ArticleMagnetic resonance in medicine2018
Automatic renal segmentation for MR urography using 3D-GrabCut and random forests.
Article in Magnetic resonance in medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it, 32 citations in OpenAlex.
- Automated Segmentation of Tissues Using CT and MRI: A Systematic Review.Academic radiology · 2019Pooled it
- Applications of Machine Learning for Early Diagnosis and Prognosis of Chronic Kidney Disease: Current Evidence.Diagnostics (Basel, Switzerland) · 2026Review
- Pediatric magnetic resonance urography from patient preparation to post-processing: basics and controversies.Pediatric radiology · 2026Review
- Quantifying ultrasound echogenicity difference for accurate chronic kidney disease diagnosis.Journal of nephrology · 2025Article
- Renal Pathology Images Segmentation Based on Improved Cuckoo Search with Diffusion Mechanism and Adaptive Beta-Hill Climbing.Journal of bionic engineering · 2023Article
- Variational Approach for Joint Kidney Segmentation and Registration from DCE-MRI Using Fuzzy Clustering with Shape Priors.Biomedicines · 2022Article
- Deep Segmentation Networks for Segmenting Kidneys and Detecting Kidney Stones in Unenhanced Abdominal CT Images.Diagnostics (Basel, Switzerland) · 2022Article
- Quantitative renal magnetic resonance imaging: magnetic resonance urography.Pediatric radiology · 2022Review
- Workflow for automatic renal perfusion quantification using ASL-MRI and machine learning.Magnetic resonance in medicine · 2022Article
- Improving Automatic Renal Segmentation in Clinically Normal and Abnormal Paediatric DCE-MRI via Contrast Maximisation and Convolutional Networks for Computing Markers of Kidney Function.Sensors (Basel, Switzerland) · 2021Article
- Healthy Kidney Segmentation in the Dce-Mr Images Using a Convolutional Neural Network and Temporal Signal Characteristics.Sensors (Basel, Switzerland) · 2021Article
- Consensus-based technical recommendations for clinical translation of renal BOLD MRI.Magma (New York, N.Y.) · 2020Article
- AUTOMATIC RENAL SEGMENTATION IN DCE-MRI USING CONVOLUTIONAL NEURAL NETWORKS.Proceedings. IEEE International Symposium on Biomedical Imaging · 2018Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
purposeTo introduce and evaluate a fully automated renal segmentation technique for glomerular filtration rate (GFR) assessment in children.
methodsAn image segmentation method based on iterative graph cuts (GrabCut) was modified to work on time-resolved 3D dynamic contrast-enhanced MRI data sets. A random forest classifier was trained to further segment the renal tissue into cortex, medulla, and the collecting system. The algorithm was tested on 26 subjects and the segmentation results were compared to the manually drawn segmentation maps using the F1-score metric. A two-compartment model was used to estimate the GFR of each subject using both automatically and manually generated segmentation maps.
resultsSegmentation maps generated automatically showed high similarity to the manually drawn maps for the whole-kidney (F1 = 0.93) and renal cortex (F1 = 0.86). GFR estimations using whole-kidney segmentation maps from the automatic method were highly correlated (Spearman's ρ = 0.99) to the GFR values obtained from manual maps. The mean GFR estimation error of the automatic method was 2.98 ± 0.66% with an average segmentation time of 45 s per patient.
conclusionThe automatic segmentation method performs as well as the manual segmentation for GFR estimation and reduces the segmentation time from several hours to 45 s. Magn Reson Med 79:1696-1707, 2018. © 2017 International Society for Magnetic Resonance in Medicine.
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