AIMC Topic: Magnetic Resonance Imaging

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Direct Segmentation-Based Full Quantification for Left Ventricle via Deep Multi-Task Regression Learning Network.

IEEE journal of biomedical and health informatics
Quantitative analysis of the heart is extremely necessary and significant for detecting and diagnosing heart disease, yet there are still some challenges. In this study, we propose a new end-to-end segmentation-based deep multi-task regression learni...

Automatic brain labeling via multi-atlas guided fully convolutional networks.

Medical image analysis
Multi-atlas-based methods are commonly used for MR brain image labeling, which alleviates the burdening and time-consuming task of manual labeling in neuroimaging analysis studies. Traditionally, multi-atlas-based methods first register multiple atla...

Learning deep similarity metric for 3D MR-TRUS image registration.

International journal of computer assisted radiology and surgery
PURPOSE: The fusion of transrectal ultrasound (TRUS) and magnetic resonance (MR) images for guiding targeted prostate biopsy has significantly improved the biopsy yield of aggressive cancers. A key component of MR-TRUS fusion is image registration. H...

Automatic localization of the subthalamic nucleus on patient-specific clinical MRI by incorporating 7 T MRI and machine learning: Application in deep brain stimulation.

Human brain mapping
Deep brain stimulation (DBS) of the subthalamic nucleus (STN) has shown clinical potential for relieving the motor symptoms of advanced Parkinson's disease. While accurate localization of the STN is critical for consistent across-patients effective D...

HyperDense-Net: A Hyper-Densely Connected CNN for Multi-Modal Image Segmentation.

IEEE transactions on medical imaging
Recently, dense connections have attracted substantial attention in computer vision because they facilitate gradient flow and implicit deep supervision during training. Particularly, DenseNet that connects each layer to every other layer in a feed-fo...

Translation of morphological and functional musculoskeletal imaging.

Journal of orthopaedic research : official publication of the Orthopaedic Research Society
In an effort to develop quantitative biomarkers for degenerative joint disease and fill the void that exists for diagnosing, monitoring, and assessing the extent of whole joint degeneration, the past decade has been marked by a greatly increased role...

A collaborative computer aided diagnosis (C-CAD) system with eye-tracking, sparse attentional model, and deep learning.

Medical image analysis
Computer aided diagnosis (CAD) tools help radiologists to reduce diagnostic errors such as missing tumors and misdiagnosis. Vision researchers have been analyzing behaviors of radiologists during screening to understand how and why they miss tumors o...

Resting-state Functional Connectivity and Deception: Exploring Individualized Deceptive Propensity by Machine Learning.

Neuroscience
Individuals show marked variability in determining to be honest or deceptive in daily life. A large number of studies have investigated the neural substrates of deception; however, the brain networks contributing to the individual differences in dece...

A novel MRI segmentation method using CNN-based correction network for MRI-guided adaptive radiotherapy.

Medical physics
PURPOSE: The purpose of this study was to expedite the contouring process for MRI-guided adaptive radiotherapy (MR-IGART), a convolutional neural network (CNN) deep-learning (DL) model is proposed to accurately segment the liver, kidneys, stomach, bo...

3D regression neural network for the quantification of enlarged perivascular spaces in brain MRI.

Medical image analysis
Enlarged perivascular spaces (EPVS) in the brain are an emerging imaging marker for cerebral small vessel disease, and have been shown to be related to increased risk of various neurological diseases, including stroke and dementia. Automated quantifi...