Deep Learning-Based Key Frame Recognition Algorithm for Adrenal Vascular in X-Ray Imaging
Adrenal vein sampling is required for the staging diagnosis of primary aldosteronism, and the frames in which the adrenal veins are presented are called key frames. Currently, the selection of key frames relies on the doctor's visual judgement which is time-consuming and laborious. This study proposes a key frame recognition algorithm based on deep learning. Firstly, wavelet denoising and multi-scale vessel-enhanced filtering are used to preserve the morphological features of the adrenal veins. Furthermore, by incorporating the self-attention mechanism, an improved recognition model called ResNet50-SA is obtained. Compared with commonly used transfer learning, the new model achieves 97.11% in accuracy, precision, recall, F1, and AUC, which is superior to other models and can help clinicians quickly identify key frames in adrenal veins.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:48 |
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| Enthalten in: |
Zhongguo yi liao qi xie za zhi = Chinese journal of medical instrumentation - 48(2024), 2 vom: 30. März, Seite 138-143 |
| Sprache: |
Chinesisch |
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| Beteiligte Personen: |
Tao, Huimin [VerfasserIn] |
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| Links: |
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| Themen: |
Adrenal angiography |
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| Anmerkungen: |
Date Completed 15.04.2024 Date Revised 15.04.2024 published: Print Citation Status MEDLINE |
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| doi: |
10.12455/j.issn.1671-7104.240040 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM370949307 |
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| 245 | 1 | 0 | |a Deep Learning-Based Key Frame Recognition Algorithm for Adrenal Vascular in X-Ray Imaging |
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| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Adrenal vein sampling is required for the staging diagnosis of primary aldosteronism, and the frames in which the adrenal veins are presented are called key frames. Currently, the selection of key frames relies on the doctor's visual judgement which is time-consuming and laborious. This study proposes a key frame recognition algorithm based on deep learning. Firstly, wavelet denoising and multi-scale vessel-enhanced filtering are used to preserve the morphological features of the adrenal veins. Furthermore, by incorporating the self-attention mechanism, an improved recognition model called ResNet50-SA is obtained. Compared with commonly used transfer learning, the new model achieves 97.11% in accuracy, precision, recall, F1, and AUC, which is superior to other models and can help clinicians quickly identify key frames in adrenal veins | ||
| 650 | 4 | |a English Abstract | |
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a adrenal angiography | |
| 650 | 4 | |a key frame recognition | |
| 650 | 4 | |a self-attention mechanism | |
| 650 | 4 | |a transfer learning | |
| 650 | 4 | |a wavelet transform | |
| 700 | 1 | |a Huang, Miao |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, Cong |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, Yongtian |e verfasserin |4 aut | |
| 700 | 1 | |a Hu, Zhihua |e verfasserin |4 aut | |
| 700 | 1 | |a Tao, Lili |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Shuping |e verfasserin |4 aut | |
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