Title
Pulmonary embolism detection using localized vessel-based features in dual energy CT
Abstract
Pulmonary embolism (PE) affects up to 600,000 patients and contributes to at least 100,000 deaths every year in the United States alone. Diagnosis of PE can be difficult as most symptoms are unspecific and early diagnosis is essential for successful treatment. Computed Tomography (CT) images can show morphological anomalies that suggest the existance of PE. Various image-based procedures have been proposed for improving computer-aided diagnosis of PE. We propose a novel method for detecting PE based on localized vessel-based features computed in Dual Energy CT (DECT) images. DECT provides 4D data indexed by the three spatial coordinates and the energy level. The proposed features encode the variation of the Hounsfield Units across the different levels and the CT attenuation related to the amount of iodine contrast in each vessel. A local classification of the vessels is obtained through the classification of these features. Moreover, the localization of the vessel in the lung provides better comparison between patients. Results show that the simple features designed are able to classify pulmonary embolism patients with an AUC (area under the receiver operating curve) of 0.71 on a lobe basis. Prior segmentation of the lung lobes is not necessary because an automatic atlas-based segmentation obtains similar AUC levels (0.65) for the same dataset. The automatic atlas reaches 0.80 AUC in a larger dataset with more control cases.
Year
DOI
Venue
2015
10.1117/12.2082791
Proceedings of SPIE
Keywords
Field
DocType
Dual Energy CT,Pulmonary Embolism,4D analysis
Computer vision,Pulmonary embolism,Receiver operating characteristic,Lung,Segmentation,Computer-aided diagnosis,Lobe,Image segmentation,Artificial intelligence,Radiology,Hounsfield scale,Physics
Conference
Volume
ISSN
Citations 
9414
0277-786X
1
PageRank 
References 
Authors
0.39
6
6