Download PDF
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)

    Permissions and Reprints

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
 

  • References

  • 1 Raithel HR, Kraus T, Hering KG, Lehnert G. Asbestbedingte Berufskrankheiten: Aktuelle arbeitsmedizinische und klinisch-diagnostische Aspekte. Dtsch Arztebl 1996; 93 (11) A685-693.
    PubMedGoogle Scholar
  • 2 Hodgson JT, McElvenny DM, Darnton AJ, Price MJ, Peto J. The expected burden of mesothelioma mortality in Great Britain from 2002 to 2050. Br J Cancer 2005; 92 (03) 587-593.
    CrossrefPubMedGoogle Scholar
  • 3 Dériot G, Godefroy JP. Rapport d’information fait au nom de la mission commune d’information sur le bilan et les conséquences de la contamination par l’amiante. Sénat de la Republique française. 2005
    PubMedGoogle Scholar
  • 4 Tweedale G. Asbestos and Its Lethal Legacy. Nat Rev Cancer 2002; 02 (04) 311-314.
    CrossrefPubMedGoogle Scholar
  • 5 Sohrab S, Hinterthaner M, Stamatis G, Rödelsperger K, Woitowitz HJ, Konietzko N. Das maligne Pleuramesotheliom. Dtsch Arztebl 2000; 97 (48) A3257-3262.
    PubMedGoogle Scholar
  • 6 Hagemeyer O, Otten H, Kraus T. Asbestos consumption, asbestos exposure and asbestos-related occupational diseases in Germany. Int Arch Occup Environ Health 2006; 79 (08) 613-620.
    CrossrefPubMedGoogle Scholar
  • 7 Lehmann TM, Aach T, Witte H. Sensor, Signaland Image Informatics – State of the Art and Current Topics. In: Haux R, Kulikowski C, editors. IMIA Yearbook of Medical Informatics 2006. Methods Inf Med 2006; 45 (Suppl. 01) S57-67.
    PubMedGoogle Scholar
  • 8 Pistolesi M, Rusthoven J. Malignant Pleural Mesothelioma: Update, Current Management, Newer Therapeutic Strategies. Chest 2004; 126 (04) 1318-1329.
    CrossrefPubMedGoogle Scholar
  • 9 Armato III SG, Oxnard GR, MacMahon H, Vogelzang NJ, Kindler HL, Kocherginsky M, Starkey A. Measurement of mesothelioma on thoracic CT scans: a comparison of manual and computerassisted techniques. Med Phys 2004; 31 (05) 1105-1115.
    CrossrefPubMedGoogle Scholar
  • 10 Carl TMI. Interreadervarianz bei der HRCT- und CXR-Befundung in einer Längsschnittstudie bei ehemals asbeststaubexponierten Personen. Doctoral thesis. Medical Faculty, RWTH Aachen; http://darwin.bth.rwth-aachen.de/opus3/volltexte/2006/1378/ 2005
    Google Scholar
  • 11 Armato III SG, Oxnard GR, Kocherginsky M, Vogelzang NJ, Kindler HL, MacMahon H. Evaluation of Semiautomated Measurements of Mesothelioma Tumor Thickness on CT Scans. Acad Radiol 2005; 12: 1301-1309.
    CrossrefPubMedGoogle Scholar
  • 12 Lehmann TM, Meinzer HP, Tolxdorff T. Advances in Biomedical Image Analysis: Past, Present and Future Challenges. Methods Inf Med 2004; 43: 308-314.
    Article in Thieme ConnectPubMedGoogle Scholar
  • 13 Vogel S, Klein T, Meyer-Ebrecht D, Kraus T. Ein Bildverarbeitungssystem für die automatisierte Vermessung und quantitative verlaufsdokumentation von pleuralen Verdickungen. Tolxdorff T, Braun J, Handels H, Horsch A, Meinzer HP. Bildverarbeitung für die Medizin 2004; Algorithmen – Systeme – Anwendungen.. Proceedings of Workshop; 2004: 433-437.
    Google Scholar
  • 14 Jäger P, Vogel S, Knepper A, Kraus T, Aach T. 3D-Erkennung, Analyse und Visualisierung pleuraler Verdickungen in CT-Daten. Handels H, Ehrhardt J, Horsch A, Meinzer HP, Tolxdorff T. Bildverarbeitung für die Medizin 2004: Algorithmen – Systeme – Anwendungen.. Proceedings of Workshop; 2006: 11-15.
    Google Scholar
  • 15 Morneburg H. Bildgebende Systeme für die medizinische Diagnostik. 3. Erlangen: Publicis MCD Verlag; 1995
  • 16 Gonzalez RC, Woods RE. Digital Image Processing. 3 Reading: Addison-Wesley Co., Inc.; 1992
  • 17 Silva AC, Carvalho PCP, Nunes RA. Researchreport, Segmentation and Reconstruction of the Pulmonary Parenchyma. Vision and Graphics Laboratory. Instituto Nacional de Matemática Pura e Aplicada; Brazil: 2002
    Google Scholar
  • 18 Suzuki S, Abe K. Topological structural analysis of digital binary images by border following. CVGIP 1985; 30 (01) 32-46.
    PubMedGoogle Scholar
  • 19 Xia F. Normal vector and winding number in 2D digital images with their application for hole detection. Pat Recog 2003; 36 (06) 1383-1395.
    CrossrefPubMedGoogle Scholar
  • 20 Preparata FR, Shamos MI. Computational Geometry: An Introduction.. New York: Springer Verlag; 1985
  • 21 Barber CB, Dobkin DP, Huhdanpaa H. The quick-hull algorithm for convex hulls. ACM TOMS. 1996; 22 (04) 469-483.
    CrossrefPubMedGoogle Scholar
  • 22 Turk G, O’Brien JF. Modelling with Implicit Surfaces that Interpolate. ACM Trans Graph 2002; 21 (04) 855-873.
    CrossrefPubMedGoogle Scholar
  • 23 Goodsell G. A multigrid-type method for thin plate spline interpolation on a circle. IMA J Numer Anal 1997; 17 (02) 321-327.
    CrossrefPubMedGoogle Scholar
  • 24 Bookstein FL. Shape and the Information in Medical Images: A Decade of the Morphometric Synthesis. CVIU 1997; 66 (02) 97-118.
    PubMedGoogle Scholar
  • 25 Belongie S. Thin Plate Spline. Math World – A Wolfram Web Resource, created by Weisstein EW. http://mathworld.wolfram.com/ThinPlateSpline.html 2000
    PubMedGoogle Scholar
  • 26 Blake A, Zisserman A. Visual reconstruction.. Cambridge, Mass.: MIT Press; 1987
  • 27 Kuratorium OFFIS e.V. DCMTK-DICOM Toolkit’Version3.5.2.. 2002.
    PubMed
  • 28 Lorensen WE, Cline HE. Marching Cubes: A High Resolution 3D Surface Construction Algorithm. Comput Graph (Proceedings of ACM SIG-GRAPH) 1987; 21 (04) 163-169.
    CrossrefPubMedGoogle Scholar
  • 29 Schreiner D. OpenGL Reference Manual: The Official Reference Document to OpenGL, Version 1.2.. Boston: Addison-Wesley Longman Publishing Co. Inc.; 1999