| Autor: |
Li, Guanghui Han, Yuhao Kong, Huixin Wu, Haojiang |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
|
| Zdroj: |
Applied Sciences; Volume 13; Issue 14; Pages: 7999 |
| ISSN: |
2076-3417 |
| DOI: |
10.3390/app13147999 |
| Popis: |
Fast and accurate lesion localization is an important step in medical image analysis. The current supervised deep learning methods have obvious limitations in the application of radiology, as they require a large number of manually annotated images. In response to the above issues, we introduced a deep reinforcement learning (DRL)-based method to locate nasopharyngeal carcinoma lesions in 3D-CT scans. The proposed method uses prior knowledge to guide the agent to reasonably reduce the search space and promote the convergence rate of the model. Furthermore, the multi-scale processing technique is also used to promote the localization of small objects. We trained the proposed model with 3D-CT scans of 50 patients and evaluated it with 3D-CT scans of 30 patients. The experimental results showed that the proposed model has strong robustness, and its accuracy was improved by more than 1 mm on average under the premise of using a smaller dataset compared with the DQN models in recent studies. The proposed model could effectively locate the lesion area of nasopharyngeal carcinoma in 3D-CT scans. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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