Hippocampal shape analysis in the Laplace Beltrami feature space for temporal lobe epilepsy diagnosis and lateralization

Shishegar, Rosita; Gandomkar, Ziba; Soltaman-Zadeh, Hamid; Moghadasi, Seyed Reza

doi:10.1109/isbi.2012.6235506

Cited by 6 publications

(4 citation statements)

References 10 publications

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“…Our preliminary results show that using shape and size features for hippocampal asymmetry detection could be helpful in diagnosis of the TLE disease in MRI with a comparable accuracy to the previous work 6,7,8,9 . Our algorithm could detect all the healthy subjects (specificity of 100%).…”

Section: Resultssupporting

confidence: 54%

“…Our algorithm could detect all the healthy subjects (specificity of 100%). Since the dataset we used in our study is formed based on a subset of the dataset used in 8,9 , not the whole set, it is challenging to reach a fair comparison between their results and ours. As an advantage, the complexity of our algorithm is less than the other presented algorithms that yield a real-time (i.e.…”

Section: Resultsmentioning

confidence: 99%

“…The deformation of hippocampus has led the research groups to conduct the shape of the hippocampus for diagnosis purposes. Kodipaka 9 . This method proposed a feature vector that described size and shape based on Laplace Beltrami eigenvalues and eigen-functions.…”

Section: Introductionmentioning

confidence: 99%

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Hippocampus shape analysis for temporal lobe epilepsy detection in magnetic resonance imaging

Kohan

Azmi

2016

SPIE Proceedings

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There are evidences in the literature that Temporal Lobe Epilepsy (TLE) causes some lateralized atrophy and deformation on hippocampus and other substructures of the brain. Magnetic Resonance Imaging (MRI), due to high-contrast soft tissue imaging, is one of the most popular imaging modalities being used in TLE diagnosis and treatment procedures. Using an algorithm to help clinicians for better and more effective shape deformations analysis could improve the diagnosis and treatment of the disease. In this project our purpose is to design, implement and test a classification algorithm for MRIs based on hippocampal asymmetry detection using shapeand size-based features. Our method consisted of two main parts; (1) shape feature extraction, and (2) image classification. We tested 11 different shape and size features and selected four of them that detect the asymmetry in hippocampus significantly in a randomly selected subset of the dataset. Then, we employed a support vector machine (SVM) classifier to classify the remaining images of the dataset to normal and epileptic images using our selected features. The dataset contains 25 patient images in which 12 cases were used as a training set and the rest 13 cases for testing the performance of classifier. We measured accuracy, specificity and sensitivity of, respectively, 76%, 100%, and 70% for our algorithm. The preliminary results show that using shape and size features for detecting hippocampal asymmetry could be helpful in TLE diagnosis in MRI.

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Section: Resultssupporting

confidence: 54%

Section: Resultsmentioning

confidence: 99%

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Hippocampus shape analysis for temporal lobe epilepsy detection in magnetic resonance imaging

Kohan

Azmi

2016

SPIE Proceedings

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“…Advances in hardware and sequence technology, which enable submillimetric resolution and an improved signal-to-noise ratio, have facilitated accurate visualization of hippocampal subfields or subregions, including the dentate gyrus, subiculum, and the cornu ammonis (CA1-4) regions [32]. Increasing demand to study large patient cohorts has motivated the shift from manual toward automated segmentation, setting the basis for largescale clinical use [33,34]. Several methods with fast inference times have been developed for MRI-based subfield segmentation [34][35][36][37][38][39], providing overlap indices of >80% with manual labels.…”

Section: Lesion Detectionmentioning

confidence: 99%

The Role of MRI in the Treatment of Drug-Resistant Focal Epilepsy

Bernasconi

2022

Eur Neurol

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Background: Epilepsy is a prevalent chronic condition affecting about 50 million people worldwide. A third of patients with focal epilepsy suffer from seizures unresponsive to medication. Uncontrolled seizures damage the brain, are associated with cognitive decline, and have negative impact on well-being. For these patients, the surgical resection of the brain region that gives rise to seizures is the most effective treatment. Summary: Magnetic resonance imaging (MRI) plays a central role in detecting epileptogenic brain lesions. In this review, we critically discuss advances in neuroimaging acquisition, analytical post-acquisition techniques, and machine leaning methods for the detection of epileptogenic lesions, prediction of clinical outcomes, and identification of disease subtypes. Key Message: MRI is a mandatory investigation for diagnosis and treatment of epilepsy, particularly when surgery is being considered. Continuous progress in imaging techniques, combined with machine learning, will continue to push the boundaries of lesion visibility and provide increasingly precise predictors of clinical outcomes. Current efforts aiming at strengthening the competences of epileptologists in neuroimaging will ultimately reduce the need for invasive diagnostics.

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Statistical shape analysis of hippocampus in temporal lobe epilepsy based on Laplace-Beltrami eigenfunction level sets

Shishegar

Soltanian‐Zadeh

Tehranipour

2012

The 16th CSI International Symposium on Artificial Intelligence and Signal Processing (AISP 2012)

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Hippocampal shape analysis in the Laplace Beltrami feature space for temporal lobe epilepsy diagnosis and lateralization

Cited by 6 publications

References 10 publications

Hippocampus shape analysis for temporal lobe epilepsy detection in magnetic resonance imaging

Hippocampus shape analysis for temporal lobe epilepsy detection in magnetic resonance imaging

The Role of MRI in the Treatment of Drug-Resistant Focal Epilepsy

Statistical shape analysis of hippocampus in temporal lobe epilepsy based on Laplace-Beltrami eigenfunction level sets

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