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.
At the data acquisition time, the operator could not know the quality of the segmentation results.
The success of deep learning heavily depends on the availability of large labeled training sets.
no code implementations • 25 Apr 2021 • Xiaowe Xu, Jiawei Zhang, Jinglan Liu, Yukun Ding, Tianchen Wang, Hailong Qiu, Haiyun Yuan, Jian Zhuang, Wen Xie, Yuhao Dong, Qianjun Jia, Meiping Huang, Yiyu Shi
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.
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.
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.
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.
Real-time cine magnetic resonance imaging (MRI) plays an increasingly important role in various cardiac interventions.
Cardiac magnetic resonance imaging (MRI) is an essential tool for MRI-guided surgery and real-time intervention.
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.
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.