ArticleInternational journal of computer assisted radiology and surgery2018
A unified methodology based on sparse field level sets and boosting algorithms for false positives reduction in lung nodules detection.
Article in International journal of computer assisted radiology and surgery, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Optical Flow Methods for Lung Nodule Segmentation on LIDC-IDRI Images.Journal of digital imaging · 2020Article
- Lung Nodule Detection in CT Images Using Statistical and Shape-Based Features.Journal of imaging · 2020Article
- Automated pulmonary nodule detection in CT images using 3D deep squeeze-and-excitation networks.International journal of computer assisted radiology and surgery · 2019Article
- Evaluation of an AI-Powered Lung Nodule Algorithm for Detection and 3D Segmentation of Primary Lung Tumors.Contrast media & molecular imaging · 2019Article
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3 authors.
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Abstract
purposeThis work aims to develop a unified methodology for the false positives reduction in lung nodules computer-aided detection schemes.
methodsThe 3D region of each detected nodule candidate is first reconstructed using the sparse field method for accurately segmenting the objects. This technique enhances the level set modeling by restricting the computations to a narrow band near the evolving curve. Then, a set of 2D and 3D relevant features are extracted for each segmented candidate. Subsequently, a hybrid undersampling/boosting algorithm called RUSBoost is applied to analyze the features and discriminate real nodules from non-nodules.
resultsThe performance of the proposed scheme was evaluated by using 70 CT images, randomly selected from the Lung Image Database Consortium and containing 198 nodules. Applying RUSBoost classifier exhibited a better performance than some commonly used classifiers. It effectively reduced the average number of FPs to only 3.9 per scan based on a fivefold cross-validation.
conclusionThe practical implementation, applicability for different nodule types and adaptability in handling the imbalanced data classification insure the improvement in lung nodules detection by utilizing this new approach.
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