Singapore 138673, Singapore
- Institute for AI in Medicine, Nanjing University of Information Science and Technology, Nanjing 210044, China
| | - Jianhua Mao
- Department of Nephrology, Children’s Hospital, Zhejiang University School of Medicine, Hangzhou 310000, China
- National Clinical Research Center for Child Health, Hangzhou 310000, China
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4
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Jacq A, Tarris G, Jaugey A, Paindavoine M, Maréchal E, Bard P, Rebibou JM, Ansart M, Calmo D, Bamoulid J, Tinel C, Ducloux D, Crepin T, Chabannes M, Funes de la Vega M, Felix S, Martin L, Legendre M. Automated evaluation with deep learning of total interstitial inflammation and peritubular capillaritis on kidney biopsies. Nephrol Dial Transplant 2023; 38:2786-2798. [PMID: 37197910 DOI: 10.1093/ndt/gfad094] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Indexed: 05/19/2023] Open
Abstract
BACKGROUND Interstitial inflammation and peritubular capillaritis are observed in many diseases on native and transplant kidney biopsies. A precise and automated evaluation of these histological criteria could help stratify patients' kidney prognoses and facilitate therapeutic management. METHODS We used a convolutional neural network to evaluate those criteria on kidney biopsies. A total of 423 kidney samples from various diseases were included; 83 kidney samples were used for the neural network training, 106 for comparing manual annotations on limited areas to automated predictions, and 234 to compare automated and visual gradings. RESULTS The precision, recall and F-score for leukocyte detection were, respectively, 81%, 71% and 76%. Regarding peritubular capillaries detection the precision, recall and F-score were, respectively, 82%, 83% and 82%. There was a strong correlation between the predicted and observed grading of total inflammation, as for the grading of capillaritis (r = 0.89 and r = 0.82, respectively, all P < .0001). The areas under the receiver operating characteristics curves for the prediction of pathologists' Banff total inflammation (ti) and peritubular capillaritis (ptc) scores were respectively all above 0.94 and 0.86. The kappa coefficients between the visual and the neural networks' scores were respectively 0.74, 0.78 and 0.68 for ti ≥1, ti ≥2 and ti ≥3, and 0.62, 0.64 and 0.79 for ptc ≥1, ptc ≥2 and ptc ≥3. In a subgroup of patients with immunoglobulin A nephropathy, the inflammation severity was highly correlated to kidney function at biopsy on univariate and multivariate analyses. CONCLUSION We developed a tool using deep learning that scores the total inflammation and capillaritis, demonstrating the potential of artificial intelligence in kidney pathology.
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Affiliation(s)
- Amélie Jacq
- Department of Nephrology, CHU Dijon, Dijon, France
| | | | - Adrien Jaugey
- ESIREM School, Dijon, France
- LEAD, Laboratoire de l'étude de l'apprentissage et du Développement, Dijon, France
| | - Michel Paindavoine
- LEAD, Laboratoire de l'étude de l'apprentissage et du Développement, Dijon, France
| | | | - Patrick Bard
- ESIREM School, Dijon, France
- LEAD, Laboratoire de l'étude de l'apprentissage et du Développement, Dijon, France
| | - Jean-Michel Rebibou
- Department of Nephrology, CHU Dijon, Dijon, France
- UMR 1098, INCREASE, Besançon, France
| | - Manon Ansart
- ESIREM School, Dijon, France
- LEAD, Laboratoire de l'étude de l'apprentissage et du Développement, Dijon, France
| | - Doris Calmo
- Department of Nephrology, CHU Besançon, Besançon, France
| | - Jamal Bamoulid
- UMR 1098, INCREASE, Besançon, France
- Department of Nephrology, CHU Besançon, Besançon, France
| | - Claire Tinel
- Department of Nephrology, CHU Dijon, Dijon, France
| | - Didier Ducloux
- UMR 1098, INCREASE, Besançon, France
- Department of Nephrology, CHU Besançon, Besançon, France
| | - Thomas Crepin
- UMR 1098, INCREASE, Besançon, France
- Department of Nephrology, CHU Besançon, Besançon, France
| | - Melchior Chabannes
- UMR 1098, INCREASE, Besançon, France
- Department of Nephrology, CHU Besançon, Besançon, France
| | | | - Sophie Felix
- Department of Pathology, CHU Besançon, Besançon, France
| | | | - Mathieu Legendre
- Department of Nephrology, CHU Dijon, Dijon, France
- UMR 1098, INCREASE, Besançon, France
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5
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Klinkhammer BM, Boor P. Kidney fibrosis: Emerging diagnostic and therapeutic strategies. Mol Aspects Med 2023; 93:101206. [PMID: 37541106 DOI: 10.1016/j.mam.2023.101206] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Accepted: 07/25/2023] [Indexed: 08/06/2023]
Abstract
An increasing number of patients worldwide suffers from chronic kidney disease (CKD). CKD is accompanied by kidney fibrosis, which affects all compartments of the kidney, i.e., the glomeruli, tubulointerstitium, and vasculature. Fibrosis is the best predictor of progression of kidney diseases. Currently, there is no specific anti-fibrotic therapy for kidney patients and invasive renal biopsy remains the only option for specific detection and quantification of kidney fibrosis. Here we review emerging diagnostic approaches and potential therapeutic options for fibrosis. We discuss how translational research could help to establish fibrosis-specific endpoints for clinical trials, leading to improved patient stratification and potentially companion diagnostics, and facilitating and optimizing development of novel anti-fibrotic therapies for kidney patients.
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Affiliation(s)
| | - Peter Boor
- Institute of Pathology, RWTH Aachen University Hospital, Aachen, Germany; Electron Microscopy Facility, RWTH Aachen University Hospital, Aachen, Germany; Division of Nephrology and Immunology, RWTH Aachen University Hospital, Aachen, Germany.
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6
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Luchian A, Cepeda KT, Harwood R, Murray P, Wilm B, Kenny S, Pregel P, Ressel L. Quantifying acute kidney injury in an Ischaemia-Reperfusion Injury mouse model using deep-learning-based semantic segmentation in histology. Biol Open 2023; 12:bio059988. [PMID: 37642317 PMCID: PMC10537956 DOI: 10.1242/bio.059988] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Accepted: 08/22/2023] [Indexed: 08/31/2023] Open
Abstract
This study focuses on ischaemia-reperfusion injury (IRI) in kidneys, a cause of acute kidney injury (AKI) and end-stage kidney disease (ESKD). Traditional kidney damage assessment methods are semi-quantitative and subjective. This study aims to use a convolutional neural network (CNN) to segment murine kidney structures after IRI, quantify damage via CNN-generated pathological measurements, and compare this to conventional scoring. The CNN was able to accurately segment the different pathological classes, such as Intratubular casts and Tubular necrosis, with an F1 score of over 0.75. Some classes, such as Glomeruli and Proximal tubules, had even higher statistical values with F1 scores over 0.90. The scoring generated based on the segmentation approach statistically correlated with the semiquantitative assessment (Spearman's rank correlation coefficient=0.94). The heatmap approach localised the intratubular necrosis mainly in the outer stripe of the outer medulla, while the tubular casts were also present in more superficial or deeper portions of the cortex and medullary areas. This study presents a CNN model capable of segmenting multiple classes of interest, including acute IRI-specific pathological changes, in a whole mouse kidney section and can provide insights into the distribution of pathological classes within the whole mouse kidney section.
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Affiliation(s)
- Andreea Luchian
- Department of Veterinary Anatomy Physiology and Pathology, Institute of Infection, Veterinary and Ecological Sciences, Faculty of Health & Life Sciences, University of Liverpool, Liverpool, CH64 7TE, UK
| | - Katherine Trivino Cepeda
- Department of Molecular Physiology and Cell Signalling, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, L69 7BE, UK
- Centre for Pre-clinical Imaging, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, L69 7TX, UK
| | - Rachel Harwood
- Department of Paediatric Surgery, Alder Hey in the Park, Liverpool, L14 5AB, UK
| | - Patricia Murray
- Department of Molecular Physiology and Cell Signalling, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, L69 7BE, UK
- Centre for Pre-clinical Imaging, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, L69 7TX, UK
| | - Bettina Wilm
- Department of Molecular Physiology and Cell Signalling, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, L69 7BE, UK
- Centre for Pre-clinical Imaging, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, L69 7TX, UK
| | - Simon Kenny
- Department of Paediatric Surgery, Alder Hey in the Park, Liverpool, L14 5AB, UK
| | - Paola Pregel
- Department of Veterinary Sciences, University of Turin, Turin, 8-10124, Italy
| | - Lorenzo Ressel
- Department of Veterinary Anatomy Physiology and Pathology, Institute of Infection, Veterinary and Ecological Sciences, Faculty of Health & Life Sciences, University of Liverpool, Liverpool, CH64 7TE, UK
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7
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Deng R, Liu Q, Cui C, Yao T, Long J, Asad Z, Womick RM, Zhu Z, Fogo AB, Zhao S, Yang H, Huo Y. Omni-Seg: A Scale-Aware Dynamic Network for Renal Pathological Image Segmentation. IEEE Trans Biomed Eng 2023; 70:2636-2644. [PMID: 37030838 PMCID: PMC10517077 DOI: 10.1109/tbme.2023.3260739] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/10/2023]
Abstract
Comprehensive semantic segmentation on renal pathological images is challenging due to the heterogeneous scales of the objects. For example, on a whole slide image (WSI), the cross-sectional areas of glomeruli can be 64 times larger than that of the peritubular capillaries, making it impractical to segment both objects on the same patch, at the same scale. To handle this scaling issue, prior studies have typically trained multiple segmentation networks in order to match the optimal pixel resolution of heterogeneous tissue types. This multi-network solution is resource-intensive and fails to model the spatial relationship between tissue types. In this article, we propose the Omni-Seg network, a scale-aware dynamic neural network that achieves multi-object (six tissue types) and multi-scale (5× to 40× scale) pathological image segmentation via a single neural network. The contribution of this article is three-fold: (1) a novel scale-aware controller is proposed to generalize the dynamic neural network from single-scale to multi-scale; (2) semi-supervised consistency regularization of pseudo-labels is introduced to model the inter-scale correlation of unannotated tissue types into a single end-to-end learning paradigm; and (3) superior scale-aware generalization is evidenced by directly applying a model trained on human kidney images to mouse kidney images, without retraining. By learning from 150,000 human pathological image patches from six tissue types at three different resolutions, our approach achieved superior segmentation performance according to human visual assessment and evaluation of image-omics (i.e., spatial transcriptomics).
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8
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Artificial Intelligence-Assisted Renal Pathology: Advances and Prospects. J Clin Med 2022; 11:jcm11164918. [PMID: 36013157 PMCID: PMC9410196 DOI: 10.3390/jcm11164918] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Revised: 07/30/2022] [Accepted: 08/11/2022] [Indexed: 11/17/2022] Open
Abstract
Digital imaging and advanced microscopy play a pivotal role in the diagnosis of kidney diseases. In recent years, great achievements have been made in digital imaging, providing novel approaches for precise quantitative assessments of nephropathology and relieving burdens of renal pathologists. Developing novel methods of artificial intelligence (AI)-assisted technology through multidisciplinary interaction among computer engineers, renal specialists, and nephropathologists could prove beneficial for renal pathology diagnoses. An increasing number of publications has demonstrated the rapid growth of AI-based technology in nephrology. In this review, we offer an overview of AI-assisted renal pathology, including AI concepts and the workflow of processing digital image data, focusing on the impressive advances of AI application in disease-specific backgrounds. In particular, this review describes the applied computer vision algorithms for the segmentation of kidney structures, diagnosis of specific pathological changes, and prognosis prediction based on images. Lastly, we discuss challenges and prospects to provide an objective view of this topic.
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9
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Sánchez-Jaramillo EA, Gasca-Lozano LE, Vera-Cruz JM, Hernández-Ortega LD, Salazar-Montes AM. Automated Computer-Assisted Image Analysis for the Fast Quantification of Kidney Fibrosis. BIOLOGY 2022; 11:biology11081227. [PMID: 36009854 PMCID: PMC9404825 DOI: 10.3390/biology11081227] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/16/2022] [Revised: 08/05/2022] [Accepted: 08/12/2022] [Indexed: 11/16/2022]
Abstract
Simple Summary Chronic kidney disease is a health problem in which the kidneys cannot function normally. Thus, they cannot filter blood effectively and cause waste accumulation in the organism, leading to serious health problems. Researchers use animals as models to replicate the human body’s behavior to understand this disease. In these studies, it is essential to evaluate the percentage of fibrosis (growth of fibrotic tissue similar to a scar in response to damage) to know the degree of kidney damage. Some researchers use programs to make the evaluation of fibrosis easier. However, this analysis is time-consuming because it needs to be made one image at a time and there are hundreds of samples in an animal model study. Here, we explain a method to conduct the same analysis but in a faster automated way with the assistance of a computer and a software package called CellProfiler™. The percentage of fibrosis using CellProfiler™ is similar to that obtained with the most widely used software for this kind of analysis called ImageJ. With the help of this approach, researchers can make more studies faster and easier and find new antifibrogenic therapies to address the common and worldwide health problem caused by chronic kidney disease. Abstract Chronic kidney disease (CKD) is a common and worldwide health problem and one of the most important causes of morbidity and mortality. Most primary research on this disease requires evaluating the fibrosis index in animal model kidneys, specifically using Masson’s trichrome stain. Different programs are used to calculate the percentage of fibrosis; however, the analysis is time-consuming since one image must be performed at a time. CellProfiler™ is a program designed to analyze data obtained from biological samples and can process multiple images through pipelines, and the results can be exported to databases. This article explains how CellProfiler™ can be used to automatically analyze kidney histology photomicrographs from samples stained with Masson’s trichrome stain to assess the percentage of fibrosis in an experimental animal model of CKD. A pipeline was created to analyze Masson’s trichrome-stained slides in a model of CDK induced by adenine at doses of 50 mg/kg and 100 mg/kg, in addition to samples with the vehicle (75% glycerin). The results were compared with those obtained by ImageJ, and no significant differences were found between both programs. The CellProfiler™ pipeline made here is a reliable, fast, and easy alternative for kidney fibrosis analysis and quantification in experimental animal models.
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Affiliation(s)
- Esteban Andrés Sánchez-Jaramillo
- Instituto de Investigación en Enfermedades Crónico-Degenerativas, Centro Universitario de Ciencias de la Salud, Universidad de Guadalajara, Guadalajara 44340, Jalisco, Mexico
| | - Luz Elena Gasca-Lozano
- Instituto de Investigación en Enfermedades Crónico-Degenerativas, Centro Universitario de Ciencias de la Salud, Universidad de Guadalajara, Guadalajara 44340, Jalisco, Mexico
| | - José María Vera-Cruz
- Instituto de Nutrigenética y Nutrigenómica Traslacional, Centro Universitario de Ciencias de la Salud, Universidad de Guadalajara, Guadalajara 44340, Jalisco, Mexico
| | - Luis Daniel Hernández-Ortega
- Centro de Investigación Multidisciplinario en Salud, Centro Universitario de Tonalá, Universidad de Guadalajara, Tonala 45425, Jalisco, Mexico
| | - Adriana María Salazar-Montes
- Instituto de Investigación en Enfermedades Crónico-Degenerativas, Centro Universitario de Ciencias de la Salud, Universidad de Guadalajara, Guadalajara 44340, Jalisco, Mexico
- Correspondence: or
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