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Erschienen in: International Journal of Computer Assisted Radiology and Surgery 1/2019

13.11.2018 | Original Article

Automatic bone segmentation in whole-body CT images

verfasst von: André Klein, Jan Warszawski, Jens Hillengaß, Klaus H. Maier-Hein

Erschienen in: International Journal of Computer Assisted Radiology and Surgery | Ausgabe 1/2019

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Abstract

Purpose

Many diagnostic or treatment planning applications critically depend on the successful localization of bony structures in CT images. Manual or semiautomatic bone segmentation is tedious, however, and often not practical in clinical routine. In this paper, we present a reliable and fully automatic bone segmentation in whole-body CT scans of patients suffering from multiple myeloma.

Methods

We address this problem by using convolutional neural networks with an architecture inspired by the U-Net [17]. In this publication, we compared three training procedures: (1) training from 2D axial slices, (2) a pseudo-3D approach including axial, sagittal and coronal slices and (3) an approach where the network is pre-trained in an unsupervised manner.

Results

We evaluated the method on an in-house dataset of 18 whole-body CT scans consisting of 6800 axial slices, achieving a dice score of 0.95 and an intersection over union (IOU) of 0.91. Furthermore, we evaluated our method on the dataset used by Peréz-Carrasco et al. (Comput Methods Progr Biomed 156:85–95, 2018). The data and the ground truth have been made publicly available. The proposed method outperformed the other methods, obtaining a dice score of 0.92 and an IOU of 0.85.

Conclusion

These promising results could facilitate the evaluation of bone density and the localization of focal lesions in the future, with a potential impact on both disease staging and treatment planning.
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Metadaten
Titel
Automatic bone segmentation in whole-body CT images
verfasst von
André Klein
Jan Warszawski
Jens Hillengaß
Klaus H. Maier-Hein
Publikationsdatum
13.11.2018
Verlag
Springer International Publishing
Erschienen in
International Journal of Computer Assisted Radiology and Surgery / Ausgabe 1/2019
Print ISSN: 1861-6410
Elektronische ISSN: 1861-6429
DOI
https://doi.org/10.1007/s11548-018-1883-7

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