Pulmonary Nodule Detection in CT Images : False Positive Reduction Using Multi-View Convolutional Networks
We propose a novel Computer-Aided Detection (CAD) system for pulmonary nodules using multi-view convolutional networks (ConvNets), for which discriminative features are automatically learnt from the training data. The network is fed with nodule candidates obtained by combining three candidate detectors specifically designed for solid, subsolid, and large nodules. For each candidate, a set of 2-D patches from differently oriented planes is extracted. The proposed architecture comprises multiple streams of 2-D ConvNets, for which the outputs are combined using a dedicated fusion method to get the final classification. Data augmentation and dropout are applied to avoid overfitting. On 888 scans of the publicly available LIDC-IDRI dataset, our method reaches high detection sensitivities of 85.4% and 90.1% at 1 and 4 false positives per scan, respectively. An additional evaluation on independent datasets from the ANODE09 challenge and DLCST is performed. We showed that the proposed multi-view ConvNets is highly suited to be used for false positive reduction of a CAD system.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2016 |
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| Erschienen: |
2016 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:35 |
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| Enthalten in: |
IEEE transactions on medical imaging - 35(2016), 5 vom: 05. Mai, Seite 1160-1169 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Setio, Arnaud Arindra Adiyoso [VerfasserIn] |
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| Anmerkungen: |
Date Completed 28.06.2017 Date Revised 30.03.2022 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1109/TMI.2016.2536809 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM258215526 |
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| 500 | |a Date Revised 30.03.2022 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a We propose a novel Computer-Aided Detection (CAD) system for pulmonary nodules using multi-view convolutional networks (ConvNets), for which discriminative features are automatically learnt from the training data. The network is fed with nodule candidates obtained by combining three candidate detectors specifically designed for solid, subsolid, and large nodules. For each candidate, a set of 2-D patches from differently oriented planes is extracted. The proposed architecture comprises multiple streams of 2-D ConvNets, for which the outputs are combined using a dedicated fusion method to get the final classification. Data augmentation and dropout are applied to avoid overfitting. On 888 scans of the publicly available LIDC-IDRI dataset, our method reaches high detection sensitivities of 85.4% and 90.1% at 1 and 4 false positives per scan, respectively. An additional evaluation on independent datasets from the ANODE09 challenge and DLCST is performed. We showed that the proposed multi-view ConvNets is highly suited to be used for false positive reduction of a CAD system | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
| 700 | 1 | |a Ciompi, Francesco |e verfasserin |4 aut | |
| 700 | 1 | |a Litjens, Geert |e verfasserin |4 aut | |
| 700 | 1 | |a Gerke, Paul |e verfasserin |4 aut | |
| 700 | 1 | |a Jacobs, Colin |e verfasserin |4 aut | |
| 700 | 1 | |a van Riel, Sarah J |e verfasserin |4 aut | |
| 700 | 1 | |a Wille, Mathilde Marie Winkler |e verfasserin |4 aut | |
| 700 | 1 | |a Naqibullah, Matiullah |e verfasserin |4 aut | |
| 700 | 1 | |a Sanchez, Clara I |e verfasserin |4 aut | |
| 700 | 1 | |a van Ginneken, Bram |e verfasserin |4 aut | |
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