Automatic Pharynx Segmentation from MRI Data for Obstructive Sleep Apnea Analysis

Muhammad Laiq Ur Rahman Shahid, Teodora Chitiboi, Tatyana Ivanovska, Vladimir Molchanov, Henry Völzke, Horst K. Hahn, Lars Linsen

Abstract

Obstructive sleep apnea (OSA) is a public health problem. Volumetric analysis of the upper airways can help us to understand the pathogenesis of OSA. A reliable pharynx segmentation is the first step in identifying the anatomic risk factors for this sleeping disorder. As manual segmentation is a time-consuming and subjective process, a fully automatic segmentation of pharyngeal structures is required when investigating larger data bases such as in cohort studies. We develop a context-based automatic algorithm for segmenting pharynx from magnetic resonance images (MRI). It consists of a pipeline of steps including pre-processing (thresholding, connected component analysis) to extract coarse 3D objects, classification of the objects (involving object-based image analysis (OBIA), visual feature space analysis, and silhouette coefficient computation) to segregate pharynx from other structures automatically, and post-processing to refine the shape of the identified pharynx (including extraction of the oropharynx and propagating results from neighboring slices to slices that are difficult to delineate). Our technique is fast such that we can apply our algorithm to population-based epidemiological studies that provide a high amount of data. Our method needs no user interaction to extract the pharyngeal structure. The approach is quantitatively evaluated on ten datasets resulting in an average of approximately 90% detected volume fraction and a 90% Dice coefficient, which is in the range of the interobserver variation within manual segmentation results.

References

  1. Andrysiak, R., Frank-Piskorska, A., Krolicki, L., Mianowicz, J., Krasum, M., and Ruszczynska, M. (2001). Mri estiamtion of upper airway in patients with obstructive sleep apnea and its correlation with body mass index. In The proceeding of 87th scientific assemly and annual meeting, RSNA01, page 245. RSNA.
  2. Berry, R. B., Budhiraja, R., Gottlieb, D. J., Gozal, D., Iber, C., Kapur, V. K., Marcus, C. L., Mehra, R., Parthasarathy, S., Quan, S. F., et al. (2012). Rules for scoring respiratory events in sleep: update of the 2007 aasm manual for the scoring of sleep and associated events. J Clin Sleep Med, 8(5):597-619.
  3. Burger, W. and Burge, M. J. (2009). Principles of Digital Image Processing. Springer.
  4. Cheng, I., Nilufar, S., Flores-Mir, C., and Basu, A. (2007). Airway segmentation and measurement in ct images. In Engineering in Medicine and Biology Society, 2007. EMBS 2007. 29th Annual International Conference of the IEEE, pages 795-799. IEEE.
  5. Daniel, M. M., Lorenzi, M. C., Leite, C. d. C., and LorenziFilho, G. (2007). Pharyngeal dimensions in healthy men and women. Clinics, 62(1):5-10.
  6. Gonzalez, R. and Woods, R. (2008). Digital image processing: Pearson prentice hall. Upper Saddle River, NJ.
  7. Homeyer, A., Schwier, M., and Hahn, H. K. (2010). A generic concept for object-based image analysis. In VISAPP (2), pages 530-533.
  8. Ivanovska, T., Buttke, E., Laqua, R., Volzke, H., and Beule, A. (2011). Automatic trachea segmentation and evaluation from mri data using intensity pre-clustering and graph cuts. In Image and Signal Processing and Analysis (ISPA), 2011 7th International Symposium on, pages 513-518. IEEE.
  9. Ivanovska, T., Dober, J., Laqua, R., Hegenscheid, K., and Völzke, H. (2013). Pharynx segmentation from mri data for analysis of sleep related disoders. In Advances in Visual Computing, pages 20-29. Springer.
  10. Jolliffe, I. (2005). Principal Component Analysis. Wiley Online Library.
  11. Liao, P.-S., Chen, T.-S., and Chung, P.-C. (2001). A fast algorithm for multilevel thresholding. J. Inf. Sci. Eng., 17(5):713-727.
  12. Liu, J., Udupa, J. K., Odhnera, D., McDonough, J. M., and Arens, R. (2003). System for upper airway segmentation and measurement with mr imaging and fuzzy connectedness. Academic radiology, 10(1):13-24.
  13. Lowe, A. and Fleetham, J. (1991). Two-and threedimensional analyses of tongue, airway, and soft palate size. Atlas of the Difficult Airway. Norton ML, Brown ACD, Eds. Mosby-Year Book, St. Louis, pages 74-82.
  14. Lowe, A. A., Fleetham, J. A., Adachi, S., and Ryan, C. F. (1995). Cephalometric and computed tomographic predictors of obstructive sleep apnea severity. American Journal of Orthodontics and Dentofacial Orthopedics, 107(6):589-595.
  15. Lowe, A. A., Santamaria, J. D., Fleetham, J. A., and Price, C. (1986). Facial morphology and obstructive sleep apnea. American Journal of Orthodontics and Dentofacial Orthopedics, 90(6):484-491.
  16. Molchanov, V. and Linsen, L. (2014). Interactive design of multidimensional data projection layout.
  17. Pack, A. I. (2002). Sleep Apnea: Pathogenesis, Diagnosis and Treatment. CRC Press.
  18. Perona, P. and Malik, J. (1990). Scale-space and edge detection using anisotropic diffusion. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 12(7):629-639.
  19. Rousseeuw, P. J. (1987). Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. Journal of computational and applied mathematics, 20:53-65.
  20. Schwab, R. J., Pasirstein, M., Pierson, R., Mackley, A., Hachadoorian, R., Arens, R., Maislin, G., and Pack, A. I. (2003). Identification of upper airway anatomic risk factors for obstructive sleep apnea with volumetric magnetic resonance imaging. American journal of respiratory and critical care medicine, 168(5):522- 530.
  21. Shapiro, L. G. and Linda, G. (2002). stockman, george c. Computer Vision, Prentice hall. ISBN 0-13-030796-3.
  22. Shaw, J. E., Punjabi, N. M., Wilding, J. P., Alberti, K., and Zimmet, P. Z. (2008). Sleep-disordered breathing and type 2 diabetes: a report from the international diabetes federation taskforce on epidemiology and prevention. Diabetes research and clinical practice, 81(1):2-12.
  23. Shi, H., Scarfe, W. C., and Farman, A. G. (2006). Upper airway segmentation and dimensions estimation from cone-beam ct image datasets. International Journal of Computer Assisted Radiology and Surgery, 1(3):177- 186.
  24. Soille, P. (1999). Morphological image processing: Principles and applications.
  25. Völzke, H., Alte, D., Schmidt, C. O., Radke, D., Lorbeer, R., Friedrich, N., Aumann, N., Lau, K., Piontek, M., Born, G., et al. (2010). Cohort profile: the study of health in pomerania. International journal of epidemiology, page dyp394.
  26. Yang, M., Kpalma, K., Ronsin, J., et al. (2008). A survey of shape feature extraction techniques. Pattern recognition, pages 43-90.
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Paper Citation


in Harvard Style

Shahid M., Chitiboi T., Ivanovska T., Molchanov V., Völzke H., Hahn H. and Linsen L. (2015). Automatic Pharynx Segmentation from MRI Data for Obstructive Sleep Apnea Analysis . In Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2015) ISBN 978-989-758-089-5, pages 599-608. DOI: 10.5220/0005315905990608


in Bibtex Style

@conference{visapp15,
author={Muhammad Laiq Ur Rahman Shahid and Teodora Chitiboi and Tatyana Ivanovska and Vladimir Molchanov and Henry Völzke and Horst K. Hahn and Lars Linsen},
title={Automatic Pharynx Segmentation from MRI Data for Obstructive Sleep Apnea Analysis},
booktitle={Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2015)},
year={2015},
pages={599-608},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005315905990608},
isbn={978-989-758-089-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2015)
TI - Automatic Pharynx Segmentation from MRI Data for Obstructive Sleep Apnea Analysis
SN - 978-989-758-089-5
AU - Shahid M.
AU - Chitiboi T.
AU - Ivanovska T.
AU - Molchanov V.
AU - Völzke H.
AU - Hahn H.
AU - Linsen L.
PY - 2015
SP - 599
EP - 608
DO - 10.5220/0005315905990608