Computerized Identification of Bronchiectasis Using a 3D Quantitative CT Protocol
Objective: This study proposes a three-dimensional automatic approach to identify bronchiectasis. The performance of the quantitative CT protocol is evaluated on bronchiectasis patients. Methods: Chest CT images from 121 patients with bronchiectasis were analyzed using
a quantitative CT protocol. The radiologist features were determined by the extent of bronchiectasis; the degree of bronchiectasis; and the time costs by comparing the discrepancy between the radiologists' results and that of computerized scheme. Results: The mean score of bronchiectasis
extent between the computerized scheme and the radiologists were 0.10±2.30 and 0.33±1.25 (r = 0.78, p = .00) respectively. When the degree of bronchiectasis was considered, the value of bronchial dilatation (VBD) reported by quantitative CT analyze was 2.13±3.20;
and the relationship between internal diameter of dilated bronchi and that of its adjacent pulmonary artery (IDB/IDA) measured by radiologists was 1.92±2.46 (r = 0.73, p = .00). The computational cost in time of segmenting bronchiectasis was 4.50 s±0.02, which was
much less than the time cost of the radiologists. (10.29 m±20.67, t = 18.78, p = .00). 24-hour sputum volume was associated with the extent of bronchiectasis determined by both the radiologists and the quantitative CT method (r = 0.64, p = .00, r =
0.58, p = .00, respectively). Conclusion: This study showed that a three-dimensional computerized scheme could achieve a reasonable performance in bronchial segmentation and detection. Three-dimensional bronchial detection can establish the relationship between the morphologic
changes and clinical activity.
Keywords: BRONCHIECTASIS; COMPUTER-AIDED DETECTION; HIGH-RESOLUTION CT; SEGMENTATION; THREE-DIMENSIONAL APPROACH
Document Type: Research Article
Publication date: 01 September 2016
- Journal of Medical Imaging and Health Informatics (JMIHI) is a medium to disseminate novel experimental and theoretical research results in the field of biomedicine, biology, clinical, rehabilitation engineering, medical image processing, bio-computing, D2H2, and other health related areas.
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