Interactive Visual Analysis of Lumbar Back Pain - What the Lumbar Spine Tells About Your Life
Paul Klemm, Sylvia Glaßer, Kai Lawonn, Marko Rak, Henry Völzke, Katrin Hegenscheid, Bernhard Preim
2015
Abstract
Epidemiology aims to provide insight into disease causations. Hence, subject groups (cohorts) are analyzed to correlate the subjects’ varying lifestyles, their medical properties and diseases. Recently, these cohort studies comprise medical image data. We assess potential relations between image-derived variables of the lumbar spine with lower back pain in a cross-sectional study. Therefore, an Interactive Visual Analysis (IVA) framework was created and tested with 2,540 segmented lumbar spine data sets. The segmentation results are evaluated and quantified by employing shape-describing variables, such as spine canal curvature and torsion. We analyze mutual dependencies among shape-describing variables and non-image variables, e.g., pain indicators. Therefore, we automatically train a decision tree classifier for each non-image variable. We provide an IVA technique to compare classifiers with a decision tree quality plot. As a first result, we conclude that image-based variables are only sufficient to describe lifestyle factors within the data. A correlation between lumbar spine shape and lower back pain could not be found with the automatically trained classifiers. However, the presented approach is a valuable extension for the IVA of epidemiological data. Hence, relations between non-image variables were successfully detected and described.
References
- Deng, H., Runger, G., and Tuv, E. (2011). Bias of importance measures for multi-valued attributes and solutions. In Artificial Neural Networks and Machine Learning-ICANN 2011, pages 293-300. Springer.
- Emerson, J. W., Green, W. A., Schloerke, B., Crowley, J., Cook, D., Hofmann, H., and Wickham, H. (2013). The generalized pairs plot. Journal of Computational and Graphical Statistics, 22(1):79-91.
- Fletcher, R. H., Fletcher, S. W., and Fletcher, G. S. (2012). Clinical epidemiology: the essentials. Lippincott Williams & Wilkins.
- Glaßer, S., Niemann, U., Preim, B., and Spiliopoulou, M. (2013). Can we Distinguish Between Benign and Malignant Breast Tumors in DCE-MRI by Studying a Tumors Most Suspect Region Only? In Proc. of Symposium on Computer-Based Medical Systems (CBMS), pages 59-64.
- Hegenscheid, K., Seipel, R., Schmidt, C. O., Völzke, H., Kühn, J.-P., Biffar, R., Kroemer, H. K., Hosten, N., and Puls, R. (2013). Potentially relevant incidental findings on research whole-body MRI in the general adult population: frequencies and management. European Radiology, 23(3):816-826.
- Hoy, D., Brooks, P., Blyth, F., and Buchbinder, R. (2010). The epidemiology of low back pain. Best Practice and Research Clinical Rheumatology, 24(6):769 - 781.
- Keim, D. A., Mansmann, F., Schneidewind, J., Thomas, J., and Ziegler, H. (2008). Visual analytics: Scope and challenges. Springer.
- Klemm, P., Lawonn, K., Rak, M., Preim, B., T önnies, K., Hegenscheid, K., Völzke, H., and Oeltze, S. (2013). Visualization and Analysis of Lumbar Spine Canal Variability in Cohort Study Data. In Proc. of Vision, Modeling, Visualization 2013, pages 121-128.
- Klemm, P., Oeltze, S., Lawonn, K., Hegenscheid, K., Völzke, H., and Preim, B. (2014). Interactive visual analysis of image-centric cohort study data. IEEE Trans. on Visualization and Computer Graphics, 20(12):1673-1682.
- Mitchell, T. M. (1997). Machine learning. 1997. Burr Ridge, IL: McGraw Hill, 45.
- Niemann, U., Völzke, H., Kühn, J.-P., and Spiliopoulou, M. (2014). Learning and inspecting classification rules from longitudinal epidemiological data to identify predictive features on hepatic steatosis. Expert Systems with Applications.
- Preim, B., Klemm, P., Hauser, H., Hegenscheid, K., Oeltze, S., Toennies, K., and Völzke, H. (2015). Visual Analytics of Image-Centric Cohort Studies in Epidemiology, chapter Visualization in Medicine and Life Sciences III, page in print. Springer.
- Rak, M., Engel, K., and Toennies, K. (2013). Closed-form hierarchical finite element models for part-based object detection. In Proc. of Vision, Modeling, Visualization 2013, pages 137-144.
- Szpalski, M., Gunzburg, R., Mélot, C., and Aebi, M. (2005). The aging of the population: a growing concern for spine care in the twenty-first century. In The Aging Spine, pages 1-3. Springer.
- Turkay, C., Lundervold, A., Lundervold, A. J., and Hauser, H. (2013). Hypothesis generation by interactive visual exploration of heterogeneous medical data. In Human-Computer Interaction and Knowledge Discovery in Complex, Unstructured, Big Data, pages 1- 12. Springer.
- Völzke, H., Alte, D., Schmidt, C., et al. (2011). Cohort Profile: The Study of Health in Pomerania. International Journal of Epidemiology, 40(2):294-307.
- Zhang, Z., Gotz, D., and Perer, A. (2012). Interactive visual patient cohort analysis. In Proc. of IEEE VisWeek Workshop on Visual Analytics in Health Care.
Paper Citation
in Harvard Style
Klemm P., Glaßer S., Lawonn K., Rak M., Völzke H., Hegenscheid K. and Preim B. (2015). Interactive Visual Analysis of Lumbar Back Pain - What the Lumbar Spine Tells About Your Life . In Proceedings of the 6th International Conference on Information Visualization Theory and Applications - Volume 1: IVAPP, (VISIGRAPP 2015) ISBN 978-989-758-088-8, pages 85-92. DOI: 10.5220/0005235500850092
in Bibtex Style
@conference{ivapp15,
author={Paul Klemm and Sylvia Glaßer and Kai Lawonn and Marko Rak and Henry Völzke and Katrin Hegenscheid and Bernhard Preim},
title={Interactive Visual Analysis of Lumbar Back Pain - What the Lumbar Spine Tells About Your Life},
booktitle={Proceedings of the 6th International Conference on Information Visualization Theory and Applications - Volume 1: IVAPP, (VISIGRAPP 2015)},
year={2015},
pages={85-92},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005235500850092},
isbn={978-989-758-088-8},
}
in EndNote Style
TY - CONF
JO - Proceedings of the 6th International Conference on Information Visualization Theory and Applications - Volume 1: IVAPP, (VISIGRAPP 2015)
TI - Interactive Visual Analysis of Lumbar Back Pain - What the Lumbar Spine Tells About Your Life
SN - 978-989-758-088-8
AU - Klemm P.
AU - Glaßer S.
AU - Lawonn K.
AU - Rak M.
AU - Völzke H.
AU - Hegenscheid K.
AU - Preim B.
PY - 2015
SP - 85
EP - 92
DO - 10.5220/0005235500850092