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SU‐GG‐I‐100: Development of Computerized Method for Automated Detection of Mesothelioma on 3D Thoracic CT
Author(s) -
Kawashita I,
Masumoto Y,
Aoyama M,
Asada N,
Nakajima M,
Okura Y,
Ishida T
Publication year - 2010
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.3468133
Subject(s) - medicine , radiology , rib cage , mesothelioma , pleural thickening , diaphragm (acoustics) , segmentation , mediastinum , nuclear medicine , computed tomography , pathology , computer science , anatomy , artificial intelligence , physics , acoustics , loudspeaker
Purpose : To develop an automated detection and segmentation scheme for the evaluation of pleural thickening on 3D thoracic CT in order to assist the early detection and follow‐up of mesothelioma. Method and Materials : Our database consists of normal 20 thoracic CT images and abnormal 18 images including pleural plaques, calcifications and mesothelioma. In the first step, 3D region growing technique was applied to original CT images in order to segment lung, soft tissue, and bone regions. Second, center lines of ribs were recognized by voting the curvature information. Then, calcifications were detected by excluding rib regions from segmented bone regions. In the third step, spherical shell filtering was applied to extract soft tissue regions for detection of initial pleural plaque candidates. In the fourth step, pleural plaque regions were determined by excepting mediastinum and under diaphragm regions from extracted pleural plaque candidates. Finally, cases with pleural plaques more than 10 % of total pleural volume were classified as abnormal. We made a quantitative evaluation by comparing the segmentation result with the respiratory physician's gold standard. Results : Our method was tested with a dataset of 38 CT images. As the detection result, all 18 abnormal cases were correctly detected with 3 false positive cases (sensitivity: 100 %, specificity: 85 %). As the segmentation result, the average coincidence degree was 63.5 %. (coincidence degree = A∩B / A B, A: pleural plaque region extracted automatically; B: pleural plaque region extracted manually by respiratory physician) Conclusion : We have developed a computerized method for the automated measurement of mesothelioma on thoracic CT images. Our method will be useful for the early detection and follow‐up of mesothelioma on thoracic CT images.