Survey of automated multiple sclerosis lesion segmentation techniques on magnetic resonance imaging
A Danelakis, T Theoharis, DA Verganelakis - … Medical Imaging and …, 2018 - Elsevier
Multiple sclerosis (MS) is a chronic disease. It affects the central nervous system and its
clinical manifestation can variate. Magnetic Resonance Imaging (MRI) is often used to …
clinical manifestation can variate. Magnetic Resonance Imaging (MRI) is often used to …
Exploring uncertainty measures in deep networks for multiple sclerosis lesion detection and segmentation
Deep learning networks have recently been shown to outperform other segmentation
methods on various public, medical-image challenge datasets, particularly on metrics …
methods on various public, medical-image challenge datasets, particularly on metrics …
Multiple sclerosis lesion analysis in brain magnetic resonance images: techniques and clinical applications
Multiple sclerosis (MS) is a chronic inflammatory and degenerative disease of the central
nervous system, characterized by the appearance of focal lesions in the white and gray …
nervous system, characterized by the appearance of focal lesions in the white and gray …
Deep 3D convolutional encoder networks with shortcuts for multiscale feature integration applied to multiple sclerosis lesion segmentation
We propose a novel segmentation approach based on deep 3D convolutional encoder
networks with shortcut connections and apply it to the segmentation of multiple sclerosis …
networks with shortcut connections and apply it to the segmentation of multiple sclerosis …
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 …
Asymmetric loss functions and deep densely-connected networks for highly-imbalanced medical image segmentation: Application to multiple sclerosis lesion …
SR Hashemi, SSM Salehi, D Erdogmus… - IEEE …, 2018 - ieeexplore.ieee.org
Fully convolutional deep neural networks have been asserted to be fast and precise
frameworks with great potential in image segmentation. One of the major challenges in …
frameworks with great potential in image segmentation. One of the major challenges in …
Disentangling human error from ground truth in segmentation of medical images
Recent years have seen increasing use of supervised learning methods for segmentation
tasks. However, the predictive performance of these algorithms depends on the quality of …
tasks. However, the predictive performance of these algorithms depends on the quality of …
[HTML][HTML] Learning from multiple annotators for medical image segmentation
Supervised machine learning methods have been widely developed for segmentation tasks
in recent years. However, the quality of labels has high impact on the predictive performance …
in recent years. However, the quality of labels has high impact on the predictive performance …
GA-UNet: UNet-based framework for segmentation of 2D and 3D medical images applicable on heterogeneous datasets
Segmentation of biomedical images is the method of semiautomatic and automatic detection
of boundaries within 2D and 3D images. The major challenge of medical image …
of boundaries within 2D and 3D images. The major challenge of medical image …
Longitudinal multiple sclerosis lesion segmentation using multi-view convolutional neural networks
A Birenbaum, H Greenspan - Deep Learning and Data Labeling for …, 2016 - Springer
Abstract Automatic segmentation of Multiple Sclerosis (MS) lesions is a challenging task due
to their variability in shape, size, location and texture in Magnetic Resonance (MR) images …
to their variability in shape, size, location and texture in Magnetic Resonance (MR) images …