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Methods Inf Med 2007; 46(03): 324-331
DOI: 10.1160/ME9050
paper
Schattauer GmbH

Computer-assisted Diagnosis for Early Stage Pleural Mesothelioma

Towards Automated Detection and Quantitative Assessment of Pleural Thickenings from Thoracic CT Images
K. Chaisaowong
1   Institute of Imaging and Computer Vision, RWTH Aachen University, Aachen, Germany
2   The Sirindhorn International Thai-German Graduate School of Engineering, King Mongkut’s Institute of Technology North Bangkok, Thailand
,
P. Jäger
1   Institute of Imaging and Computer Vision, RWTH Aachen University, Aachen, Germany
,
S. Vogel
3   Chair for Medical Information Technology, RWTH Aachen University, Aachen, Germany
,
A. Knepper
1   Institute of Imaging and Computer Vision, RWTH Aachen University, Aachen, Germany
,
T. Kraus
4   Institute and Out-Patient Clinic for Occupational Medicine, University Hospital, Aachen, Germany
,
T. Aach
1   Institute of Imaging and Computer Vision, RWTH Aachen University, Aachen, Germany
› Author Affiliations
Further Information

Publication History

Publication Date:
20 January 2018 (online)

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Summary

Objectives: Pleural thickenings as biomarker of exposure to asbestos may evolve into malignant pleural mesothelioma. Foritsearly stage, pleurectomy with perioperative treatment can reduce morbidity and mortality. The diagnosis is based on a visual investigation of CT images, which is a time-consuming and subjective procedure. Our aim is to develop an automatic image processing approach to detect and quantitatively assess pleural thickenings.

Methods: We first segment the lung areas, and identify the pleural contours. A convexity model is then used together with a Hounsfield unit threshold to detect pleural thickenings. The assessment of the detected pleural thickenings is based on a spline-based model of the healthy pleura.

Results: Tests were carried out on 14 data sets from three patients. In all cases, pleural contours were reliably identified, and pleural thickenings detected. PC-based Computation times were 85 min for a data set of 716 slices, 35 min for 401 slices, and 4 min for 75 slices, resulting in an average computation time of about 5.2 s per slice. Visualizations of pleurae and detected thickeningswere provided.

Conclusion: Results obtained so far indicate that our approach is able to assist physicians in the tedious task of finding and quantifying pleural thickenings in CT data. In the next step, our system will undergo an evaluation in a clinical test setting using routine CT data to quantifyits performance.

Keywords

Malignant pleural mesothelioma - pleural thickening - thoracic spiral computed tomography - computerassisted diagnosis - automatic image processing algorithms
 

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