Deep Learning-Based Medical Ultrasound Image and Video Segmentation Methods: Overview, Frontiers, and Challenges
The intricate imaging structures, artifacts, and noise present in ultrasound images and videos pose significant challenges for accurate segmentation. Deep learning has recently emerged as a prominent field, playing a crucial role in medical image processing. This paper reviews ultrasound image and v...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-04-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/25/8/2361 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849713288620802048 |
|---|---|
| author | Xiaolong Xiao Jianfeng Zhang Yuan Shao Jialong Liu Kaibing Shi Chunlei He Dexing Kong |
| author_facet | Xiaolong Xiao Jianfeng Zhang Yuan Shao Jialong Liu Kaibing Shi Chunlei He Dexing Kong |
| author_sort | Xiaolong Xiao |
| collection | DOAJ |
| description | The intricate imaging structures, artifacts, and noise present in ultrasound images and videos pose significant challenges for accurate segmentation. Deep learning has recently emerged as a prominent field, playing a crucial role in medical image processing. This paper reviews ultrasound image and video segmentation methods based on deep learning techniques, summarizing the latest developments in this field, such as diffusion and segment anything models as well as classical methods. These methods are classified into four main categories based on the characteristics of the segmentation methods. Each category is outlined and evaluated in the corresponding section. We provide a comprehensive overview of deep learning-based ultrasound image segmentation methods, evaluation metrics, and common ultrasound datasets, hoping to explain the advantages and disadvantages of each method, summarize its achievements, and discuss challenges and future trends. |
| format | Article |
| id | doaj-art-4bc0a60a42574de8b9268e51f7b7b16e |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-4bc0a60a42574de8b9268e51f7b7b16e2025-08-20T03:13:59ZengMDPI AGSensors1424-82202025-04-01258236110.3390/s25082361Deep Learning-Based Medical Ultrasound Image and Video Segmentation Methods: Overview, Frontiers, and ChallengesXiaolong Xiao0Jianfeng Zhang1Yuan Shao2Jialong Liu3Kaibing Shi4Chunlei He5Dexing Kong6College of Mathematical Medicine, Zhejiang Normal University, Jinhua 321004, ChinaCollege of Mathematical Medicine, Zhejiang Normal University, Jinhua 321004, ChinaCollege of Mathematical Medicine, Zhejiang Normal University, Jinhua 321004, ChinaCollege of Mathematical Medicine, Zhejiang Normal University, Jinhua 321004, ChinaCollege of Mathematical Medicine, Zhejiang Normal University, Jinhua 321004, ChinaCollege of Mathematical Medicine, Zhejiang Normal University, Jinhua 321004, ChinaCollege of Mathematical Medicine, Zhejiang Normal University, Jinhua 321004, ChinaThe intricate imaging structures, artifacts, and noise present in ultrasound images and videos pose significant challenges for accurate segmentation. Deep learning has recently emerged as a prominent field, playing a crucial role in medical image processing. This paper reviews ultrasound image and video segmentation methods based on deep learning techniques, summarizing the latest developments in this field, such as diffusion and segment anything models as well as classical methods. These methods are classified into four main categories based on the characteristics of the segmentation methods. Each category is outlined and evaluated in the corresponding section. We provide a comprehensive overview of deep learning-based ultrasound image segmentation methods, evaluation metrics, and common ultrasound datasets, hoping to explain the advantages and disadvantages of each method, summarize its achievements, and discuss challenges and future trends.https://www.mdpi.com/1424-8220/25/8/2361deep learningultrasound imagedatasetssegmentationultrasound videoreview |
| spellingShingle | Xiaolong Xiao Jianfeng Zhang Yuan Shao Jialong Liu Kaibing Shi Chunlei He Dexing Kong Deep Learning-Based Medical Ultrasound Image and Video Segmentation Methods: Overview, Frontiers, and Challenges Sensors deep learning ultrasound image datasets segmentation ultrasound video review |
| title | Deep Learning-Based Medical Ultrasound Image and Video Segmentation Methods: Overview, Frontiers, and Challenges |
| title_full | Deep Learning-Based Medical Ultrasound Image and Video Segmentation Methods: Overview, Frontiers, and Challenges |
| title_fullStr | Deep Learning-Based Medical Ultrasound Image and Video Segmentation Methods: Overview, Frontiers, and Challenges |
| title_full_unstemmed | Deep Learning-Based Medical Ultrasound Image and Video Segmentation Methods: Overview, Frontiers, and Challenges |
| title_short | Deep Learning-Based Medical Ultrasound Image and Video Segmentation Methods: Overview, Frontiers, and Challenges |
| title_sort | deep learning based medical ultrasound image and video segmentation methods overview frontiers and challenges |
| topic | deep learning ultrasound image datasets segmentation ultrasound video review |
| url | https://www.mdpi.com/1424-8220/25/8/2361 |
| work_keys_str_mv | AT xiaolongxiao deeplearningbasedmedicalultrasoundimageandvideosegmentationmethodsoverviewfrontiersandchallenges AT jianfengzhang deeplearningbasedmedicalultrasoundimageandvideosegmentationmethodsoverviewfrontiersandchallenges AT yuanshao deeplearningbasedmedicalultrasoundimageandvideosegmentationmethodsoverviewfrontiersandchallenges AT jialongliu deeplearningbasedmedicalultrasoundimageandvideosegmentationmethodsoverviewfrontiersandchallenges AT kaibingshi deeplearningbasedmedicalultrasoundimageandvideosegmentationmethodsoverviewfrontiersandchallenges AT chunleihe deeplearningbasedmedicalultrasoundimageandvideosegmentationmethodsoverviewfrontiersandchallenges AT dexingkong deeplearningbasedmedicalultrasoundimageandvideosegmentationmethodsoverviewfrontiersandchallenges |