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.
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© 2019 Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature
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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
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DOI: https://doi.org/10.1007/978-3-658-25326-4_11
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