Información del autor
Autor Menze, Bjoern |
Documentos disponibles escritos por este autor (2)
Crear una solicitud de compra Refinar búsquedaBrainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries / Crimi, Alessandro ; Bakas, Spyridon ; Kuijf, Hugo ; Menze, Bjoern ; Reyes, Mauricio
TÃtulo : Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries : Third International Workshop, BrainLes 2017, Held in Conjunction with MICCAI 2017, Quebec City, QC, Canada, September 14, 2017, Revised Selected Papers Tipo de documento: documento electrónico Autores: Crimi, Alessandro, ; Bakas, Spyridon, ; Kuijf, Hugo, ; Menze, Bjoern, ; Reyes, Mauricio, Mención de edición: 1 ed. Editorial: [s.l.] : Springer Fecha de publicación: 2018 Número de páginas: XIII, 517 p. 233 ilustraciones ISBN/ISSN/DL: 978-3-319-75238-9 Nota general: Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos. Idioma : Inglés (eng) Palabras clave: Visión por computador Inteligencia artificial Informática Estadistica matematica Informática Médica Probabilidad y EstadÃstica en Informática Informática de la Salud Clasificación: 006.37 Resumen: Este libro constituye una selección revisada de artÃculos del Tercer Taller Internacional MICCAI sobre Lesiones Cerebrales, BrainLes 2017, asà como de los desafÃos de segmentación Internacional Multimodal Brain Tumor Segmentation, BraTS e White Matter Hyperintensities, WMH, que se llevaron a cabo conjuntamente en Medical Image Computing for Computer. Conferencia de Intervención Asistida, MICCAI, en la ciudad de Quebec, Canadá, en septiembre de 2017. Los 40 artÃculos presentados en este volumen fueron cuidadosamente revisados ​​y seleccionados entre 46 presentaciones. Se organizaron en secciones temáticas denominadas: análisis de imágenes de lesiones cerebrales; segmentación de imágenes de tumores cerebrales; y segmentación de imágenes de lesiones de accidente cerebrovascular isquémico. Nota de contenido: 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 SemanticSegmentation 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. Tipo de medio : Computadora Summary : 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. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...] Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries : Third International Workshop, BrainLes 2017, Held in Conjunction with MICCAI 2017, Quebec City, QC, Canada, September 14, 2017, Revised Selected Papers [documento electrónico] / Crimi, Alessandro, ; Bakas, Spyridon, ; Kuijf, Hugo, ; Menze, Bjoern, ; Reyes, Mauricio, . - 1 ed. . - [s.l.] : Springer, 2018 . - XIII, 517 p. 233 ilustraciones.
ISBN : 978-3-319-75238-9
Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos.
Idioma : Inglés (eng)
Palabras clave: Visión por computador Inteligencia artificial Informática Estadistica matematica Informática Médica Probabilidad y EstadÃstica en Informática Informática de la Salud Clasificación: 006.37 Resumen: Este libro constituye una selección revisada de artÃculos del Tercer Taller Internacional MICCAI sobre Lesiones Cerebrales, BrainLes 2017, asà como de los desafÃos de segmentación Internacional Multimodal Brain Tumor Segmentation, BraTS e White Matter Hyperintensities, WMH, que se llevaron a cabo conjuntamente en Medical Image Computing for Computer. Conferencia de Intervención Asistida, MICCAI, en la ciudad de Quebec, Canadá, en septiembre de 2017. Los 40 artÃculos presentados en este volumen fueron cuidadosamente revisados ​​y seleccionados entre 46 presentaciones. Se organizaron en secciones temáticas denominadas: análisis de imágenes de lesiones cerebrales; segmentación de imágenes de tumores cerebrales; y segmentación de imágenes de lesiones de accidente cerebrovascular isquémico. Nota de contenido: 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 SemanticSegmentation 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. Tipo de medio : Computadora Summary : 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. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...]
TÃtulo : Medical Computer Vision and Bayesian and Graphical Models for Biomedical Imaging : MICCAI 2016 International Workshops, MCV and BAMBI, Athens, Greece, October 21, 2016, Revised Selected Papers Tipo de documento: documento electrónico Autores: Müller, Henning, ; Kelm, B. Michael, ; Arbel, Tal, ; Cai, Weidong, ; Cardoso, M. Jorge, ; Langs, Georg, ; Menze, Bjoern, ; Metaxas, Dimitris, ; Montillo, Albert, ; Wells III, William M., ; Zhang, Shaoting, ; Chung, Albert C.S, ; Jenkinson, Mark, ; Ribbens, Annemie, Mención de edición: 1 ed. Editorial: [s.l.] : Springer Fecha de publicación: 2017 Número de páginas: XIII, 222 p. 75 ilustraciones ISBN/ISSN/DL: 978-3-319-61188-4 Nota general: Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos. Idioma : Inglés (eng) Palabras clave: Visión por computador Informática Médica Inteligencia artificial Informática Estadistica matematica Sistemas de reconocimiento de patrones Informática de la Salud Probabilidad y EstadÃstica en Informática Aplicaciones matemáticas en informática Reconocimiento de patrones automatizado Clasificación: 006.37 Resumen: Este libro constituye las actas posteriores al taller, exhaustivamente arbitradas, del Taller internacional sobre visión médica por computadora, MCV 2016, y del Taller internacional sobre modelos gráficos y bayesianos para imágenes biomédicas, BAMBI 2016, celebrado en Atenas, Grecia, en octubre de 2016. en conjunto con la 19.ª Conferencia Internacional sobre Computación de Imágenes Médicas e Intervención Asistida por Computadora, MICCAI 2016. Los 13 artÃculos presentados en el taller MCV y los 6 artÃculos presentados en el taller BAMBI fueron cuidadosamente revisados ​​y seleccionados entre numerosas presentaciones. El objetivo del taller MCV es explorar el uso de algoritmos de "grandes datos" para recopilar, organizar y aprender a partir de conjuntos de datos de imágenes médicas a gran escala y para la comprensión automática de imágenes médicas con fines generales. El taller BAMBI tiene como objetivo resaltar el potencial del uso de modelos gráficos de campo aleatorios o bayesianos para avanzar en la investigación en el análisis de imágenes biomédicas. Nota de contenido: Constructing Subject- and Disease-Specific Effect Maps: Application to Neurodegenerative Diseases -- BigBrain: Automated Cortical Parcellation and Comparison with Existing Brain Atlases -- LATEST: Local AdapTivE and Sequential Training for Tissue Segmentation of Isointense Infant Brain MR Images -- Landmark-based Alzheimer's Disease Diagnosis Using Longitudinal Structural MR Images -- Inferring Disease Status by non-Parametric Probabilistic Embedding -- A Lung Graph-Model for Pulmonary Hypertension and Pulmonary Embolism Detection on DECT Images -- Explaining Radiological Emphysema Subtypes with Unsupervised Texture Prototypes: MESA COPD Study -- Automatic Segmentation of Abdominal MRI Using Selective Sampling and Random Walker -- Gaze2Segment: A Pilot Study for Integrating Eye-Tracking Technology into Medical Image Segmentation -- Automatic Detection of Histological Artifacts in Mouse Brain Slice Images -- Lung Nodule Classification by Jointly Using Visual Descriptors and Deep Features -- Representation Learning for Cross-Modality Classification -- Guideline-based Machine Learning for Standard Plane Extraction in 3D Cardiac Ultrasound -- A Statistical Model for Simultaneous Template Estimation, Bias Correction, and Registration of 3D Brain Images -- Bayesian Multiview Manifold Learning Applied to Hippocampus Shape and Clinical Score Data -- Rigid Slice-To-Volume Medical Image Registration through Markov Random Fields -- Sparse Probabilistic Parallel Factor Analysis for the Modeling of PET and Task-fMRI data -- Non-local Graph-based Regularization for Deformable Image Registration -- Unsupervised Framework for Consistent Longitudinal MS Lesion Segmentation. . Tipo de medio : Computadora Summary : This book constitutes the thoroughly refereed post-workshop proceedings of the International Workshop on Medical Computer Vision, MCV 2016, and of the International Workshop on Bayesian and grAphical Models for Biomedical Imaging, BAMBI 2016, held in Athens, Greece, in October 2016, held in conjunction with the 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016. The 13 papers presented in MCV workshop and the 6 papers presented in BAMBI workshop were carefully reviewed and selected from numerous submissions. The goal of the MCV workshop is to explore the use of "big data" algorithms for harvesting, organizing and learning from large-scale medical imaging data sets and for general-purpose automatic understanding of medical images. The BAMBI workshop aims to highlight the potential of using Bayesian or random field graphical models for advancing research in biomedical image analysis. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...] Medical Computer Vision and Bayesian and Graphical Models for Biomedical Imaging : MICCAI 2016 International Workshops, MCV and BAMBI, Athens, Greece, October 21, 2016, Revised Selected Papers [documento electrónico] / Müller, Henning, ; Kelm, B. Michael, ; Arbel, Tal, ; Cai, Weidong, ; Cardoso, M. Jorge, ; Langs, Georg, ; Menze, Bjoern, ; Metaxas, Dimitris, ; Montillo, Albert, ; Wells III, William M., ; Zhang, Shaoting, ; Chung, Albert C.S, ; Jenkinson, Mark, ; Ribbens, Annemie, . - 1 ed. . - [s.l.] : Springer, 2017 . - XIII, 222 p. 75 ilustraciones.
ISBN : 978-3-319-61188-4
Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos.
Idioma : Inglés (eng)
Palabras clave: Visión por computador Informática Médica Inteligencia artificial Informática Estadistica matematica Sistemas de reconocimiento de patrones Informática de la Salud Probabilidad y EstadÃstica en Informática Aplicaciones matemáticas en informática Reconocimiento de patrones automatizado Clasificación: 006.37 Resumen: Este libro constituye las actas posteriores al taller, exhaustivamente arbitradas, del Taller internacional sobre visión médica por computadora, MCV 2016, y del Taller internacional sobre modelos gráficos y bayesianos para imágenes biomédicas, BAMBI 2016, celebrado en Atenas, Grecia, en octubre de 2016. en conjunto con la 19.ª Conferencia Internacional sobre Computación de Imágenes Médicas e Intervención Asistida por Computadora, MICCAI 2016. Los 13 artÃculos presentados en el taller MCV y los 6 artÃculos presentados en el taller BAMBI fueron cuidadosamente revisados ​​y seleccionados entre numerosas presentaciones. El objetivo del taller MCV es explorar el uso de algoritmos de "grandes datos" para recopilar, organizar y aprender a partir de conjuntos de datos de imágenes médicas a gran escala y para la comprensión automática de imágenes médicas con fines generales. El taller BAMBI tiene como objetivo resaltar el potencial del uso de modelos gráficos de campo aleatorios o bayesianos para avanzar en la investigación en el análisis de imágenes biomédicas. Nota de contenido: Constructing Subject- and Disease-Specific Effect Maps: Application to Neurodegenerative Diseases -- BigBrain: Automated Cortical Parcellation and Comparison with Existing Brain Atlases -- LATEST: Local AdapTivE and Sequential Training for Tissue Segmentation of Isointense Infant Brain MR Images -- Landmark-based Alzheimer's Disease Diagnosis Using Longitudinal Structural MR Images -- Inferring Disease Status by non-Parametric Probabilistic Embedding -- A Lung Graph-Model for Pulmonary Hypertension and Pulmonary Embolism Detection on DECT Images -- Explaining Radiological Emphysema Subtypes with Unsupervised Texture Prototypes: MESA COPD Study -- Automatic Segmentation of Abdominal MRI Using Selective Sampling and Random Walker -- Gaze2Segment: A Pilot Study for Integrating Eye-Tracking Technology into Medical Image Segmentation -- Automatic Detection of Histological Artifacts in Mouse Brain Slice Images -- Lung Nodule Classification by Jointly Using Visual Descriptors and Deep Features -- Representation Learning for Cross-Modality Classification -- Guideline-based Machine Learning for Standard Plane Extraction in 3D Cardiac Ultrasound -- A Statistical Model for Simultaneous Template Estimation, Bias Correction, and Registration of 3D Brain Images -- Bayesian Multiview Manifold Learning Applied to Hippocampus Shape and Clinical Score Data -- Rigid Slice-To-Volume Medical Image Registration through Markov Random Fields -- Sparse Probabilistic Parallel Factor Analysis for the Modeling of PET and Task-fMRI data -- Non-local Graph-based Regularization for Deformable Image Registration -- Unsupervised Framework for Consistent Longitudinal MS Lesion Segmentation. . Tipo de medio : Computadora Summary : This book constitutes the thoroughly refereed post-workshop proceedings of the International Workshop on Medical Computer Vision, MCV 2016, and of the International Workshop on Bayesian and grAphical Models for Biomedical Imaging, BAMBI 2016, held in Athens, Greece, in October 2016, held in conjunction with the 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016. The 13 papers presented in MCV workshop and the 6 papers presented in BAMBI workshop were carefully reviewed and selected from numerous submissions. The goal of the MCV workshop is to explore the use of "big data" algorithms for harvesting, organizing and learning from large-scale medical imaging data sets and for general-purpose automatic understanding of medical images. The BAMBI workshop aims to highlight the potential of using Bayesian or random field graphical models for advancing research in biomedical image analysis. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...]
