Segmentation of ventricles in Alzheimer MR images using Tukey's biweight edge indicator and level set method

Anandh, K. R.; Sujatha, C. M.; Swaminathan, Ramakrishnan

doi:10.1109/nebec.2014.6972712

Cited by 3 publications

(2 citation statements)

References 7 publications

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“…The left temporal lobe is involved in language and AD (Cretin et al, 2015; Flick et al, 2018; Trimmel et al, 2018), and the right temporal lobe atrophy is involved in severe impairment in emotion recognition (Everhart et al, 2015) and causes frontotemporal dementia (Gliebus, 2014), with the brain ventricles often affected AD (Ferrarini et al, 2006). Ventricle enlargement is a useful structural biomarker for the diagnosis of AD (Anandh et al, 2014).…”

Section: Resultsmentioning

confidence: 99%

Deep Feature Selection and Causal Analysis of Alzheimer’s Disease

Liu

et al. 2019

Front. Neurosci.

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Deep convolutional neural networks (DCNNs) have achieved great success for image classification in medical research. Deep learning with brain imaging is the imaging method of choice for the diagnosis and prediction of Alzheimer’s disease (AD). However, it is also well known that DCNNs are “black boxes” owing to their low interpretability to humans. The lack of transparency of deep learning compromises its application to the prediction and mechanism investigation in AD. To overcome this limitation, we develop a novel general framework that integrates deep leaning, feature selection, causal inference, and genetic-imaging data analysis for predicting and understanding AD. The proposed algorithm not only improves the prediction accuracy but also identifies the brain regions underlying the development of AD and causal paths from genetic variants to AD via image mediation. The proposed algorithm is applied to the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset with diffusion tensor imaging (DTI) in 151 subjects (51 AD and 100 non-AD) who were measured at four time points of baseline, 6 months, 12 months, and 24 months. The algorithm identified brain regions underlying AD consisting of the temporal lobes (including the hippocampus) and the ventricular system.

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Section: Resultsmentioning

confidence: 99%

Deep Feature Selection and Causal Analysis of Alzheimer’s Disease

Liu

et al. 2019

Front. Neurosci.

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“…7 Among computer-based models, level set (LS) algorithms 8 have become widely used to determine lesion, organ, and bone volumes in recent years. [9][10][11] LS algorithms grow a "seed" placed within the ROI. Several parameters, including the rate of growth and curvature of the segmented surface, are controlled by local image properties and can also be adjusted by the reader.…”

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confidence: 99%