A robust and efficient framework for tubular structure segmentation in chest CT images.
Frangi filter
Tubular structure segmentation
computer-aided diagnosis
lung nodule
multi-view discriminating scheme
Journal
Technology and health care : official journal of the European Society for Engineering and Medicine
ISSN: 1878-7401
Titre abrégé: Technol Health Care
Pays: Netherlands
ID NLM: 9314590
Informations de publication
Date de publication:
2021
2021
Historique:
pubmed:
12
1
2021
medline:
18
9
2021
entrez:
11
1
2021
Statut:
ppublish
Résumé
Tubular structure segmentation in chest CT images can reduce false positives (FPs) dramatically and improve the performance of nodules malignancy levels classification. In this study, we present a framework that can segment the pulmonary tubular structure regions robustly and efficiently. Firstly, we formulate a global tubular structure identification model based on Frangi filter. The model can recognize irregular vascular structures including bifurcation, small vessel, and junction, robustly and sensitively in 2D images. In addition, to segment the vessels from JVN, we design a local tubular structure identification model with a sliding window. Finally, we propose a multi-view voxel discriminating scheme on the basis of the previous two models. This scheme reduces the computational complexity of obtaining high entropy spatial tubular structure information. Experimental results have shown that the proposed framework achieves TPR of 85.79%, FPR of 24.83%, and ACC of 84.47% with the average elapsed time of 162.9 seconds. The framework provides an automated approach for effectively segmenting tubular structure from the chest CT images.
Sections du résumé
BACKGROUND
BACKGROUND
Tubular structure segmentation in chest CT images can reduce false positives (FPs) dramatically and improve the performance of nodules malignancy levels classification.
OBJECTIVE
OBJECTIVE
In this study, we present a framework that can segment the pulmonary tubular structure regions robustly and efficiently.
METHODS
METHODS
Firstly, we formulate a global tubular structure identification model based on Frangi filter. The model can recognize irregular vascular structures including bifurcation, small vessel, and junction, robustly and sensitively in 2D images. In addition, to segment the vessels from JVN, we design a local tubular structure identification model with a sliding window. Finally, we propose a multi-view voxel discriminating scheme on the basis of the previous two models. This scheme reduces the computational complexity of obtaining high entropy spatial tubular structure information.
RESULTS
RESULTS
Experimental results have shown that the proposed framework achieves TPR of 85.79%, FPR of 24.83%, and ACC of 84.47% with the average elapsed time of 162.9 seconds.
CONCLUSIONS
CONCLUSIONS
The framework provides an automated approach for effectively segmenting tubular structure from the chest CT images.
Identifiants
pubmed: 33427700
pii: THC202431
doi: 10.3233/THC-202431
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
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