Automated System for Multiple Sclerosis Lesion Segmentation in 3D Brain MRI PDF Download

Author: 劉彥良
Publisher:
ISBN:
Category :
Languages : en
Pages : 91

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Author: M.A. Sahraian
Publisher: Springer Science & Business Media
ISBN: 3540713719
Category : Medical
Languages : en
Pages : 184

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Book Description
MRI has become the main paraclinical test in the diagnosis and management of multiple sclerosis. We have demonstrated more than 400 pictures of different typical and atypical MS lesions in this atlas. Each image has a teaching point. New diagnostic criteria and differential diagnosis have been discussed.

Author: Olivier Commowick
Publisher: Frontiers Media SA
ISBN: 2832520375
Category : Science
Languages : en
Pages : 132

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Author: Nassir Navab
Publisher: Springer
ISBN: 3319245740
Category : Computers
Languages : en
Pages : 801

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Book Description
The three-volume set LNCS 9349, 9350, and 9351 constitutes the refereed proceedings of the 18th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2015, held in Munich, Germany, in October 2015. Based on rigorous peer reviews, the program committee carefully selected 263 revised papers from 810 submissions for presentation in three volumes. The papers have been organized in the following topical sections: quantitative image analysis I: segmentation and measurement; computer-aided diagnosis: machine learning; computer-aided diagnosis: automation; quantitative image analysis II: classification, detection, features, and morphology; advanced MRI: diffusion, fMRI, DCE; quantitative image analysis III: motion, deformation, development and degeneration; quantitative image analysis IV: microscopy, fluorescence and histological imagery; registration: method and advanced applications; reconstruction, image formation, advanced acquisition - computational imaging; modelling and simulation for diagnosis and interventional planning; computer-assisted and image-guided interventions.

Author: Bennett Landman
Publisher:
ISBN: 9781479126187
Category :
Languages : en
Pages : 164

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Book Description
Characterization of anatomical structure through segmentation has become essential for morphological assessment and localizing quantitative measures. Segmentation through registration and atlas label transfer has proven to be a flexible and fruitful approach as efficient, non-rigid image registration methods have become prevalent. Label transfer segmentation using multiple atlases has helped to bring statistical fusion, shape modeling, and meta-analysis techniques to the forefront of segmentation research. Numerous creative approaches have proposed to use atlas information to apply labels to brain anatomy. However, it is difficult to evaluate the relative advantages and limitations of these methods as they have been applied on very different datasets. This workshop provides a snapshot of the current progress in the field through extended discussions and provides researchers an opportunity to characterize their methods on standardized data in a grand challenge.

Author: Marc Niethammer
Publisher: Springer
ISBN: 3319590502
Category : Computers
Languages : en
Pages : 691

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Book Description
This book constitutes the proceedings of the 25th International Conference on Information Processing in Medical Imaging, IPMI 2017, held at the Appalachian State University, Boon, NC, USA, in June 2017. The 53 full papers presented in this volume were carefully reviewed and selected from 147 submissions. They were organized in topical sections named: analysis on manifolds; shape analysis; disease diagnosis/progression; brain networks an connectivity; diffusion imaging; quantitative imaging; imaging genomics; image registration; segmentation; general image analysis.

Author: Bassem A Abdullah
Publisher:
ISBN:
Category :
Languages : en
Pages :

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Book Description
Multiple Sclerosis (MS) is an autoimmune disease of central nervous system. It may result in a variety of symptoms from blurred vision to severe muscle weakness and degradation, depending on the affected regions in brain. To better understand this disease and to quantify its evolution, magnetic resonance imaging (MRI) is increasingly used nowadays. Manual delineation of MS lesions in MR images by human expert is time-consuming, subjective, and prone to inter-expert variability. Therefore, automatic segmentation is needed as an alternative to manual segmentation. However, the progression of the MS lesions shows considerable variability and MS lesions present temporal changes in shape, location, and area between patients and even for the same patient, which renders the automatic segmentation of MS lesions a challenging problem. In this dissertation, a set of segmentation pipelines are proposed for automatic segmentation of multiple sclerosis (MS) lesions from brain magnetic resonance imaging (MRI) data. These techniques use a trained support vector machine (SVM) to discriminate between the blocks in regions of MS lesions and the blocks in non-MS lesion regions mainly based on the textural features with aid of the other features. The main contribution of this set of frameworks is the use of textural features to detect MS lesions in a fully automated approach that does not rely on manually delineating the MS lesions. In addition, the technique introduces the concept of the multi-sectional views segmentation to produce verified segmentation. The multi-sectional views pipeline is customized to provide better segmentation performance and to benefit from the properties and the nature of MS lesion in MRI. These customization and enhancement leads to development of the customized MV-T-SVM. The MRI datasets that were used in the evaluation of the proposed pipelines are simulated MRI datasets (3 subjects) generated using the McGill University BrainWeb MRI Simulator, real datasets (51 subjects) publicly available at the workshop of MS Lesion Segmentation Challenge 2008 and real MRI datasets (10 subjects) for MS subjects acquired at the University of Miami. The obtained results indicate that the proposed method would be viable for use in clinical practice for the detection of MS lesions in MRI.

Author: Alessandro Crimi
Publisher: Springer
ISBN: 3319752383
Category : Computers
Languages : en
Pages : 524

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Book Description
This book constitutes revised selected papers from the Third International MICCAI Brainlesion Workshop, BrainLes 2017, as well as the International Multimodal Brain Tumor Segmentation, BraTS, and White Matter Hyperintensities, WMH, segmentation challenges, which were held jointly at the Medical Image computing for Computer Assisted Intervention Conference, MICCAI, in Quebec City, Canada, in September 2017. The 40 papers presented in this volume were carefully reviewed and selected from 46 submissions. They were organized in topical sections named: brain lesion image analysis; brain tumor image segmentation; and ischemic stroke lesion image segmentation.

Author: Mostafa Salem
Publisher:
ISBN:
Category :
Languages : en
Pages : 143

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Book Description
This thesis is focused on developing novel and fully automated methods for the detection of new multiple sclerosis (MS) lesions inlongitudinal brain magnetic resonance imaging (MRI). First, we proposed a fully automated logistic regression-based framework forthe detection and segmentation of new T2-w lesions. The framework was based on intensity subtraction and deformation field (DF).Second, we proposed a fully convolutional neural network (FCNN) approach to detect new T2-w lesions in longitudinal brain MRimages. The model was trained end-to-end and simultaneously learned both the DFs and the new T2-w lesions. Finally, weproposed a deep learning-based approach for MS lesion synthesis to improve the lesion detection and segmentation performancein both cross-sectional and longitudinal analysis.

Author: Micheline Kamber
Publisher:
ISBN:
Category : Brain
Languages : en
Pages : 0

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Book Description
Magnetic resonance (MR) imaging is a medical technique which permits the visualization of a variety of tumors, lesions, and abnormalities present within the soft biological tissues of the body. Segmentation of medical image data is the process of assigning anatomically-meaningful labels to each component of the image. This thesis describes the development of a tool for the segmentation of MR images of the head. In particular, the tool is designed for the detection of multiple sclerosis lesions of the brain. The design was based on two objectives: (i) to evaluate the effectiveness of incorporating a priori knowledge of brain anatomy in the classification process, and (ii) to compare the statistical and symbolic approaches to machine learning. Knowledge of neuroanatomy is represented in the form of a tissue probability model. The model was constructed to provide a priori probabilities of brain tissue distribution per unit voxel in a standardized 3D 'brain space'. Use of the model to detect multiple sclerosis lesions reduces the number of false positive lesions by 50%. The performance of the statistical minimum distance and Bayesian classifiers was compared to that of a symbolic decision tree learning algorithm. A version of this algorithm for the handling of noisy data was included in the comparative study. Each classifier performed at about the same level of accuracy. The statistical classifiers were the fastest in training, yet were the slowest in recall.