AIMC Topic: Heart

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LCC-Net: A Lightweight Cross-Consistency Network for Semisupervised Cardiac MR Image Segmentation.

Computational and mathematical methods in medicine
Semantic segmentation plays a crucial role in cardiac magnetic resonance (MR) image analysis. Although supervised deep learning methods have made significant performance improvements, they highly rely on a large amount of pixel-wise annotated data, w...

Deep learning-enhanced light-field imaging with continuous validation.

Nature methods
Visualizing dynamic processes over large, three-dimensional fields of view at high speed is essential for many applications in the life sciences. Light-field microscopy (LFM) has emerged as a tool for fast volumetric image acquisition, but its effect...

Multiscale attention guided U-Net architecture for cardiac segmentation in short-axis MRI images.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Automatic cardiac segmentation plays an utmost role in the diagnosis and quantification of cardiovascular diseases.

Interactive contouring through contextual deep learning.

Medical physics
PURPOSE: To investigate a deep learning approach that enables three-dimensional (3D) segmentation of an arbitrary structure of interest given a user provided two-dimensional (2D) contour for context. Such an approach could decrease delineation times ...

Myocardial Function Imaging in Echocardiography Using Deep Learning.

IEEE transactions on medical imaging
Deformation imaging in echocardiography has been shown to have better diagnostic and prognostic value than conventional anatomical measures such as ejection fraction. However, despite clinical availability and demonstrated efficacy, everyday clinical...

Echocardiographic image multi-structure segmentation using Cardiac-SegNet.

Medical physics
PURPOSE: Cardiac boundary segmentation of echocardiographic images is important for cardiac function assessment and disease diagnosis. However, it is challenging to segment cardiac ventricles due to the low contrast-to-noise ratio and speckle noise o...

3DeeCellTracker, a deep learning-based pipeline for segmenting and tracking cells in 3D time lapse images.

eLife
Despite recent improvements in microscope technologies, segmenting and tracking cells in three-dimensional time-lapse images (3D + T images) to extract their dynamic positions and activities remains a considerable bottleneck in the field. We develope...

Edge-Sensitive Left Ventricle Segmentation Using Deep Reinforcement Learning.

Sensors (Basel, Switzerland)
Deep reinforcement learning (DRL) has been utilized in numerous computer vision tasks, such as object detection, autonomous driving, etc. However, relatively few DRL methods have been proposed in the area of image segmentation, particularly in left v...

Disentangle, Align and Fuse for Multimodal and Semi-Supervised Image Segmentation.

IEEE transactions on medical imaging
Magnetic resonance (MR) protocols rely on several sequences to assess pathology and organ status properly. Despite advances in image analysis, we tend to treat each sequence, here termed modality, in isolation. Taking advantage of the common informat...

Direct Attenuation Correction Using Deep Learning for Cardiac SPECT: A Feasibility Study.

Journal of nuclear medicine : official publication, Society of Nuclear Medicine
Dedicated cardiac SPECT scanners with cadmium-zinc-telluride cameras have shown capabilities for shortened scan times or reduced radiation doses, as well as improved image quality. Since most dedicated scanners do not have integrated CT, image quanti...