Deep Learning in Medical Ultrasound Image Analysis: A Review
| Autor: | He Ma, Yu Wang, Shouliang Qi, Ge Xinke, Zhang Guanjing, Yu-Dong Yao |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
General Computer Science
Contextual image classification business.industry Computer science Deep learning Feature extraction General Engineering ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Iterative reconstruction Machine learning computer.software_genre ultrasound image preprocessing Object detection Computer-aided diagnosis medical ultrasound image analysis Medical imaging General Materials Science Artificial intelligence lcsh:Electrical engineering. Electronics. Nuclear engineering Medical diagnosis business computer lcsh:TK1-9971 |
| Zdroj: | IEEE Access, Vol 9, Pp 54310-54324 (2021) |
| ISSN: | 2169-3536 |
| Popis: | Ultrasound (US) is one of the most widely used imaging modalities in medical diagnosis. It has the advantages of real-time, low cost, noninvasive nature, and easy to operate. However, it also has the unique disadvantages of strong artifacts and noise and high dependence on the experience of doctors. In order to overcome the shortcomings of ultrasound diagnosis and help doctor improve the accuracy and efficiency of diagnosis, many computer aided diagnosis (CAD) systems have been developed. In recent years, deep learning has achieved great success in computer vision with its unique advantages. In the aspect of medical US image analysis, deep learning has also been exploited for its great potential and more and more researchers apply it to CAD systems. In this paper, we first introduce the deep learning models commonly used in medical US image analysis; Second, we review the data preprocessing methods of medical US images, including data augmentation, denoising, and enhancement; Finally, we analyze the applications of deep learning in medical US imaging tasks (such as image classification, object detection, and image reconstruction). |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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