ArticleProceedings of SPIE--the International Society for Optical Engineering2018
Multi-atlas segmentation of the hydrocephalus brain using an adaptive ventricle atlas.
Article in Proceedings of SPIE--the International Society for Optical Engineering, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed, 5 citations in OpenAlex.
- Investigation of probability maps in deep-learning-based brain ventricle parcellation.Proceedings of SPIE--the International Society for Optical Engineering · 2023Article
- Evaluating White Matter Lesion Segmentations with Refined Sørensen-Dice Analysis.Scientific reports · 2020Article
- Brain ventricle parcellation using a deep neural network: Application to patients with ventriculomegaly.NeuroImage. Clinical · 2019Article
- Comparing fully automated state-of-the-art cerebellum parcellation from magnetic resonance images.NeuroImage · 2018Article
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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 caused by disruption of the flow of cerebrospinal fluid (CSF). The dementia-like symptoms of NPH are often mistakenly attributed to Alzheimer's disease. However, if correctly diagnosed, NPH patients can potentially be treated and their symptoms reversed through surgery. Observing the dilated ventricles through magnetic resonance imaging (MRI) is one element in diagnosing NPH. Diagnostic accuracy therefore benefits from accurate, automatic parcellation of the ventricular system into its sub-compartments. We present an improvement to a whole brain segmentation approach designed for subjects with enlarged and deformed ventricles. Our method incorporates an adaptive ventricle atlas from an NPH-atlas-based segmentation as a prior and uses a more robust relaxation scheme for the multi-atlas label fusion approach that accurately labels the four sub-compartments of the ventricular system. We validated our method on NPH patients, demonstrating improvement over state-of-the-art segmentation techniques.
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Registered trials
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