ArticleProceedings. IEEE International Symposium on Biomedical Imaging2018
AUTOMATIC RENAL SEGMENTATION IN DCE-MRI USING CONVOLUTIONAL NEURAL NETWORKS.
Article in Proceedings. IEEE International Symposium on Biomedical Imaging, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Comparison of dynamic contrast-enhanced MRI versus MAG-3 scintigraphy for differential renal function assessment in pediatric patients.Pediatric radiology · 2026Article
- Enhancing Medical Image Segmentation and Classification Using a Fuzzy-Driven Method.Sensors (Basel, Switzerland) · 2025Article
- CNN-Based Kidney Segmentation Using a Modified CLAHE Algorithm.Sensors (Basel, Switzerland) · 2024Article
- Feature Extraction Based on Local Histogram with Unequal Bins and a Recurrent Neural Network for the Diagnosis of Kidney Diseases from CT Images.Bioengineering (Basel, Switzerland) · 2024Article
- Kidney Segmentation from Dynamic Contrast-Enhanced Magnetic Resonance Imaging Integrating Deep Convolutional Neural Networks and Level Set Methods.Bioengineering (Basel, Switzerland) · 2023Article
- In vivo MRI is sensitive to remyelination in a nonhuman primate model of multiple sclerosis.eLife · 2023Article
- Deep-learning-based ensemble method for fully automated detection of renal masses on magnetic resonance images.Journal of medical imaging (Bellingham, Wash.) · 2023Article
- Validation of automatically measured T1 map cortico-medullary difference (ΔT1) for eGFR and fibrosis assessment in allograft kidneys.PloS one · 2023Article
- Prospective motion correction in kidney MRI using FID navigators.Magnetic resonance in medicine · 2023Article
- Variational Approach for Joint Kidney Segmentation and Registration from DCE-MRI Using Fuzzy Clustering with Shape Priors.Biomedicines · 2022Article
- Level-Set-Based Kidney Segmentation from DCE-MRI Using Fuzzy Clustering with Population-Based and Subject-Specific Shape Statistics.Bioengineering (Basel, Switzerland) · 2022Article
- Kidney segmentation from DCE-MRI converging level set methods, fuzzy clustering and Markov random field modeling.Scientific reports · 2022Article
- Transfer learning-based approach for automated kidney segmentation on multiparametric MRI sequences.Journal of medical imaging (Bellingham, Wash.) · 2022Article
- Quantitative renal magnetic resonance imaging: magnetic resonance urography.Pediatric radiology · 2022Review
- 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
- 3D Deep Learning for Anatomical Structure Segmentation in Multiple Imaging Modalities.Proceedings. IEEE International Symposium on Computer-Based Medical Systems · 2021Article
- Modeling dynamic radial contrast enhanced MRI with linear time invariant systems for motion correction in quantitative assessment of kidney function.Medical image analysis · 2021Article
- Automatic cerebellum anatomical parcellation using U-Net with locally constrained optimization.NeuroImage · 2020Article
- Bulk motion-compensated DCE-MRI for functional imaging of kidneys in newborns.Journal of magnetic resonance imaging : JMRI · 2020Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Kidney function evaluation using dynamic contrast-enhanced MRI (DCE-MRI) images could help in diagnosis and treatment of kidney diseases of children. Automatic segmentation of renal parenchyma is an important step in this process. In this paper, we propose a time and memory efficient fully automated segmentation method which achieves high segmentation accuracy with running time in the order of seconds in both normal kidneys and kidneys with hydronephrosis. The proposed method is based on a cascaded application of two 3D convolutional neural networks that employs spatial and temporal information at the same time in order to learn the tasks of localization and segmentation of kidneys, respectively. Segmentation performance is evaluated on both normal and abnormal kidneys with varying levels of hydronephrosis. We achieved a mean dice coefficient of 91.4 and 83.6 for normal and abnormal kidneys of pediatric patients, respectively.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.