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Heart

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Estimation of Cardiac Short Axis Slice Levels with a Cascaded Deep Convolutional and Recurrent Neural Network Model.

Tomography (Ann Arbor, Mich.)
Automatic identification of short axis slice levels in cardiac magnetic resonance imaging (MRI) is important in efficient and precise diagnosis of cardiac disease based on the geometry of the left ventricle. We developed a combined model of convoluti...

Rapid estimation of 2D relative -maps from localizers in the human heart at 7T using deep learning.

Magnetic resonance in medicine
PURPOSE: Subject-tailored parallel transmission pulses for ultra-high fields body applications are typically calculated based on subject-specific -maps of all transmit channels, which require lengthy adjustment times. This study investigates the fea...

Deep learning for myocardial ischemia auxiliary diagnosis using CZT SPECT myocardial perfusion imaging.

Journal of the Chinese Medical Association : JCMA
BACKGROUND: The World Health Organization reported that cardiovascular disease is the most common cause of death worldwide. On average, one person dies of heart disease every 26 min worldwide. Deep learning approaches are characterized by the appropr...

Improving myocardial pathology segmentation with U-Net++ and EfficientSeg from multi-sequence cardiac magnetic resonance images.

Computers in biology and medicine
BACKGROUND: Myocardial pathology segmentation plays an utmost role in the diagnosis and treatment of myocardial infarction (MI). However, manual segmentation is time-consuming and labor-intensive, and requires a lot of professional knowledge and clin...

Echocardiography Segmentation With Enforced Temporal Consistency.

IEEE transactions on medical imaging
Convolutional neural networks (CNN) have demonstrated their ability to segment 2D cardiac ultrasound images. However, despite recent successes according to which the intra-observer variability on end-diastole and end-systole images has been reached, ...

Two-Stage CNN Whole Heart Segmentation Combining Image Enhanced Attention Mechanism and Metric Classification.

Journal of digital imaging
Accurate segmentation of multiple tissues and organs in cardiac medical imaging is of great value in computer-aided cardiovascular diagnosis. However, it is challenging due to the complex distribution of various tissues and organs in cardiac MRI (mag...

IFT-Net: Interactive Fusion Transformer Network for Quantitative Analysis of Pediatric Echocardiography.

Medical image analysis
The task of automatic segmentation and measurement of key anatomical structures in echocardiography is critical for subsequent extraction of clinical parameters. However, the influence of boundary blur, speckle noise, and other factors increase the d...

DuDoSS: Deep-learning-based dual-domain sinogram synthesis from sparsely sampled projections of cardiac SPECT.

Medical physics
PURPOSE: Myocardial perfusion imaging (MPI) using single-photon emission-computed tomography (SPECT) is widely applied for the diagnosis of cardiovascular diseases. In clinical practice, the long scanning procedures and acquisition time might induce ...

Deep learning-based 4D-synthetic CTs from sparse-view CBCTs for dose calculations in adaptive proton therapy.

Medical physics
BACKGROUND: Time-resolved 4D cone beam-computed tomography (4D-CBCT) allows a daily assessment of patient anatomy and respiratory motion. However, 4D-CBCTs suffer from imaging artifacts that affect the CT number accuracy and prevent accurate proton d...

An automated deep learning method and novel cardiac index to detect canine cardiomegaly from simple radiography.

Scientific reports
Since most of degenerative canine heart diseases accompany cardiomegaly, early detection of cardiac enlargement is main priority healthcare issue for dogs. In this study, we developed a new deep learning-based radiographic index quantifying canine he...