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Artykuły w czasopismach na temat „Kidney-glomeruli segmentation”

Autor: Grafiati

Data publikacji: 23 listopada 2024

Data aktualizacji: 31 lipca 2025

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1

Altini, Nicola, Giacomo Donato Cascarano, Antonio Brunetti, et al. "A Deep Learning Instance Segmentation Approach for Global Glomerulosclerosis Assessment in Donor Kidney Biopsies." Electronics 9, no. 11 (2020): 1768. http://dx.doi.org/10.3390/electronics9111768.

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The histological assessment of glomeruli is fundamental for determining if a kidney is suitable for transplantation. The Karpinski score is essential to evaluate the need for a single or dual kidney transplant and includes the ratio between the number of sclerotic glomeruli and the overall number of glomeruli in a kidney section. The manual evaluation of kidney biopsies performed by pathologists is time-consuming and error-prone, so an automatic framework to delineate all the glomeruli present in a kidney section can be very useful. Our experiments have been conducted on a dataset provided by

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Han, Yutong, Zhan Zhang, Yafeng Li, et al. "FastCellpose: A Fast and Accurate Deep-Learning Framework for Segmentation of All Glomeruli in Mouse Whole-Kidney Microscopic Optical Images." Cells 12, no. 23 (2023): 2753. http://dx.doi.org/10.3390/cells12232753.

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Automated evaluation of all glomeruli throughout the whole kidney is essential for the comprehensive study of kidney function as well as understanding the mechanisms of kidney disease and development. The emerging large-volume microscopic optical imaging techniques allow for the acquisition of mouse whole-kidney 3D datasets at a high resolution. However, fast and accurate analysis of massive imaging data remains a challenge. Here, we propose a deep learning-based segmentation method called FastCellpose to efficiently segment all glomeruli in whole mouse kidneys. Our framework is based on Cellp

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Altini, Nicola, Giacomo Donato Cascarano, Antonio Brunetti, et al. "Semantic Segmentation Framework for Glomeruli Detection and Classification in Kidney Histological Sections." Electronics 9, no. 3 (2020): 503. http://dx.doi.org/10.3390/electronics9030503.

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The evaluation of kidney biopsies performed by expert pathologists is a crucial process for assessing if a kidney is eligible for transplantation. In this evaluation process, an important step consists of the quantification of global glomerulosclerosis, which is the ratio between sclerotic glomeruli and the overall number of glomeruli. Since there is a shortage of organs available for transplantation, a quick and accurate assessment of global glomerulosclerosis is essential for retaining the largest number of eligible kidneys. In the present paper, the authors introduce a Computer-Aided Diagno

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4

Dimitri, Giovanna Maria, Paolo Andreini, Simone Bonechi, et al. "Deep Learning Approaches for the Segmentation of Glomeruli in Kidney Histopathological Images." Mathematics 10, no. 11 (2022): 1934. http://dx.doi.org/10.3390/math10111934.

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Deep learning is widely applied in bioinformatics and biomedical imaging, due to its ability to perform various clinical tasks automatically and accurately. In particular, the application of deep learning techniques for the automatic identification of glomeruli in histopathological kidney images can play a fundamental role, offering a valid decision support system tool for the automatic evaluation of the Karpinski metric. This will help clinicians in detecting the presence of sclerotic glomeruli in order to decide whether the kidney is transplantable or not. In this work, we implemented a deep

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Javvadi, Sai. "Evaluating the Impact of Color Normalization on Kidney Image Segmentation." International Journal on Cybernetics & Informatics 12, no. 5 (2023): 93–105. http://dx.doi.org/10.5121/ijci.2023.120509.

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The role of deep learning in the recognition of morphological structures in histopathological data has progressed significantly. But, less intensive preprocessing stages and their contribution to deep learning pipelines is often overlooked. Color normalization (CN) algorithms are among the most prominent methods in this stage, and they work by standardizing the staining pattern of a dataset. However, the impact of various color normalization algorithms on the detection of glomeruli functional tissue units (FTUs) in kidney tissue data has not been explored before. An advanced deep learning arch

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6

Hermsen, Meyke, Thomas de Bel, Marjolijn den Boer, et al. "Deep Learning–Based Histopathologic Assessment of Kidney Tissue." Journal of the American Society of Nephrology 30, no. 10 (2019): 1968–79. http://dx.doi.org/10.1681/asn.2019020144.

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BackgroundThe development of deep neural networks is facilitating more advanced digital analysis of histopathologic images. We trained a convolutional neural network for multiclass segmentation of digitized kidney tissue sections stained with periodic acid–Schiff (PAS).MethodsWe trained the network using multiclass annotations from 40 whole-slide images of stained kidney transplant biopsies and applied it to four independent data sets. We assessed multiclass segmentation performance by calculating Dice coefficients for ten tissue classes on ten transplant biopsies from the Radboud University M

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7

Kawazoe, Yoshimasa, Kiminori Shimamoto, Ryohei Yamaguchi, et al. "Computational Pipeline for Glomerular Segmentation and Association of the Quantified Regions with Prognosis of Kidney Function in IgA Nephropathy." Diagnostics 12, no. 12 (2022): 2955. http://dx.doi.org/10.3390/diagnostics12122955.

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The histopathological findings of the glomeruli from whole slide images (WSIs) of a renal biopsy play an important role in diagnosing and grading kidney disease. This study aimed to develop an automated computational pipeline to detect glomeruli and to segment the histopathological regions inside of the glomerulus in a WSI. In order to assess the significance of this pipeline, we conducted a multivariate regression analysis to determine whether the quantified regions were associated with the prognosis of kidney function in 46 cases of immunoglobulin A nephropathy (IgAN). The developed pipeline

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8

Marechal, Elise, Adrien Jaugey, Georges Tarris, et al. "Automatic Evaluation of Histological Prognostic Factors Using Two Consecutive Convolutional Neural Networks on Kidney Samples." Clinical Journal of the American Society of Nephrology 17, no. 2 (2021): 260–70. http://dx.doi.org/10.2215/cjn.07830621.

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Background and objectivesThe prognosis of patients undergoing kidney tumor resection or kidney donation is linked to many histologic criteria. These criteria notably include glomerular density, glomerular volume, vascular luminal stenosis, and severity of interstitial fibrosis/tubular atrophy. Automated measurements through a deep-learning approach could save time and provide more precise data. This work aimed to develop a free tool to automatically obtain kidney histologic prognostic features.Design, setting, participants, & measurementsIn total, 241 samples of healthy kidney tissue were

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Dr. Harikiran Jonnadula, Sitanaboina S. L. Parvathi,. "Small Blob Detection and Classification in 3D MRI Human Kidney Images Using IMBKM and EDCNN Classifier." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 5 (2021): 629–42. http://dx.doi.org/10.17762/turcomat.v12i5.1061.

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The spatial and temporal resolution is dramatically increased due to the quick development of medical imaging technology, which in turn increases the size of clinical imaging data. Typically, it is very challenging to do small blob segmentation as of Medical Images (MI) but it encompasses so many vital applications. Some examples are labelling cell, lesion, along with glomeruli aimed at disease diagnosis. Though various detectors were suggested by the prevailing method for this type of issue, they mostly used 2D detectors, which may render less detection accuracy. To trounce this, the system h

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Celia, A. I., X. Yang, M. A. Petri, A. Rosenberg, and A. Fava. "POS0288 DEGRANULATING PR3+ MYELOID CELLS CHARACTERIZE PROLIFERATIVE LUPUS NEPHRITIS." Annals of the Rheumatic Diseases 82, Suppl 1 (2023): 385.2–386. http://dx.doi.org/10.1136/annrheumdis-2023-eular.767.

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BackgroundDespite optimal treatment, lupus nephritis (LN) remains associated with irreversible kidney damage[1]. A better understanding of the mechanisms underlying LN pathogenesis is needed to develop better treatment targets. As part of the Accelerating Medicines Partnership (AMP), we discovered that urinary PR3, a myeloid degranulation product, correlated with histological activity implicating neutrophil/monocyte degranulation in proliferative LN, the most aggressive type[2]. PR3 is a serine protease that can mediate kidney damage. Mature neutrophils with classical polylobate nuclei are rar

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11

Hara, Satoshi, Emi Haneda, Masaki Kawakami, et al. "Evaluating tubulointerstitial compartments in renal biopsy specimens using a deep learning-based approach for classifying normal and abnormal tubules." PLOS ONE 17, no. 7 (2022): e0271161. http://dx.doi.org/10.1371/journal.pone.0271161.

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Renal pathology is essential for diagnosing and assessing the severity and prognosis of kidney diseases. Deep learning-based approaches have developed rapidly and have been applied in renal pathology. However, methods for the automated classification of normal and abnormal renal tubules remain scarce. Using a deep learning-based method, we aimed to classify normal and abnormal renal tubules, thereby assisting renal pathologists in the evaluation of renal biopsy specimens. Consequently, we developed a U-Net-based segmentation model using randomly selected regions obtained from 21 renal biopsy s

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Zlobina, Olga V., Alexey N. Ivanov, Taisiya V. Milashevskaya, Valeria Yu Seryogina, and Irina O. Bugaeva. "Comparative analysis of morphological changes in renal tissue under the influence of light desynchronosis." Morphology 159, no. 2 (2022): 63–70. http://dx.doi.org/10.17816/1026-3543-2021-159-2-63-70.

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AIM: To compare morphological changes that occur in renal tissue, as a result of exposure to various models of light desynchronosis. MATERIAL AND METHODS: This study was conducted on 48 white rats. Three experimental groups were exposed to light for 21 days. The LL (0:24) model was studied in the first group, while the LD 18:6 and 12:10 models were studied in the second and third groups, respectively. The control group was kept in natural conditions all through the experiment. The animals were placed under anesthesia with a combination of Telazol (ZoetisInc, USA) and Xylanit (Nita-

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13

Hao, Fang, Xueyu Liu, Ming Li, and Weixia Han. "Accurate Kidney Pathological Image Classification Method Based on Deep Learning and Multi-Modal Fusion Method with Application to Membranous Nephropathy." Life 13, no. 2 (2023): 399. http://dx.doi.org/10.3390/life13020399.

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Membranous nephropathy is one of the most prevalent conditions responsible for nephrotic syndrome in adults. It is clinically nonspecific and mainly diagnosed by kidney biopsy pathology, with three prevalent techniques: light microscopy, electron microscopy, and immunofluorescence microscopy. Manual observation of glomeruli one by one under the microscope is very time-consuming, and there are certain differences in the observation results between physicians. This study makes use of whole-slide images scanned by a light microscope as well as immunofluorescence images to classify patients with m

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14

Shen, Luping, Wenyi Sun, Qixiang Zhang, et al. "Deep Learning-Based Model Significantly Improves Diagnostic Performance for Assessing Renal Histopathology in Lupus Glomerulonephritis." Kidney Diseases 8, no. 4 (2022): 326–35. http://dx.doi.org/10.1159/000524880.

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<b><i>Background:</i></b> Assessment of glomerular lesions and structures plays an essential role in understanding the pathological diagnosis of glomerulonephritis and prognostic evaluation of many kidney diseases. Renal pathophysiological assessment requires novel high-throughput tools to conduct quantitative, unbiased, and reproducible analyses representing a central readout. Deep learning may be an effective tool for glomerulonephritis pathological analysis. <b><i>Methods:</i></b> We developed a murine renal pathological system (MRPS) model to

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15

Hölscher, David, Nassim Bouteldja, Yu-Chia Lan, Saskia von Stillfried, Roman David Bülow, and Peter Boor. "MO062: Pan-Disease Segmentation and Feature Extraction for Large-Scale Digital Nephropathology." Nephrology Dialysis Transplantation 37, Supplement_3 (2022). http://dx.doi.org/10.1093/ndt/gfac063.014.

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Abstract BACKGROUND AND AIMS Nephropathology is essential for the diagnosis of kidney diseases. Deep learning-based image analyses, including segmentation of kidney histology, open new possibilities for reproducible quantitative precision pathology. Current segmentation approaches in kidney histology focused on specific use cases. Here, we developed a framework for automated segmentation and quantification of a wide spectrum of non-neoplastic kidney diseases. METHOD We trained U-Net Convolutional Neural Networks (CNNs) for two streamlined tasks: (i) detection of kidney tissue and (ii) instance

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Klaus, Martin, Rosch Ronny Abdullah, Qiubo LI, Christoph Walz, Hans Joachim Anders, and Stefanie Steiger. "#439 Nephron number determination in whole slide mice kidney specimens using a deep learning assisted disector/fractionator approach." Nephrology Dialysis Transplantation 39, Supplement_1 (2024). http://dx.doi.org/10.1093/ndt/gfae069.277.

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Abstract Background and Aims Nephron number highly varies between different species and within species, ranging in humans from 200 000 to 2 000 000 nephrons per kidney. Low nephron numbers can promote early onset of end-stage kidney disease. Nephron assessment is highly needed for better diagnosis and more targeted treatment. However, its assessment in vivo is not established in clinical practice yet. Pioneer studies suggest nephron number estimation in vivo through a combination of glomerular densities in kidney biopsies together with medical imaging. In contrast, post-mortem nephron assessme

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Kaur, Gurjinder, Meenu Garg, and Sheifali Gupta. "Integrated Model for Segmentation of Glomeruli in Kidney Images." Cognitive Robotics, November 2024. https://doi.org/10.1016/j.cogr.2024.11.007.

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Li, Xiang, Richard C. Davis, Yuemei Xu, et al. "Deep learning segmentation of glomeruli on kidney donor frozen sections." Journal of Medical Imaging 8, no. 06 (2021). http://dx.doi.org/10.1117/1.jmi.8.6.067501.

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Lutnick, Brendon, Katharina Moos, Surya V. Seshan, et al. "MO077AUTOMATIC SEGMENTATION OF ARTERIES, ARTERIOLES AND GLOMERULI IN NATIVE BIOPSIES WITH THROMBOTIC MICROANGIOPATHY AND OTHER VASCULAR DISEASES." Nephrology Dialysis Transplantation 36, Supplement_1 (2021). http://dx.doi.org/10.1093/ndt/gfab078.0013.

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Abstract Background and Aims Thrombotic microangiopathies (TMAs) manifest themselves in arteries, arterioles and glomeruli. Nephropathologists need to differentiate TMAs from mimickers like hypertensive nephropathy and vasculitis which can be problematic due to interobserver disagreement and poorly defined diagnostic criteria over a wide spectrum of morphological changes with partial overlap. As a first step towards a machine learning analysis of TMAs, we developed a computer vision model for segmenting arteries, arterioles and glomeruli in TMA and mimickers. Method We manually segmented n=939

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Shipman, Katherine, Catherine Baty, and Ora Weisz. "Fluorescent Labeling Approach for Visualizing and Quantifying Glomerular Density and Volume in Mouse Kidneys." Physiology 40, S1 (2025). https://doi.org/10.1152/physiol.2025.40.s1.0450.

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Glomeruli number and glomerular volume are indicators of kidney health and chronic kidney disease (CKD) progression. Low nephron number is predictive of the development of CKD, and increased glomerular volume is associated with hyperfiltration and hypertension. Accurate estimates of glomeruli number are necessary for relating whole body GFR to single nephron GFR (SNGFR) which is an important parameter for mathematical modeling of transport along the nephron. In addition, previous studies had observed larger volumes and greater SNGFR in juxtamedullary glomeruli compared to cortical glomeruli. T

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MANSOUR, Mohammed, Mert Süleyman DEMİRSOY, and Mustafa Çağrı KUTLU. "Kidney Segmentations Using CNN models." Journal of Smart Systems Research, March 5, 2023. http://dx.doi.org/10.58769/joinssr.1175622.

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For medical diagnostic tests, kidney segmentation from high-volume imagery is an important major. Since 3D medical images need a lot of GPU memory, slices and patches are used for training and inference in traditional neural network variant architectures, which necessarily slows down contextual learning. In this research, Mobile Net and Efficient Net CNN models were trained for segmenting human kidney images generated from The Human Biomolecular Atlas Program (HuBMAP). The purpose of this work is to evaluate the effectiveness of different strategies for Glomeruli identification in order to sol

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Bhaskara, Suneil, Michael Ferkowicz, Daria Barwinska, and Tarek M. Ashkar (El-Achkar). "Three-dimensional morphometric analysis of human kidney nephron structures in health and disease." Proceedings of IMPRS 4, no. 1 (2021). http://dx.doi.org/10.18060/25866.

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Background and Hypothesis: Chronic kidney disease (CKD) is very common and affects as many as 37 million people in the United States. Diabetes and hypertension are the most common causes of CKD. The pathogenesis of CKD is not fully elucidated. Morphological changes such as glomerulosclerosis and tubular atrophy are commonly observed with advanced disease. However, it is unclear if such changes occur at earlier stages of disease, a time when therapeutic interventions are likely to have the most benefit. Measurements of glomerular, vascular and tubular dimensions have been typically derived from

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Marechal, Elise, Adrien Jaugey, Georges Tarris, et al. "FC046: Automated Mest-C Classification in IGA Nephropathy using Deep-Learning based Segmentation." Nephrology Dialysis Transplantation 37, Supplement_3 (2022). http://dx.doi.org/10.1093/ndt/gfac105.002.

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Abstract BACKGROUND AND AIMS IgA nephropathy prognosis depends on histological factors. The MEST-C score is used to grade those factors and assess the renal prognosis of this disease. Nevertheless, this manual evaluation is time-consuming, tedious and has a poor reproducibility. An automated analysis would be faster and more objective. This work aimed to use deep-learning techniques on whole kidney biopsies from patients suffering from IgA nephropathy to obtain an automated MEST-C score. METHOD We used a previously developed convolutional neural network (CNN) to isolate the cortical area. Then

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Jin, Feng Yong, Yang Huang, Yu Lin Huang, Qing Zhao, Qin Yi Wu, and Kun Ling Ma. "#2775 USING A DEEP LEARNING MODEL TO EVALUATE PATHOLOGICAL INJURY OF DIABETIC KIDNEY DISEASE." Nephrology Dialysis Transplantation 38, Supplement_1 (2023). http://dx.doi.org/10.1093/ndt/gfad063b_2775.

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Abstract Background and Aims Early diagnosis and evaluation play an important role in preventing the progression of diabetic kidney disease (DKD). Renal biopsy is the gold standard of DKD diagnosis. In 2010, the Renal Pathology Society (RPS) developed a consensus classification for DKD, which classifies DKD glomerular lesions and scores for tubulointerstitial lesion. However, as the pathologic heterogeneity of DKD patients and unparallel relationship between pathology features and clinical symptoms, it remains controversial whether is reliable to use RPS classification for renal outcomes predi

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Feng, Chunyue, Kokhaur Ong, David M. Young, et al. "Artificial intelligence-assisted quantification and assessment of whole slide images for pediatric kidney disease diagnosis." Bioinformatics, December 7, 2023. http://dx.doi.org/10.1093/bioinformatics/btad740.

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Abstract Motivation Pediatric kidney disease is a widespread, progressive condition that severely impacts growth and development of children. Chronic Kidney Disease (CKD) 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 (

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Klaus, Martin, Manga Motrapu, Stefanie Steiger, and Hans Joachim Anders. "#2863 SEX-, AGING AND DIABETES-RELATED ALTERATIONS IN GLOMERULAR DIMENSIONS AND PODOCYTE DENSITIES USING DEEP-LEARNING QUANTIFICATION." Nephrology Dialysis Transplantation 38, Supplement_1 (2023). http://dx.doi.org/10.1093/ndt/gfad063c_2863.

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Abstract Background and Aims Kidney function, as well as its morphology, changes markedly with age and disorders such as diabetes. This process is associated with structural and functional alterations in cortical and juxtamedullary glomeruli. Currently, data on differences in cortical and juxtamedullary glomeruli associated with sex, age, genetic factors, and diabetes are limited. In this study, we investigated the abundance and morphometry of podocytes and glomeruli in mice of different ages and sex’, and suffering from diabetes or not using a deep-learning based analysis of immuno-stained ki

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Lucarelli, Nicholas, Brandon Ginley, Jarcy Zee, et al. "Correlating Deep Learning-Based Automated Reference Kidney Histomorphometry with Patient Demographics and Creatinine." Kidney360, November 14, 2023. http://dx.doi.org/10.34067/kid.0000000000000299.

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Background: Reference histomorphometric data of healthy human kidneys are largely lacking due to laborious quantitation requirements. Correlating histomorphometric features with clinical parameters through machine learning approaches can provide valuable information about natural population variance. To this end, we leveraged deep learning, computational image analysis, and feature analysis to associate the relationship of histomorphometry with patient age, sex, serum creatinine (SCr), and estimated glomerular filtration rate (eGFR) in a multinational set of reference kidney tissue sections. M

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Luchian, Andreea, and Lorenzo Ressel. "Combined Colour Deconvolution and Artificial Intelligence approach for region‐selective immunohistochemical labelling quantification. The example of alpha Smooth Muscle Actin in mouse kidney." Journal of Biophotonics, October 25, 2023. http://dx.doi.org/10.1002/jbio.202300244.

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AbstractImmunohistochemical (IHC) localisation of protein expression is a widely used tool in pathology. This is semi‐quantitative and exhibits substantial intra‐and inter‐observer variability. Digital approaches based on stain quantification applied to IHC are precise but still operator‐dependent and time‐consuming when regions of interest (ROIs) must be defined to quantify protein expression in a specific tissue area. This study aimed at developing an IHC quantification workflow that benefits from colour deconvolution for stain quantification and artificial intelligence for automatic ROI def

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Wajeeh Us Sima, Muhammad, Chengliang Wang, Muhammad Arshad, Jamshed Ali Shaikh, Salem Alkhalaf, and Fahad Alturise. "Leveraging advanced feature extraction for improved kidney biopsy segmentation." Frontiers in Medicine 12 (June 18, 2025). https://doi.org/10.3389/fmed.2025.1591999.

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Medical image segmentation faces critical challenges in renal histopathology due to the intricate morphology of glomeruli characterized by small size, fragmented structures, and low contrast against complex tissue backgrounds. While the Segment Anything Model (SAM) excels in natural image segmentation, its direct application to medical imaging underperforms due to (1) insufficient preservation of fine-grained anatomical details, (2) computational inefficiency on gigapixel whole-slide images (WSIs), and (3) poor adaptation to domain-specific features like staining variability and sparse annotat

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Luchian, Andreea, Katherine Trivino Cepeda, Rachel Harwood, et al. "Quantifying acute kidney injury in an Ischaemia-Reperfusion Injury mouse model using Deep Learning-based semantic segmentation in histology." Biology Open, August 29, 2023. http://dx.doi.org/10.1242/bio.059988.

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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 clas

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Mikhailov, Alexei V., Heng-Jie Cheng, Jen-Jar Lin, and Che Ping Cheng. "Abstract 9396: Calmodulin-Dependent Protein Kinase II Activation Promotes Kidney Mesangial Expansion in Streptozotocin-Induced Diabetic Mice." Circulation 146, Suppl_1 (2022). http://dx.doi.org/10.1161/circ.146.suppl_1.9396.

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Background: Calmodulin-dependent protein kinase II (CaMKII) is upregulated in diabetes mellitus (DM), leading to the overproduction of collagen in the myocardium. We hypothesized that CaMKII also stimulates the extracellular matrix production in the kidneys leading to mesangial matrix expansion, a hallmark of diabetic nephropathy and a major cause of renal failure. To test this hypothesis, we developed an automated process of mesangial matrix measurement. Methods: We induced Type 1 diabetes (DM1) in wild type female FVB mice via an intraperitoneal injection of Streptozotocin (STZ, 200 mg/kg ip

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Siegerist, Florian, Eleonora Hay, Julien Dang, et al. "FC 017DEEP-LEARNING ENABLED QUANTIFICATION OF SINGLE-CELL SINGLE-MRNA TRANSCRIPTS AND CORRELATIVE SUPER-RESOLVED PODOCYTE FOOT PROCESS MORPHOMETRY IN ROUTINE KIDNEY BIOPSY SPECIMEN." Nephrology Dialysis Transplantation 36, Supplement_1 (2021). http://dx.doi.org/10.1093/ndt/gfab138.003.

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Abstract Background and Aims Although high-throughput single-cell transcriptomic analysis, super-resolution light microscopy and deep-learning methods are broadly used, the gold-standard to evaluate kidney biopsies is still the histologic assessment of formalin-fixed and paraffin embedded (FFPE) samples with parallel ultrastructural evaluation. Recently, we and others have shown that super-resolution fluorescence microscopy can be used to study glomerular ultrastructure in human biopsy samples. Additionally, in the last years mRNA in situ hybridization techniques have been improved to increase

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Selvaskandan, Haresh, Charlotte Boys, Izabella Pawluczyk, and Jonathan Barratt. "#3979 DIGITAL SPATIAL PROFILING CAN BE USED TO STUDY GLOMERULAR ENDOTHELIAL CELLS IN IGA NEPHROPATHY." Nephrology Dialysis Transplantation 38, Supplement_1 (2023). http://dx.doi.org/10.1093/ndt/gfad063c_3979.

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Abstract Background and Aims IgA nephropathy (IgAN) is the most common primary glomerular disease worldwide; approximately 30% of cases progress to kidney failure 10-20 years from diagnosis. Five histopathological kidney lesions independently predict a poor prognosis in IgAN (MEST-C score) [1]. Published case series highlight the ‘endocapillary hypercellularity’ (E1) lesion as potentially reversible with systemic immunosuppression, improving clinical outcomes [2]. Delineating differences in the transcriptomes of glomerular endothelial cells (GEnCs) associated with and without E1 (E0) may highl

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Schmitz, Jessica, David Christensen, Lena Müller, et al. "MO055: Multi-Class Segmentation of Kidney Tissues using Convolutional Neuronal Networks (CNNS)." Nephrology Dialysis Transplantation 37, Supplement_3 (2022). http://dx.doi.org/10.1093/ndt/gfac063.007.

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Abstract BACKGROUND AND AIMS Routine pathological diagnostics in kidneys are mainly based on semi-quantitative eyeballing. In own former studies, we showed predictive value of precise immune cell quantification in allografts using digital semi-automated techniques. We now aim to achieve fully automated segmentation workflow with CNNs. METHOD Standard routine stains (immuno/histochemistry, immunofluorescence) were digitized (20×) with Metafer, a commercial scanning/imaging platform. Diagnostically relevant anatomical compartments (cortex, medulla, glomeruli, tubuli [proximal/distal/collecting d

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Martínez Mora, Andrés, Elin Lundström, Taro Langner, et al. "MO621AGE-RELATED PATTERNS OF KIDNEY PARENCHIMAL VOLUME IN T1D, T2D AND DIFFERENT TREATMENT GROUPS OF T2D: A CROSS-SECTIONAL STUDY OF 35,703 UK BIOBANK PARTICIPANTS." Nephrology Dialysis Transplantation 36, Supplement_1 (2021). http://dx.doi.org/10.1093/ndt/gfab093.002.

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Abstract Background and Aims Kidney parenchymal volume (KPV) presents a natural variation with respect to sex, age, and body size, and is also affected by diseases such as diabetes. The UK Biobank (UKBB) is a large-scale study including clinical and MRI data. The current project investigated the association between KPV and age in UKBB participants without diabetes and with diabetes type 1 (T1D), and type 2 (T2D). In addition, the effect of different treatments for T2D on KPV was investigated. Method KPV was estimated in 35,703 UKBB participants (52% women, age = 45-82 years) with a deep-learni

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Kuo, Willy, Diego Rossinelli, Georg Schulz, et al. "Terabyte-scale supervised 3D training and benchmarking dataset of the mouse kidney." Scientific Data 10, no. 1 (2023). http://dx.doi.org/10.1038/s41597-023-02407-5.

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AbstractThe performance of machine learning algorithms, when used for segmenting 3D biomedical images, does not reach the level expected based on results achieved with 2D photos. This may be explained by the comparative lack of high-volume, high-quality training datasets, which require state-of-the-art imaging facilities, domain experts for annotation and large computational and personal resources. The HR-Kidney dataset presented in this work bridges this gap by providing 1.7 TB of artefact-corrected synchrotron radiation-based X-ray phase-contrast microtomography images of whole mouse kidneys

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