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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 …
3D fully convolutional networks for subcortical segmentation in MRI: A large-scale study
This study investigates a 3D and fully convolutional neural network (CNN) for subcortical
brain structure segmentation in MRI. 3D CNN architectures have been generally avoided …
brain structure segmentation in MRI. 3D CNN architectures have been generally avoided …
Data augmentation using learned transformations for one-shot medical image segmentation
Image segmentation is an important task in many medical applications. Methods based on
convolutional neural networks attain state-of-the-art accuracy; however, they typically rely on …
convolutional neural networks attain state-of-the-art accuracy; however, they typically rely on …
3D whole brain segmentation using spatially localized atlas network tiles
Detailed whole brain segmentation is an essential quantitative technique in medical image
analysis, which provides a non-invasive way of measuring brain regions from a clinical …
analysis, which provides a non-invasive way of measuring brain regions from a clinical …
Individual-specific areal-level parcellations improve functional connectivity prediction of behavior
Resting-state functional magnetic resonance imaging (rs-fMRI) allows estimation of
individual-specific cortical parcellations. We have previously developed a multi-session …
individual-specific cortical parcellations. We have previously developed a multi-session …
[HTML][HTML] Test-time adaptable neural networks for robust medical image segmentation
Abstract Convolutional Neural Networks (CNNs) work very well for supervised learning
problems when the training dataset is representative of the variations expected to be …
problems when the training dataset is representative of the variations expected to be …
Multi-scale patch and multi-modality atlases for whole heart segmentation of MRI
A whole heart segmentation (WHS) method is presented for cardiac MRI. This segmentation
method employs multi-modality atlases from MRI and CT and adopts a new label fusion …
method employs multi-modality atlases from MRI and CT and adopts a new label fusion …
Multi-atlas segmentation of biomedical images: a survey
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 …
DeepNAT: Deep convolutional neural network for segmenting neuroanatomy
We introduce DeepNAT, a 3D Deep convolutional neural network for the automatic
segmentation of NeuroAnaTomy in T1-weighted magnetic resonance images. DeepNAT is …
segmentation of NeuroAnaTomy in T1-weighted magnetic resonance images. DeepNAT is …
FreeSurfer
FreeSurfer is a suite of tools for the analysis of neuroimaging data that provides an array of
algorithms to quantify the functional, connectional and structural properties of the human …
algorithms to quantify the functional, connectional and structural properties of the human …