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Titlebook: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries; Second International Alessandro Crimi,Bjoern Menze,Heinz Hand

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发表于 2025-3-21 17:57:52 | 显示全部楼层 |阅读模式
期刊全称Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries
期刊简称Second International
影响因子2023Alessandro Crimi,Bjoern Menze,Heinz Handels
视频video
发行地址Includes supplementary material:
学科分类Lecture Notes in Computer Science
图书封面Titlebook: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries; Second International Alessandro Crimi,Bjoern Menze,Heinz Hand
影响因子This book constitutes the thoroughly refereed post-workshop proceedings of the International Workshop on Brain Lesion, as well as the challenges on  Brain Tumor Segmentation (BRATS), Ischemic Stroke Lesion Image Segmentation (ISLES), and the Mild Traumatic Brain Injury Outcome Prediction (mTOP), held in Athens, October 17, 2016, in conjunction with the International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016. .The 26 papers presented in this volume were carefully reviewed.  They present the latest advances in segmentation, disease prognosis and other applications to the clinical context. .
Pindex Conference proceedings 2016
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Towards a Second Brain Images of Tumours for Evaluation (BITE2) Databasevalidate new registration, segmentation, and other image processing algorithms. In this work we present a collection of data from tumour patients acquired at the Montreal Neurological Institute and Hospital that will be released as a publicly available dataset to the image processing community. The
发表于 2025-3-22 04:09:14 | 显示全部楼层
Topological Measures of Connectomics for Low Grades Gliomacal disorders have been investigated from a network perspective. These include Alzheimer’s disease, autism spectrum disorder, stroke, and traumatic brain injury. So far, few studies have been conducted on glioma by using connectome techniques. A connectome-based approach might be useful in quantifyi
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An Online Platform for the Automatic Reporting of Multi-parametric Tissue Signatures: A Case Study ilesion presents a high degree of heterogeneity that requires being studied through a multiparametric combination of several imaging sequences. Nowadays few systems are available to perform a relevant multiparametric analysis of this tumour. In this work, we present the study of GBM by means of ., an
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A Fast Approach to Automatic Detection of Brain Lesionscient for localizing lesions and assisting clinicians in diagnosis. However, processing large MR volumes with three-dimensional (3D) templates is demanding in terms of computational resources, hence the importance of the reduction of computational complexity of template matching, particularly in sit
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Improving Boundary Classification for Brain Tumor Segmentation and Longitudinal Disease Progression cysts, enhancing patterns, edema and necrosis. In this paper, we propose a Deep Neural Network based architecture that does automatic segmentation of brain tumor, and focuses on improving accuracy at the edges of these different classes. We show that enhancing the loss function to give more weight
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CRF-Based Brain Tumor Segmentation: Alleviating the Shrinking Biasinking bias inherent to many grid-structured CRFs. We focus on illustrating the impact of alleviating the shrinking bias on the performance of CRF-based brain tumor segmentation. The proposed segmentation method is evaluated using data from the MICCAI BRATS 2013 & 2015 data sets (up to 110 patient c
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