Novel Pulmonary Nodule Position Detection Method Based on Multiscale Convolution
OBJECTIVE: In order to improve the accuracy of the current pulmonary nodule location detection method based on CT images, reduce the problem of missed detection or false detection, and effectively assist imaging doctors in the diagnosis of pulmonary nodules.
METHODS: Propose a novel method for detecting the location of pulmonary nodules based on multiscale convolution. First, image preprocessing methods are used to eliminate the noise and artifacts in lung CT images. Second, multiple adjacent single-frame CT images are selected to be concatenate into multi-frame images, and the feature extraction is carried out through the artificial neural network model U-Net improved by multi-scale convolution to enhanced feature extraction capability for pulmonary nodules of different sizes and shapes, so as to improve the accuracy of feature extraction of pulmonary nodules. Finally, using point detection to improve the loss function of U-Net training process, the accuracy of pulmonary nodule location detection is improved.
RESULTS: The accuracy of detecting pulmonary nodules equal or larger than 3 mm and smaller than 3 mm are 98.02% and 96.94% respectively.
CONCLUSIONS: This method can effectively improve the detection accuracy of pulmonary nodules on CT image sequence, and can better meet the diagnostic needs of pulmonary nodules.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:47 |
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| Enthalten in: |
Zhongguo yi liao qi xie za zhi = Chinese journal of medical instrumentation - 47(2023), 4 vom: 30. Juli, Seite 402-405 |
| Sprache: |
Chinesisch |
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| Beteiligte Personen: |
Wu, Mengmeng [VerfasserIn] |
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| Links: |
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| Themen: |
CT image sequence |
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| Anmerkungen: |
Date Completed 16.08.2023 Date Revised 16.08.2023 published: Print Citation Status MEDLINE |
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| doi: |
10.3969/j.issn.1671-7104.2023.04.009 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM360796168 |
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| 245 | 1 | 0 | |a Novel Pulmonary Nodule Position Detection Method Based on Multiscale Convolution |
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| 500 | |a Date Completed 16.08.2023 | ||
| 500 | |a Date Revised 16.08.2023 | ||
| 500 | |a published: Print | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a OBJECTIVE: In order to improve the accuracy of the current pulmonary nodule location detection method based on CT images, reduce the problem of missed detection or false detection, and effectively assist imaging doctors in the diagnosis of pulmonary nodules | ||
| 520 | |a METHODS: Propose a novel method for detecting the location of pulmonary nodules based on multiscale convolution. First, image preprocessing methods are used to eliminate the noise and artifacts in lung CT images. Second, multiple adjacent single-frame CT images are selected to be concatenate into multi-frame images, and the feature extraction is carried out through the artificial neural network model U-Net improved by multi-scale convolution to enhanced feature extraction capability for pulmonary nodules of different sizes and shapes, so as to improve the accuracy of feature extraction of pulmonary nodules. Finally, using point detection to improve the loss function of U-Net training process, the accuracy of pulmonary nodule location detection is improved | ||
| 520 | |a RESULTS: The accuracy of detecting pulmonary nodules equal or larger than 3 mm and smaller than 3 mm are 98.02% and 96.94% respectively | ||
| 520 | |a CONCLUSIONS: This method can effectively improve the detection accuracy of pulmonary nodules on CT image sequence, and can better meet the diagnostic needs of pulmonary nodules | ||
| 650 | 4 | |a English Abstract | |
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a CT image sequence | |
| 650 | 4 | |a U-Net | |
| 650 | 4 | |a multi-scale convolution | |
| 650 | 4 | |a pulmonary nodules | |
| 700 | 1 | |a Du, Qiuchen |e verfasserin |4 aut | |
| 700 | 1 | |a Guo, Yi |e verfasserin |4 aut | |
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