Multi-atlas segmentation of biomedical images: a survey
JE Iglesias, MR Sabuncu - Medical image analysis, 2015 - Elsevier
Abstract Multi-atlas segmentation (MAS), first introduced and popularized by the pioneering
work of Rohlfing, et al.(2004), Klein, et al.(2005), and Heckemann, et al.(2006), is becoming …
work of Rohlfing, et al.(2004), Klein, et al.(2005), and Heckemann, et al.(2006), is becoming …
Benchmark on automatic six-month-old infant brain segmentation algorithms: the iSeg-2017 challenge
Accurate segmentation of infant brain magnetic resonance (MR) images into white matter
(WM), gray matter (GM), and cerebrospinal fluid is an indispensable foundation for early …
(WM), gray matter (GM), and cerebrospinal fluid is an indispensable foundation for early …
A deep learning model for early prediction of Alzheimer's disease dementia based on hippocampal magnetic resonance imaging data
Introduction It is challenging at baseline to predict when and which individuals who meet
criteria for mild cognitive impairment (MCI) will ultimately progress to Alzheimer's disease …
criteria for mild cognitive impairment (MCI) will ultimately progress to Alzheimer's disease …
Non-rigid image registration using self-supervised fully convolutional networks without training data
A novel non-rigid image registration algorithm is built upon fully convolutional networks
(FCNs) to optimize and learn spatial transformations between pairs of images to be …
(FCNs) to optimize and learn spatial transformations between pairs of images to be …
Automated segmentation of tissues using CT and MRI: a systematic review
Rationale and Objectives The automated segmentation of organs and tissues throughout the
body using computed tomography and magnetic resonance imaging has been rapidly …
body using computed tomography and magnetic resonance imaging has been rapidly …
Analysis of brain sub regions using optimization techniques and deep learning method in Alzheimer disease
D Chitradevi, S Prabha - Applied Soft Computing, 2020 - Elsevier
Automatic segmentation of brain sub regions such as White Matter (GM), Corpus Callosum
(CC), Grey Matter (WM) and Hippocampus (HC) is a challenging task due to the variations in …
(CC), Grey Matter (WM) and Hippocampus (HC) is a challenging task due to the variations in …
Independent and reproducible hippocampal radiomic biomarkers for multisite Alzheimer's disease: diagnosis, longitudinal progress and biological basis
Hippocampal morphological change is one of the main hallmarks of Alzheimer's disease
(AD). However, whether hippocampal radiomic features are robust as predictors of …
(AD). However, whether hippocampal radiomic features are robust as predictors of …
Multi-atlas segmentation with augmented features for cardiac MR images
Multi-atlas segmentation infers the target image segmentation by combining prior
anatomical knowledge encoded in multiple atlases. It has been quite successfully applied to …
anatomical knowledge encoded in multiple atlases. It has been quite successfully applied to …
Automatic kidney segmentation in ultrasound images using subsequent boundary distance regression and pixelwise classification networks
It remains challenging to automatically segment kidneys in clinical ultrasound (US) images
due to the kidneys' varied shapes and image intensity distributions, although semi-automatic …
due to the kidneys' varied shapes and image intensity distributions, although semi-automatic …
Non-rigid image registration using fully convolutional networks with deep self-supervision
We propose a novel non-rigid image registration algorithm that is built upon fully
convolutional networks (FCNs) to optimize and learn spatial transformations between pairs …
convolutional networks (FCNs) to optimize and learn spatial transformations between pairs …