Gotowe bibliografie tematyczne / Kidney-glomeruli segmentation

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Gotowa bibliografia na temat „Kidney-glomeruli segmentation”

Autor: Grafiati

Data publikacji: 23 listopada 2024

Data aktualizacji: 31 lipca 2025

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Artykuły w czasopismach na temat "Kidney-glomeruli segmentation"

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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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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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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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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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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Rozprawy doktorskie na temat "Kidney-glomeruli segmentation"

Nisar, Zeeshan. "Self-supervised learning in the presence of limited labelled data for digital histopathology." Electronic Thesis or Diss., Strasbourg, 2024. http://www.theses.fr/2024STRAD016.

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Un défi majeur dans l'application de l'apprentissage profond à l'histopathologie réside dans la variation des colorations, à la fois inter et intra-coloration. Les modèles d'apprentissage profond entraînés sur une seule coloration (ou domaine) échouent souvent sur d'autres, même pour la même tâche (par exemple, la segmentation des glomérules rénaux). L'annotation de chaque coloration est coûteuse et chronophage, ce qui pousse les chercheurs à explorer des méthodes de transfert de coloration basées sur l'adaptation de domaine. Celles-ci visent à réaliser une segmentation multi-coloration en uti

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Streszczenia konferencji na temat "Kidney-glomeruli segmentation"

Swain, Debabrata, Manish Kumar, and Krisha Patel. "YOLOv8 vs. Mask R-CNN: A Comparative Analysis of for Glomeruli Instance Segmentation in Kidney Tissues." In 2024 8th International Conference on Computing, Communication, Control and Automation (ICCUBEA). IEEE, 2024. https://doi.org/10.1109/iccubea61740.2024.10774679.

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Amar, Aurel J., Nyoman D. Kurniawan, Luise A. Cullen-McEwen, et al. "Automated 3D Segmentation of Glomeruli in Human Kidney Tissue Specimens Using 16.4 T MRI Without Contrast Agents." In 2025 IEEE 22nd International Symposium on Biomedical Imaging (ISBI). IEEE, 2025. https://doi.org/10.1109/isbi60581.2025.10980815.

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