Titlebook: Statistical Atlases and Computational Models of the Heart. Multi-Sequence CMR Segmentation, CRT-EPig; 10th International W Mihaela Pop,Maxi

迫击炮

SPURN

Ultra-DenseNet for Low-Dose X-Ray Image Denoising in Cardiac Catheter-Based Proceduresn dose to both patients and clinicians. Reducing the radiation dose leads to increased image noise and artifacts, which may reduce discernable image information. Therefore, advanced denoising methods for low-dose X-ray images are needed to improve safety and reliability. Previous X-ray imaging denoi

custody

A Cascade Regression Model for Anatomical Landmark Detectionsion model to make coarse-to-fine landmark detection. Specifically, in the first stage, a Gaussian heatmap regression model customized from U-Net is exploited to make primary prediction, which takes the downsampled entire image as input. In the second stage, we develop a CNN to regress displacements

Fecal-Impaction

Comparison of 2D Echocardiography and Cardiac Cine MRI in the Assessment of Regional Left Ventriculacularly at the epicardial surface. 2D echo images generally have a better contrast-to-noise ratio compared to 3D echo images, thus wall thickness (WT) estimates from 2D echo may provide a reliable means to constrain model fits to 3D echo images. WT estimates were calculated by solving a pair of diff

出汗

Fully Automatic 3D Bi-Atria Segmentation from Late Gadolinium-Enhanced MRIs Using Double Convolutions that sustain atrial fibrillation (AF), the most common cardiac arrhythmia. However, due to the lack of a large labeled dataset, current automated methods have only been developed for left atrium (LA) segmentation. Since AF is sustained across both the LA and right atrium (RA), an automatic bi-atri

nutrition

Exploit

Two-Stage 2D CNN for Automatic Atrial Segmentation from LGE-MRIse current medical practice, atrial segmentation from medical images for clinical diagnosis and treatment, is a labor-intensive and error-prone manual process. The atrial segmentation challenge held in conjunction with the 2018 the Medical Image Computing and Computer Assisted Intervention Society (M

约会

3D Left Ventricular Segmentation from 2D Cardiac MR Images Using Spatial ContextEF). We present a CNN-based method to obtain a 3D representation of LV by integrating information from 2D short-axis and horizontal and vertical long-axis images. Our CNN is flexible to the number of input slices and uses an additional input of image coordinates as spatial context. This concept is v

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