ArticleUnderstanding and interpreting machine learning in medical image computing applications : first international workshops, MLCN 2018, DLF 2018, and iMIMIC 2018, held in conjunction with MICCAI 2018, Granada, Spain, September 16-20, 2018 :...2018
Shortcomings of Ventricle Segmentation Using Deep Convolutional Networks.
Article in Understanding and interpreting machine learning in medical image computing applications : first international workshops, MLCN 2018, DLF 2018, and iMIMIC 2018, held in conjunction with MICCAI 2018, Granada, Spain, September 16-20, 2018 :..., 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed, 22 citations in OpenAlex.
- Deep Learning for Automated Ventricle and Periventricular Space Segmentation on CT and T1CE MRI in Neuro-Oncology Patients.Cancers · 2025Article
- A review of applications of automated ventricular parcellation from magnetic resonance imaging of the brain.Frontiers in neurology · 2025Review
- Comparing a pre-defined versus deep learning approach for extracting brain atrophy patterns to predict cognitive decline due to Alzheimer's disease in patients with mild cognitive symptoms.Alzheimer's research & therapy · 2024Article
- Auto encoder-based defense mechanism against popular adversarial attacks in deep learning.PloS one · 2024Article
- Comparing a pre-defined versus deep learning approach for extracting brain atrophy patterns to predict cognitive decline due to Alzheimer's disease in patients with mild cognitive symptoms.Research square · 2023Article
- ENRICHing medical imaging training sets enables more efficient machine learning.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- AUTOMATED VENTRICLE PARCELLATION AND EVAN'S RATIO COMPUTATION IN PRE- AND POST-SURGICAL VENTRICULOMEGALY.Proceedings. IEEE International Symposium on Biomedical Imaging · 2023Article
- Investigation of probability maps in deep-learning-based brain ventricle parcellation.Proceedings of SPIE--the International Society for Optical Engineering · 2023Article
- A joint ventricle and WMH segmentation from MRI for evaluation of healthy and pathological changes in the aging brain.PloS one · 2022Article
- Automated Segmentation and Connectivity Analysis for Normal Pressure Hydrocephalus.BME frontiers · 2022Article
- Applications of a deep learning method for anti-aliasing and super-resolution in MRI.Magnetic resonance imaging · 2019Article
- Brain ventricle parcellation using a deep neural network: Application to patients with ventriculomegaly.NeuroImage. Clinical · 2019Article
- Deep learning interpretability in neuroimaging: A comprehensive survey and methodological recommendations.Imaging neuroscience (Cambridge, Mass.)Article
- eXplainable artificial intelligence-Eval: A framework for comparative evaluation of explanation methods in healthcare.Digital healthArticle
- Evaluating the impact of MR image harmonization on thalamus deep network segmentation.Proceedings of SPIE--the International Society for Optical EngineeringArticle
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 2 countries.
Funding
Abstract
Normal Pressure Hydrocephalus (NPH) is a brain disorder that can present with ventriculomegaly and dementia-like symptoms, which often can be reversed through surgery. Having accurate segmentation of the ventricular system into its sub-compartments from magnetic resonance images (MRI) would be beneficial to better characterize the condition of NPH patients. Previous segmentation algorithms need long processing time and often fail to accurately segment severely enlarged ventricles in NPH patients. Recently, deep convolutional neural network (CNN) methods have been reported to have fast and accurate performance on medical image segmentation tasks. In this paper, we present a 3D U-net CNN-based network to segment the ventricular system in MRI. We trained three networks on different data sets and compared their performances. The networks trained on healthy controls (HC) failed in patients with NPH pathology, even in patients with normal appearing ventricles. The network trained on images from HC and NPH patients provided superior performance against state-of-the-art methods when evaluated on images from both data sets.
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Registered trials
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.