Applications of a deep learning method for anti-aliasing and super-resolution in MRI
Magnetic resonance (MR) images with both high resolutions and high signal-to-noise ratios
(SNRs) are desired in many clinical and research applications. However, acquiring such …
(SNRs) are desired in many clinical and research applications. However, acquiring such …
Deep learning-based detection and correction of cardiac MR motion artefacts during reconstruction for high-quality segmentation
Segmenting anatomical structures in medical images has been successfully addressed with
deep learning methods for a range of applications. However, this success is heavily …
deep learning methods for a range of applications. However, this success is heavily …
An overview of disentangled representation learning for MR image harmonization
Magnetic resonance (MR) imaging is one of the most popular imaging modalities for clinical
and translational studies. This is due in no small part to its flexibility; by choosing different …
and translational studies. This is due in no small part to its flexibility; by choosing different …
Brain ventricle parcellation using a deep neural network: Application to patients with ventriculomegaly
Numerous brain disorders are associated with ventriculomegaly, including both neuro-
degenerative diseases and cerebrospinal fluid disorders. Detailed evaluation of the …
degenerative diseases and cerebrospinal fluid disorders. Detailed evaluation of the …
A joint ventricle and WMH segmentation from MRI for evaluation of healthy and pathological changes in the aging brain
Age-related changes in brain structure include atrophy of the brain parenchyma and white
matter changes of presumed vascular origin. Enlargement of the ventricles may occur due to …
matter changes of presumed vascular origin. Enlargement of the ventricles may occur due to …
Automated ventricle parcellation and evan's ratio computation in pre-and post-surgical ventriculomegaly
Normal pressure hydrocephalus (NPH) is a brain disorder associated with enlarged
ventricles and multiple cognitive and motor symptoms. The degree of ventricular …
ventricles and multiple cognitive and motor symptoms. The degree of ventricular …
[HTML][HTML] Fully automatic adaptive meshing based segmentation of the ventricular system for augmented reality visualization and navigation
JAM van Doormaal, T Fick, M Ali, M Köllen… - World Neurosurgery, 2021 - Elsevier
Objective Effective image segmentation of cerebral structures is fundamental to 3-
dimensional techniques such as augmented reality. To be clinically viable, segmentation …
dimensional techniques such as augmented reality. To be clinically viable, segmentation …
[HTML][HTML] Evaluating the impact of MR image harmonization on thalamus deep network segmentation
Medical image segmentation is one of the core tasks of medical image analysis. Automatic
segmentation of brain magnetic resonance images (MRIs) can be used to visualize and …
segmentation of brain magnetic resonance images (MRIs) can be used to visualize and …
Investigation of probability maps in deep-learning-based brain ventricle parcellation
Normal Pressure Hydrocephalus (NPH) is a brain disorder associated with
ventriculomegaly. Accurate segmentation of the ventricle system into its sub-compartments …
ventriculomegaly. Accurate segmentation of the ventricle system into its sub-compartments …
ENRICHing medical imaging training sets enables more efficient machine learning
E Chinn, R Arora, R Arnaout… - Journal of the American …, 2023 - academic.oup.com
Objective Deep learning (DL) has been applied in proofs of concept across biomedical
imaging, including across modalities and medical specialties. Labeled data are critical to …
imaging, including across modalities and medical specialties. Labeled data are critical to …