ArticleDiagnostics (Basel, Switzerland)2022
Deep Segmentation Networks for Segmenting Kidneys and Detecting Kidney Stones in Unenhanced Abdominal CT Images.
Article in Diagnostics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Frugal Learning Methods for Kidney Segmentation in Non-Contrast MRI.Journal of clinical medicine · 2026Article
- Pyramid Xception network for accurate detection of abdominal abnormalities in CT scans.Scientific reports · 2026Article
- Automatically Measuring Kidney, Liver, and Cyst Volumes in Autosomal Dominant Polycystic Kidney Disease.Journal of the American Society of Nephrology : JASN · 2026Article
- Open-source domain adaptation to handle data shift for volumetric segmentation-use case kidney segmentation.European radiology · 2026Article
- Impact of Intraoperative Digital Stone Measurement on Surgeon Decision Making in Ureteroscopy.Urology · 2025Article
- Artificial Intelligence in Nephrology: From Early Detection to Clinical Management of Kidney Diseases.Bioengineering (Basel, Switzerland) · 2025Review
- Enhancing Medical Image Segmentation and Classification Using a Fuzzy-Driven Method.Sensors (Basel, Switzerland) · 2025Article
- Fine-tuned deep learning models for early detection and classification of kidney conditions in CT imaging.Scientific reports · 2025Article
- CNN-Based Kidney Segmentation Using a Modified CLAHE Algorithm.Sensors (Basel, Switzerland) · 2024Article
- Artificial Intelligence Tools in Pediatric Urology: A Comprehensive Review of Recent Advances.Diagnostics (Basel, Switzerland) · 2024Review
- Automatic substantia nigra segmentation with Swin-Unet in susceptibility- and T2-weighted imaging: application to Parkinson disease diagnosis.Quantitative imaging in medicine and surgery · 2024Article
- Acoustic emission of kidney stones: a medical adaptation of statistical breakdown mechanisms.Urolithiasis · 2024Article
- CT-based radiomics: predicting early outcomes after percutaneous transluminal renal angioplasty in patients with severe atherosclerotic renal artery stenosis.Visual computing for industry, biomedicine, and art · 2024Article
- Clinical Reproducibility of the Stone Volume Measurement: A "Kidney Stone Calculator" Study.Journal of clinical medicine · 2023Article
- Transforming urinary stone disease management by artificial intelligence-based methods: A comprehensive review.Asian journal of urology · 2023Review
- Detection of Gallbladder Disease Types Using Deep Learning: An Informative Medical Method.Diagnostics (Basel, Switzerland) · 2023Article
- Changes in CT-Based Morphological Features of the Kidney with Declining Glomerular Filtration Rate in Chronic Kidney Disease.Diagnostics (Basel, Switzerland) · 2023Article
- AI-Driven Robust Kidney and Renal Mass Segmentation and Classification on 3D CT Images.Bioengineering (Basel, Switzerland) · 2023Article
- A deep learning model for automated kidney calculi detection on non-contrast computed tomography scans in dogs.Frontiers in veterinary science · 2023Article
- Theranostic roles of machine learning in clinical management of kidney stone disease.Computational and structural biotechnology journal · 2023Review
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
Recent breakthroughs of deep learning algorithms in medical imaging, automated detection, and segmentation techniques for renal (kidney) in abdominal computed tomography (CT) images have been limited. Radiomics and machine learning analyses of renal diseases rely on the automatic segmentation of kidneys in CT images. Inspired by this, our primary aim is to utilize deep semantic segmentation learning models with a proposed training scheme to achieve precise and accurate segmentation outcomes. Moreover, this work aims to provide the community with an open-source, unenhanced abdominal CT dataset for training and testing the deep learning segmentation networks to segment kidneys and detect kidney stones. Five variations of deep segmentation networks are trained and tested both dependently (based on the proposed training scheme) and independently. Upon comparison, the models trained with the proposed training scheme enable the highly accurate 2D and 3D segmentation of kidneys and kidney stones. We believe this work is a fundamental step toward AI-driven diagnostic strategies, which can be an essential component of personalized patient care and improved decision-making in treating kidney diseases.
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