A fusion method for pulmonary nodule segmentation in chest CT image sets
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Dosyalar
Tarih
2016
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
IEEE
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
This paper presents a fusion algorithm combining two methods for pulmonary nodule segmentation in Chest CT scans. Segmentation is an important task for both diagnosis and therapy monitoring. Since manual segmentation of volumes is time-consuming and there is an inter-observer variability between each radiologist, there have been numerous studies on developing schemes for automated segmentation of lung nodules. In order to segment nodules automatically, we have merged two proposed methods from the literature. First, a semi-automated segmentation method is used to find an initial estimate of the nodule using a non-fixed thresholding scheme. Then, the vessels and tissues connected to the nodule segment are removed by using a refinement method based on geodesic distances. The initial results indicate a 0.618 Jaccard index on 50% consensus ground truth data extracted from 18 non-solid and solid nodules from the LIDC-IDRI database, representing a significant improvement for this type of nodules. It is shown that fusion improves the segmentation process by combining the well performing parts of individual algorithms. We expect to further improve the results by fusing other algorithms.
Açıklama
3rd IEEE EMBS International Conference on Biomedical and Health Informatics (IEEE BHI) -- FEB 24-27, 2016 -- Las Vegas, NV
Anahtar Kelimeler
Small Lung Nodules, Database Consortium, Scans
Kaynak
2016 3rd Ieee Embs International Conference on Biomedical and Health Informatics
WoS Q Değeri
N/A