Automatic bone segmentation in whole-body CT images

• Verfasst von: Klein, André [VerfasserIn]
• Verfasst von: Warszawski, Jan [VerfasserIn]
• Titel: Automatic bone segmentation in whole-body CT images
• Mitwirkende: Hillengaß, Jens
• Mitwirkende: Maier-Hein, Klaus H.
• Verf.angabe: André Klein, Jan Warszawski, Jens Hillengaß, Klaus H. Maier-Hein
• Jahr: 2019
• Umfang: 9 S.
• Fussnoten: Published online: 13 November 2018 ; Gesehen am 05.06.2019
• Titel Quelle: Enthalten in: International journal of computer assisted radiology and surgery
• Ort Quelle: Berlin : Springer, 2006
• Jahr Quelle: 2019
• Band/Heft Quelle: 14(2019), 1, Seite 21-29
• ISSN Quelle: 1861-6429
• DOI: doi:10.1007/s11548-018-1883-7
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: Multiple myeloma
• Sach-SW: Deep learning
• Sach-SW: Bone segmentation
• Sach-SW: Computed tomography
• Sach-SW: U-Net
• K10plus-PPN: 1666818224

Abstract

PurposeMany 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.MethodsWe 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.ResultsWe 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.ConclusionThese 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.