10 Result(s)
within
Chapter
Diana M. Sima
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Chapter and Conference Paper
Correction to: Knowledge-Guided Segmentation of Isointense Infant Brain
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Chapter and Conference Paper
Knowledge-Guided Segmentation of Isointense Infant Brain
Tissue segmentation of infants could lead to early diagnosis of neurological disorders, potentially enabling early interventions. However, the challenge of tissue quantification is increased due to the very dy...
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Chapter and Conference Paper
Unsupervised 3D Brain Anomaly Detection
Anomaly detection (AD) is the identification of data samples that do not fit a learned data distribution. As such, AD systems can help physicians to determine the presence, severity, and extension of a patholo...
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Chapter and Conference Paper
Improved Inter-scanner MS Lesion Segmentation by Adversarial Training on Longitudinal Data
The evaluation of white matter lesion progression is an important biomarker in the follow-up of MS patients and plays a crucial role when deciding the course of treatment. Current automated lesion segmentation...
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Chapter and Conference Paper
Differentiable Deconvolution for Improved Stroke Perfusion Analysis
Perfusion imaging is the current gold standard for acute ischemic stroke analysis. It allows quantification of the salvageable and non-salvageable tissue regions (penumbra and core areas respectively). In clin...
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Chapter and Conference Paper
Relevance Vector Machines for Harmonization of MRI Brain Volumes Using Image Descriptors
With the increased need for multi-center magnetic resonance imaging studies, problems arise related to differences in hardware and software between centers. Namely, current algorithms for brain volume quantifi...
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Chapter and Conference Paper
A Comparison of Machine Learning Approaches for Classifying Multiple Sclerosis Courses Using MRSI and Brain Segmentations
The objective of this paper is to classify Multiple Sclerosis courses using features extracted from Magnetic Resonance Spectroscopic Imaging (MRSI) combined with brain tissue segmentations of gray matter, whit...
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Chapter and Conference Paper
Unsupervised Framework for Consistent Longitudinal MS Lesion Segmentation
Quantification of white matter lesion changes on brain magnetic resonance (MR) images is of major importance for the follow-up of patients with Multiple Sclerosis (MS). Many automated segmentation methods have...
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Chapter
Use Case I: Imaging Biomarkers in Neurological Disease. Focus on Multiple Sclerosis
Imaging is widely used for diagnosis and monitoring of neurological diseases. CT scans are routinely acquired in emergency units in patients with traumatic injuries or stroke. PET imaging has gained a strong f...
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Chapter and Conference Paper
Adaptive Alternating Minimization for Fitting Magnetic Resonance Spectroscopic Imaging Signals
In this paper we discuss the problem of modeling Magnetic Resonance Spectroscopic Imaging (MRSI) signals, in the aim of estimating metabolite concentration over a region of the brain. To this end, we formulate...
