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| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Hongxu, Yang | - |
| dc.contributor.author | Caifeng, Shan | - |
| dc.contributor.author | Alexander F., Kolen | - |
| dc.date.accessioned | 2023-04-25T02:36:45Z | - |
| dc.date.available | 2023-04-25T02:36:45Z | - |
| dc.date.issued | 2022 | - |
| dc.identifier.uri | https://link.springer.com/article/10.1007/s10462-022-10287-1 | - |
| dc.identifier.uri | https://dlib.phenikaa-uni.edu.vn/handle/PNK/8265 | - |
| dc.description | CC BY | vi |
| dc.description.abstract | Medical instrument detection is essential for computer-assisted interventions, since it facilitates clinicians to find instruments efficiently with a better interpretation, thereby improving clinical outcomes. This article reviews image-based medical instrument detection methods for ultrasound-guided (US-guided) operations. Literature is selected based on an exhaustive search in different sources, including Google Scholar, PubMed, and Scopus. We first discuss the key clinical applications of medical instrument detection in the US, including delivering regional anesthesia, biopsy taking, prostate brachytherapy, and catheterization. Then, we present a comprehensive review of instrument detection methodologies, including non-machine-learning and machine-learning methods. | vi |
| dc.language.iso | en | vi |
| dc.publisher | Springer | vi |
| dc.subject | US-guided | vi |
| dc.title | Medical instrument detection in ultrasound: a review | vi |
| dc.type | Book | vi |
| Appears in Collections | OER - Công nghệ thông tin | |
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