Crimi Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries 9783319752372

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Автор: Crimi
Название: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries
ISBN: 9783319752372
Издательство: Springer
Классификация:

Неврология и клиническая нейрофизиология

Медицинская визуализация

ISBN-10: 3319752375
Обложка/Формат: Paperback
Страницы: 517
Вес: 0.81 кг.
Дата издания: 2018
Серия: Image Processing, Computer Vision, Pattern Recognition, and Graphics
Язык: English
Издание: 1st ed. 2018
Иллюстрации: 233 illustrations, black and white; xiii, 517 p. 233 illus.
Размер: 234 x 156 x 27
Читательская аудитория: Professional & vocational
Основная тема: Image Processing and Computer Vision
Подзаголовок: Third International Workshop, BrainLes 2017, Held in Conjunction with MICCAI 2017, Quebec City, QC, Canada, September 14, 2017, Revised Selected Papers
Ссылка на Издательство: Link
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Поставляется из: Германии
Описание:

Invited Talks.- Dice overlap measures for objects of unknown number: Application to lesion segmentation.- Lesion Detection, Segmentation and Prediction in Multiple Sclerosis Clinical Trials.- Brain Lesion Image Analysis.- Automated Segmentation of Multiple Sclerosis Lesions using Multi-Dimensional Gated Recurrent Units.- Joint Intensity Fusion Image Synthesis Applied to Multiple Sclerosis Lesion Segmentation.- MARCEL (inter-Modality Ane Registration with CorELation ratio): An Application for Brain Shift Correction in Ultrasound-Guided Brain Tumor Resection.- Generalised Wasserstein Dice Score for Imbalanced Multi-class Segmentation using Holistic Convolutional Networks.- Overall Survival Time Prediction for High Grade Gliomas based on Sparse Representation Framework.- Traumatic Brain Lesion Quantication based on Mean Diusivity Changes.- Pairwise, Ordinal Outlier Detection of Traumatic Brain Injuries.- Sub-Acute & Chronic Ischemic Stroke Lesion MRI Segmentation.- Brain Tumor Segmentation Using an Adversarial Network.- Brain Cancer Imaging Phenomics Toolkit (brain-CaPTk): An Interactive Platform for Quantitative Analysis of Glioblastoma.- Brain Tumor Image Segmentation.- Deep Learning based Multimodal Brain Tumor Diagnosis.- Multimodal Brain Tumor Segmentation using Ensemble of Forest Method.- Pooling-free fully convolutional networks with dense skip connections for semantic segmentation, with application to brain tumor segmentation.- Automatic Brain Tumor Segmentation using Cascaded Anisotropic Convolutional Neural Networks.- 3D Brain Tumor Segmentation through Integrating Multiple 2D FCNNs.- MRI Brain Tumor Segmentation and Patient Survival Prediction using Random Forests and Fully Convolutional Networks.- Automatic Segmentation and Overall Survival Prediction in Gliomas using Fully Convolutional Neural Network and Texture Analysis.- Multimodal Brain Tumor Segmentation Using 3D Convolutional Networks.- A Conditional Adversarial Network for Semantic Segmentation of Brain Tumor.- Dilated Convolutions for Brain Tumor Segmentation in MRI Scans.- Residual Encoder and Convolutional Decoder Neural Network for Glioma Segmentation.- TPCNN: Two-phase Patch-based Convolutional Neural Network for Automatic Brain Tumor Segmentation and Survival Prediction.- Brain Tumor Segmentation and Radiomics Survival Prediction: Contribution to the BRATS 2017 Challenge.- Multi-modal PixelNet for Brain Tumor Segmentation.- Brain Tumor Segmentation using Dense Fully Convolutional Neural Network.- Brain Tumor Segmentation in MRI Scans using Deeply-Supervised Neural Networks.- Brain Tumor Segmentation and Parsing on MRIs using Multiresolution Neural Networks.- Brain Tumor Segmentation using Deep Fully Convolutional Neural Networks.- Glioblastoma and Survival Prediction.- MRI Augmentation via Elastic Registration for Brain Lesions Segmentation.- Cascaded V-Net using ROI masks for brain tumor segmentation.- Brain Tumor Segmentation using a 3D FCN with Multi-Scale Loss.- Brain tumor segmentation using a multi-path CNN based method.- 3D Deep Neural Network-Based Brain Tumor Segmentation Using Multimodality Magnetic Resonance Sequences.- Automated Brain Tumor Segmentation on Magnetic Resonance Images (MRIs) and Patient Overall Survival Prediction using Support Vector Machines.- Ensembles of Multiple Models and Architectures for Robust Brain Tumour Segmentation.- Tumor segmentation from multimodal MRI using random forest with superpixel and tensor based feature extraction.- Towards Uncertainty-assisted Brain Tumor Segmentation and Survival Prediction.- Ischemic Stroke Lesion Image Segmentation.- WMH Segmentation Challenge: a Texture-based Classication Approach.- White Matter Hyperintensities Segmentation In a Few Seconds Using Fully Convolutional Network and Transfer Learning.

Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, Crimi