Computer-aided Detection of the Most Suitable MRI Sequences for Subsequent Spinal Metastasis Delineation

Hille, Georg; Serowy, Steffen; Tönnies, Klaus; Saalfeld, Sylvia

doi:https://doi.org/10.1007/978-3-662-56537-7_71

by Georg Hille, Steffen Serowy, Klaus Tönnies, Sylvia Saalfeld

Abstract:

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 0.92Ã‚Â±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.

Reference:

Computer-aided Detection of the Most Suitable MRI Sequences for Subsequent Spinal Metastasis Delineation (Georg Hille, Steffen Serowy, Klaus Tönnies, Sylvia Saalfeld), In Bildverarbeitung für die Medizin 2018 (Andreas Maier, Thomas M. Deserno, Heinz Handels, Klaus Hermann Maier-Hein, Christoph Palm, Thomas Tolxdorff, eds.), Springer Berlin Heidelberg, 2018.

Bibtex Entry:

@inproceedings{hille_computer-aided_2018,
	address = {Berlin, Heidelberg},
	title = {Computer-aided {Detection} of the {Most} {Suitable} {MRI} {Sequences} for {Subsequent} {Spinal} {Metastasis} {Delineation}},
	isbn = {978-3-662-56537-7},
	doi = {https://doi.org/10.1007/978-3-662-56537-7_71},
	abstract = {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 0.92Ã‚Â±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.},
	booktitle = {Bildverarbeitung für die {Medizin} 2018},
	publisher = {Springer Berlin Heidelberg},
	author = {Hille, Georg and Serowy, Steffen and Tönnies, Klaus and Saalfeld, Sylvia},
	editor = {Maier, Andreas and Deserno, Thomas M. and Handels, Heinz and Maier-Hein, Klaus Hermann and Palm, Christoph and Tolxdorff, Thomas},
	year = {2018},
	pages = {275--279}
}