Low-dose computed tomography (CT) will increase noise and artefacts while reducing the radiation dose, which will adversely affect the diagnosis of radiologists. Low-dose CT image denoising is a challenging task. There are essential differences between the traditional methods and the deep learning-based methods. This paper discusses the denoising approaches of low-dose CT image via deep learning. Deep learning-based methods have achieved relatively ideal denoising effects in both subjective visual quality and quantitative objective metrics. This paper focuses on three state-of-the-art deep learning-based image denoising methods, in addition, four traditional methods are used as the control group to compare the denoising effect. Comprehensive experiments show that the deep learning-based methods are superior to the traditional methods in low-dose CT images denoising.
The aim of this review is to introduce some applications of artificial intelligence (AI) algorithms for the detection and quantification of coronary stenosis using computed tomography angiography (CTA). The realization of automatic/semi-automatic stenosis detection and quantification includes the following steps: vessel central axis extraction, vessel segmentation, stenosis detection, and quantification. Many new AI techniques, such as machine learning and deep learning, have been widely used in medical image segmentation and stenosis detection. This review also summarizes the recent progress regarding coronary stenosis detection and quantification, and discusses the development trends in this field. Through evaluation and comparison, researchers can better understand the research frontier in related fields, compare the advantages and disadvantages of various methods, and better optimize the new technologies. Machine learning and deep learning will promote the process of automatic detection and quantification of coronary artery stenosis. However, the machine learning and the deep learning methods need a large amount of data, so they also face some challenges because of the lack of professional image annotations (manually add labels by experts).
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