Deep Learning in Medical Ultrasound Image Analysis: A Review
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....
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2021-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9395635/ |
| _version_ | 1819139055096430592 |
|---|---|
| author | Yu Wang Xinke Ge He Ma Shouliang Qi Guanjing Zhang Yudong Yao |
| author_facet | Yu Wang Xinke Ge He Ma Shouliang Qi Guanjing Zhang Yudong Yao |
| author_sort | Yu Wang |
| collection | DOAJ |
| description | 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). |
| first_indexed | 2024-12-22T11:16:34Z |
| format | Article |
| id | doaj.art-46f6003922204f5cabe2f70ace318de2 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-22T11:16:34Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-46f6003922204f5cabe2f70ace318de22022-12-21T18:27:59ZengIEEEIEEE Access2169-35362021-01-019543105432410.1109/ACCESS.2021.30713019395635Deep Learning in Medical Ultrasound Image Analysis: A ReviewYu Wang0https://orcid.org/0000-0001-7294-8317Xinke Ge1He Ma2Shouliang Qi3https://orcid.org/0000-0003-0977-1939Guanjing Zhang4Yudong Yao5https://orcid.org/0000-0003-3868-0593College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, ChinaE-Techco Group, Shenzhen, ChinaCollege of Medicine and Biological Information Engineering, Northeastern University, Shenyang, ChinaCollege of Medicine and Biological Information Engineering, Northeastern University, Shenyang, ChinaE-Techco Group, Shenzhen, ChinaDepartment of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, NJ, USAUltrasound (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).https://ieeexplore.ieee.org/document/9395635/Deep learningmedical ultrasound image analysisultrasound image preprocessing |
| spellingShingle | Yu Wang Xinke Ge He Ma Shouliang Qi Guanjing Zhang Yudong Yao Deep Learning in Medical Ultrasound Image Analysis: A Review IEEE Access Deep learning medical ultrasound image analysis ultrasound image preprocessing |
| title | Deep Learning in Medical Ultrasound Image Analysis: A Review |
| title_full | Deep Learning in Medical Ultrasound Image Analysis: A Review |
| title_fullStr | Deep Learning in Medical Ultrasound Image Analysis: A Review |
| title_full_unstemmed | Deep Learning in Medical Ultrasound Image Analysis: A Review |
| title_short | Deep Learning in Medical Ultrasound Image Analysis: A Review |
| title_sort | deep learning in medical ultrasound image analysis a review |
| topic | Deep learning medical ultrasound image analysis ultrasound image preprocessing |
| url | https://ieeexplore.ieee.org/document/9395635/ |
| work_keys_str_mv | AT yuwang deeplearninginmedicalultrasoundimageanalysisareview AT xinkege deeplearninginmedicalultrasoundimageanalysisareview AT hema deeplearninginmedicalultrasoundimageanalysisareview AT shouliangqi deeplearninginmedicalultrasoundimageanalysisareview AT guanjingzhang deeplearninginmedicalultrasoundimageanalysisareview AT yudongyao deeplearninginmedicalultrasoundimageanalysisareview |