Zusammenfassung
This study’s objective was to segment vertebral metastases in diagnostic MR images by using a deep learning-based approach. Segmentation of such lesions can present a pivotal step towards enhanced therapy planning and implementation of minimally-invasive interventions like radiofrequency ablations. For this purpose, we used a U-Net-like architecture trained with 38 patient-cases. Our proposed method has been evaluated by comparison to expertly annotated lesion segmentations via Dice coeffcients, sensitivity and specificity rates. While the experiments with T1-weighted MRI images yielded promising results (average Dice score of 73:84 %), T2-weighted images were in average rather insufficient (53:02 %). To our best knowledge, our proposed study is the first to tackle this particular issue, which limits direct comparability with related works. In respect to similar deep learning-based lesion segmentations, e.g. in liver MR images or spinal CT images, our experiments with T1-weighted MR images show similar or in some respects superior segmentation quality.
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
Harrington K. Metastatic disease of the spine. JBJS. 1986;68:1110-1115.
Dupuy DE, Liu D, Hartfeil D, et al. Percutaneous radiofrequency ablation of painful osseous metastases. Cancer. 2010;116:989-997.
Kröger T, Pätz T, Altrogge I, et al. Fast estimation of the vascular cooling in RFA based on numerical simulation. Open Biomed Eng J. 2010;4.
Liu J, Li M, Wang J, et al. A survey of MRI-based brain tumor segmentation methods. Tsinghua Sci Technol. 2014;19(6):578-595.
Christ PF, Ettlinger F, Grün F, et al. Automatic liver and tumor segmentation of ct and mri volumes using cascaded fully convolutional neural networks. arXiv:170205970. 2017;.
Havaei M, Davy A, Warde-Farley D, et al. Brain tumor segmentation with deep neural networks. Med Image Anal. 2017;35:18-31.
Kamnitsas K, Ledig C, Newcombe VF, et al. Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation. Med Image Anal. 2017;36:61-78.
Chmelik J, Jakubicek R, Walek P, et al. Deep convolutional neural network-based segmentation and classification of difficult to define metastatic spinal lesions in 3D CT data. Med Image Anal. 2018;49:76-88.
Ççicek Ӧ, Abdulkadir A, Lienkamp SS, et al.; Springer. 3D U-Net: learning dense volumetric segmentation from sparse annotation. Proc MICCAI. 2016; p. 424-432.
Isensee F, Kickingereder P, Bonekamp D, et al. Brain tumor segmentation using large receptive field deep convolutional neural networks. Proc BVM. 2017; p. 86-91.
Li X, Chen H, Qi X, et al. H-DenseUNet: hybrid densely connected UNet for liver and liver tumor segmentation from CT volumes. arXiv:170907330. 2017;.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature
About this paper
Cite this paper
Hille, G., Dünnwald, M., Becker, M., Steffen, J., Saalfeld, S., Tönnies, K. (2019). Segmentation of Vertebral Metastases in MRI Using an U-Net like Convolutional Neural Network. In: Handels, H., Deserno, T., Maier, A., Maier-Hein, K., Palm, C., Tolxdorff, T. (eds) Bildverarbeitung für die Medizin 2019. Informatik aktuell. Springer Vieweg, Wiesbaden. https://doi.org/10.1007/978-3-658-25326-4_11
Download citation
DOI: https://doi.org/10.1007/978-3-658-25326-4_11
Published:
Publisher Name: Springer Vieweg, Wiesbaden
Print ISBN: 978-3-658-25325-7
Online ISBN: 978-3-658-25326-4
eBook Packages: Computer Science and Engineering (German Language)