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Appl. Sci. 2018, 8(9), 1586;

Semi-Automatic Segmentation of Vertebral Bodies in MR Images of Human Lumbar Spines

School of Electrical and Electronic Engineering, Yonsei University, Seoul 06974, Korea
Department of Radiology, VA San Diego Healthcare System, San Diego, CA 92161-0114, USA
Department of Radiology, University of California-San Diego, La Jolla, CA 92093-0997, USA
Department of Orthopedic Surgery, University of California-San Diego, La Jolla, CA 92037, USA
Author to whom correspondence should be addressed.
Received: 13 August 2018 / Revised: 1 September 2018 / Accepted: 5 September 2018 / Published: 7 September 2018
(This article belongs to the Special Issue Intelligent Imaging and Analysis)
Full-Text   |   PDF [1758 KB, uploaded 12 September 2018]   |  


We propose a semi-automatic algorithm for the segmentation of vertebral bodies in magnetic resonance (MR) images of the human lumbar spine. Quantitative analysis of spine MR images often necessitate segmentation of the image into specific regions representing anatomic structures of interest. Existing algorithms for vertebral body segmentation require heavy inputs from the user, which is a disadvantage. For example, the user needs to define individual regions of interest (ROIs) for each vertebral body, and specify parameters for the segmentation algorithm. To overcome these drawbacks, we developed a semi-automatic algorithm that considerably reduces the need for user inputs. First, we simplified the ROI placement procedure by reducing the requirement to only one ROI, which includes a vertebral body; subsequently, a correlation algorithm is used to identify the remaining vertebral bodies and to automatically detect the ROIs. Second, the detected ROIs are adjusted to facilitate the subsequent segmentation process. Third, the segmentation is performed via graph-based and line-based segmentation algorithms. We tested our algorithm on sagittal MR images of the lumbar spine and achieved a 90% dice similarity coefficient, when compared with manual segmentation. Our new semi-automatic method significantly reduces the user’s role while achieving good segmentation accuracy. View Full-Text
Keywords: semi-automatic segmentation; MR spine image; vertebral body; graph-based segmentation; correlation semi-automatic segmentation; MR spine image; vertebral body; graph-based segmentation; correlation

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Kim, S.; Bae, W.C.; Masuda, K.; Chung, C.B.; Hwang, D. Semi-Automatic Segmentation of Vertebral Bodies in MR Images of Human Lumbar Spines. Appl. Sci. 2018, 8, 1586.

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