Search Results for author:
Found 11 papers, 2 papers with code
"One-Shot" Reduction of Additive Artifacts in Medical Images
Medical images may contain various types of artifacts with different patterns and mixtures, which depend on many factors such as scan setting, machine condition, patients' characteristics, surrounding environment, etc.
Hardware-aware Real-time Myocardial Segmentation Quality Control in Contrast Echocardiography
At the data acquisition time, the operator could not know the quality of the segmentation results.
Hardware Aware Neural Architecture Search
Neural Architecture Search
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Positional Contrastive Learning for Volumetric Medical Image Segmentation
The success of deep learning heavily depends on the availability of large labeled training sets.
Multi-Cycle-Consistent Adversarial Networks for Edge Denoising of Computed Tomography Images
As one of the most commonly ordered imaging tests, computed tomography (CT) scan comes with inevitable radiation exposure that increases the cancer risk to patients.
ImageCHD: A 3D Computed Tomography Image Dataset for Classification of Congenital Heart Disease
To demonstrate this, we further present a baseline framework for the automatic classification of CHD, based on a state-of-the-art CHD segmentation method.
Myocardial Segmentation of Cardiac MRI Sequences with Temporal Consistency for Coronary Artery Disease Diagnosis
Coronary artery disease (CAD) is the most common cause of death globally, and its diagnosis is usually based on manual myocardial segmentation of Magnetic Resonance Imaging (MRI) sequences.
Towards Cardiac Intervention Assistance: Hardware-aware Neural Architecture Exploration for Real-Time 3D Cardiac Cine MRI Segmentation
Experimental results on ACDC MICCAI 2017 dataset demonstrate that our hardware-aware multi-scale NAS framework can reduce the latency by up to 3. 5 times and satisfy the real-time constraints, while still achieving competitive segmentation accuracy, compared with the state-of-the-art NAS segmentation framework.
ICA-UNet: ICA Inspired Statistical UNet for Real-time 3D Cardiac Cine MRI Segmentation
Real-time cine magnetic resonance imaging (MRI) plays an increasingly important role in various cardiac interventions.
MSU-Net: Multiscale Statistical U-Net for Real-time 3D Cardiac MRI Video Segmentation
Cardiac magnetic resonance imaging (MRI) is an essential tool for MRI-guided surgery and real-time intervention.
Accurate Congenital Heart Disease Model Generation for 3D Printing
3D printing has been widely adopted for clinical decision making and interventional planning of Congenital heart disease (CHD), while whole heart and great vessel segmentation is the most significant but time-consuming step in the model generation for 3D printing.
Machine Vision Guided 3D Medical Image Compression for Efficient Transmission and Accurate Segmentation in the Clouds
In this paper we will use deep learning based medical image segmentation as a vehicle and demonstrate that interestingly, machine and human view the compression quality differently.
