Advances in auto-segmentation
Manual image segmentation is a time-consuming task routinely performed in radiotherapy to
identify each patient's targets and anatomical structures. The efficacy and safety of the …
identify each patient's targets and anatomical structures. The efficacy and safety of the …
The multimodal brain tumor image segmentation benchmark (BRATS)
In this paper we report the set-up and results of the Multimodal Brain Tumor Image
Segmentation Benchmark (BRATS) organized in conjunction with the MICCAI 2012 and …
Segmentation Benchmark (BRATS) organized in conjunction with the MICCAI 2012 and …
SegAN: Adversarial Network with Multi-scale L1 Loss for Medical Image Segmentation
Abstract Inspired by classic Generative Adversarial Networks (GANs), we propose a novel
end-to-end adversarial neural network, called SegAN, for the task of medical image …
end-to-end adversarial neural network, called SegAN, for the task of medical image …
A probabilistic atlas of the human thalamic nuclei combining ex vivo MRI and histology
The human thalamus is a brain structure that comprises numerous, highly specific nuclei.
Since these nuclei are known to have different functions and to be connected to different …
Since these nuclei are known to have different functions and to be connected to different …
A combined deep-learning and deformable-model approach to fully automatic segmentation of the left ventricle in cardiac MRI
Segmentation of the left ventricle (LV) from cardiac magnetic resonance imaging (MRI)
datasets is an essential step for calculation of clinical indices such as ventricular volume and …
datasets is an essential step for calculation of clinical indices such as ventricular volume and …
Improving automated multiple sclerosis lesion segmentation with a cascaded 3D convolutional neural network approach
In this paper, we present a novel automated method for White Matter (WM) lesion
segmentation of Multiple Sclerosis (MS) patient images. Our approach is based on a …
segmentation of Multiple Sclerosis (MS) patient images. Our approach is based on a …
Fully automatic brain tumor segmentation using end-to-end incremental deep neural networks in MRI images
Abstract Background and Objective: Nowadays, getting an efficient Brain Tumor
Segmentation in Multi-Sequence MR images as soon as possible, gives an early clinical …
Segmentation in Multi-Sequence MR images as soon as possible, gives an early clinical …
Automated brain tumour detection and segmentation using superpixel-based extremely randomized trees in FLAIR MRI
Purpose We propose a fully automated method for detection and segmentation of the
abnormal tissue associated with brain tumour (tumour core and oedema) from Fluid …
abnormal tissue associated with brain tumour (tumour core and oedema) from Fluid …
Optical coherence tomography reflects brain atrophy in multiple sclerosis: a four‐year study
Objective The aim of this work was to determine whether atrophy of specific retinal layers
and brain substructures are associated over time, in order to further validate the utility of …
and brain substructures are associated over time, in order to further validate the utility of …
Decision forests: A unified framework for classification, regression, density estimation, manifold learning and semi-supervised learning
Decision Forests Page 1 Decision Forests A Unified Framework for Classification,
Regression, Density Estimation, Manifold Learning and Semi-Supervised Learning Full text …
Regression, Density Estimation, Manifold Learning and Semi-Supervised Learning Full text …