Vertebral body segmentation in wide range clinical routine spine MRI data. (March 2018)

• Record Type: Journal Article
• Authors: Hille, Georg; Saalfeld, Sylvia; Serowy, Steffen; Tönnies, Klaus
• Is Part Of: Computer methods and programs in biomedicine. Volume 155(2018)
• Page Start: 93
• Page End: 99
• Publication Date: 2018-03
• Subjects: Clinical spine MRI -- Vertebral body -- Segmentation -- Hybrid level-sets -- Various MRI sequences; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2017.12.013
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: Clinical spine MRI segmentation is challenging and time consuming. Previous works are limited to particular MRI sequences or take minutes to compute. A precise and seconds-fast vertebral body segmentation method is proposed. Our evaluation set is the largest so far, consisting of 419 vertebral bodies. Abstract: Background and objective: In this work we propose a 3D vertebral body segmentation approach for clinical magnetic resonance (MR) spine imaging. So far, vertebrae segmentation approaches in MR spine imaging are either limited to particular MR imaging sequences or require minutes to compute, which can be hindering in clinical routine. The major contribution of our work is a reasonably precise segmentation result, within seconds and with minimal user interaction, for spine MR imaging commonly used in clinical routine. Our focus lies on the applicability towards a large variety of clinical MR imaging sequences, dealing with low image quality, high anisotropy and spine pathologies.Methods: Our method starts with a intensity correction step to deal with bias field artifacts and a minimal user-assisted initialization. Next, appearance-based vertebral body probability maps guide a subsequent hybrid level-set segmentation.Results: We tested our method on different MR imaging sequences from 48 subjects. Overall, our evaluation set contains 63 datasets including 419 vertebral bodies, which differ in age, sex and presence of spine pathologies. This is the largest set of reference segmentations of clinical routine spine MR imaging so far. We achieved a Dice coefficient of 86.0%, a mean Euclidean surface distance error of 1.59 ± 0.24 mm and a Hausdorff distance of 6.86 mm.Conclusions: These results illustrate the robustness of our segmentation approach towards the variety of MR image data, which is a pivotal aspect for clinical usefulness and reliable diagnosis.