AIMC Topic: Magnetic Resonance Imaging
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Within node connectivity changes, not simply edge changes, influence graph theory measures in functional connectivity studies of the brain.
NeuroImage
Jul 2, 2021
Interest in understanding the organization of the brain has led to the application of graph theory methods across a wide array of functional connectivity studies. The fundamental basis of a graph is the node. Recent work has shown that functional nod...
Deep Learning-Based Magnetic Resonance Imaging Image Features for Diagnosis of Anterior Cruciate Ligament Injury.
Journal of healthcare engineering
Jul 2, 2021
To study and explore the adoption value of magnetic resonance imaging (MRI) in the diagnosis of anterior cruciate ligament (ACL) injuries, a multimodal feature fusion model based on deep learning was proposed for MRI diagnosis. After the related perf...
A deep learning method for automatic segmentation of the bony orbit in MRI and CT images.
Scientific reports
Jul 1, 2021
This paper proposes a fully automatic method to segment the inner boundary of the bony orbit in two different image modalities: magnetic resonance imaging (MRI) and computed tomography (CT). The method, based on a deep learning architecture, uses two...
A transfer learning approach to facilitate ComBat-based harmonization of multicentre radiomic features in new datasets.
PloS one
Jul 1, 2021
PURPOSE: To facilitate the demonstration of the prognostic value of radiomics, multicenter radiomics studies are needed. Pooling radiomic features of such data in a statistical analysis is however challenging, as they are sensitive to the variability...
Datasets as Topic
Chemoradiotherapy
Adult
Magnetic Resonance Imaging
Uterine Cervical Neoplasms
Humans
Decision Support Systems, Clinical
Cervix Uteri
Retrospective Studies
Middle Aged
Aged
Machine Learning
Treatment Outcome
Tomography, X-Ray Computed
Aged, 80 and over
Positron-Emission Tomography
Follow-Up Studies
Young Adult
Female
Image Interpretation, Computer-Assisted
Cross Attention Squeeze Excitation Network (CASE-Net) for Whole Body Fetal MRI Segmentation.
Sensors (Basel, Switzerland)
Jun 30, 2021
Segmentation of the fetus from 2-dimensional (2D) magnetic resonance imaging (MRI) can aid radiologists with clinical decision making for disease diagnosis. Machine learning can facilitate this process of automatic segmentation, making diagnosis more...
Effect of age and sex on fully automated deep learning assessment of left ventricular function, volumes, and contours in cardiac magnetic resonance imaging.
The international journal of cardiovascular imaging
Jun 29, 2021
Deep learning algorithms for left ventricle (LV) segmentation are prone to bias towards the training dataset. This study assesses sex- and age-dependent performance differences when using deep learning for automatic LV segmentation. Retrospective ana...
Nonlinear manifold learning in functional magnetic resonance imaging uncovers a low-dimensional space of brain dynamics.
Human brain mapping
Jun 29, 2021
Large-scale brain dynamics are believed to lie in a latent, low-dimensional space. Typically, the embeddings of brain scans are derived independently from different cognitive tasks or resting-state data, ignoring a potentially large-and shared-portio...
Reproducibility of automated habenula segmentation via deep learning in major depressive disorder and normal controls with 7 Tesla MRI.
Scientific reports
Jun 29, 2021
The habenula is one of the most important brain regions for investigating the etiology of psychiatric diseases such as major depressive disorder (MDD). However, the habenula is challenging to delineate with the naked human eye in brain imaging due to...
Deep learning-based synthetic CT generation for MR-only radiotherapy of prostate cancer patients with 0.35T MRI linear accelerator.
Journal of applied clinical medical physics
Jun 28, 2021
PURPOSE: To develop a deep learning model to generate synthetic CT for MR-only radiotherapy of prostate cancer patients treated with 0.35 T MRI linear accelerator.
Novel Approaches to Detection of Cerebral Microbleeds: Single Deep Learning Model to Achieve a Balanced Performance.
Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association
Jun 25, 2021
PURPOSE: Cerebral microbleeds (CMBs) are considered essential indicators for the diagnosis of cerebrovascular disease and cognitive disorders. Traditionally, CMBs are manually interpreted based on criteria including the shape, diameter, and signal ch...
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