Zusammenfassung
Detection and segmentation of vertebral metastases is a crucial step for support of diagnosis and treatment planning, especially in minimally invasive interventions. Even though computer-assistant tools will not dispense radiologists yet, algorithmically supported detection and segmentation of spinal metastases will play a more and more important role in the near future. The usage of images, where a sufficiently good differentiation between metastases and surrounding tissue is possible, constitutes a critical requirement for successful segmentation procedures. Therefore, we proposed a pipeline, that semi-automatically sorts out unsuitable imaging sequences, as well as combinations of different images via absolute intensity difference images and returns a ranking based recommendation of which image data fits best the requirements for future segmentation tasks. We evaluated our method with 10 patient cases and matched the produced ranking with those of a segmentation field expert. With an average Spearman’s ranking coefficient of 0.92±0.07, our method showed promising results and could be a valueable pre-processing step to speed up clinical segmentation procedures due to omitting the time-consuming manual initialization of choosing suitable image data.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Literatur
Witham TF, Khavkin YA, Gallia GL, et al. Surgery insight: current management of epidural spinal cord compression from metastatic spine disease. Nature Clin Pract Neurol. 2006;2:87–94.
Klimo P, Schmidt MH. Surgical management of spinal metastases. The Oncologist. 2004;9:188–196.
Guillevin R, Vallee JN, Lafitte F, et al. Spine metastasis imaging: review of the literature. Journal of neuroradiology. 2007;34(5):311–321.
Koh J, Chaudhary V, Jeon EK, et al. Automatic spinal canal detection in lumbar MR images in the sagittal view using dynamic programming. Comput Med Imaging Graph. 2014;38(7):569–579.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer-Verlag GmbH Deutschland
About this paper
Cite this paper
Hille, G., Serowy, S., Tönnies, K., Saalfeld, S. (2018). Computer-aided Detection of the Most Suitable MRI Sequences for Subsequent Spinal Metastasis Delineation. In: Maier, A., Deserno, T., Handels, H., Maier-Hein, K., Palm, C., Tolxdorff, T. (eds) Bildverarbeitung für die Medizin 2018. Informatik aktuell. Springer Vieweg, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-56537-7_71
Download citation
DOI: https://doi.org/10.1007/978-3-662-56537-7_71
Published:
Publisher Name: Springer Vieweg, Berlin, Heidelberg
Print ISBN: 978-3-662-56536-0
Online ISBN: 978-3-662-56537-7
eBook Packages: Computer Science and Engineering (German Language)