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

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. Existin...

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Vydáno v:Applied sciences Ročník 8; číslo 9; s. 1586
Hlavní autoři: Kim, Sewon, Bae, Won C., Masuda, Koichi, Chung, Christine B., Hwang, Dosik
Médium: Journal Article
Jazyk:angličtina
Vydáno: Switzerland MDPI AG 01.09.2018
Témata:
Algorithms
Back pain
Bone surgery
Clustering
correlation
graph-based segmentation
International conferences
Medical imaging
Methods
MR spine image
semi-automatic segmentation
vertebral body
La Jolla California
United States > US
Germany
graph-based segmentation
correlation
MR spine image
semi-automatic segmentation
vertebral body
ISSN:2076-3417, 2076-3417
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Shrnutí: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.
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Author Contributions: W.C.B.; K.M. and C.B.C. proposed the idea and contributed to data acquisition and performed manual segmentation; S.K. contributed to performing data analysis, algorithm construction, and writing the article; D.H. technically supported the algorithm and evaluation and also professionally reviewed and edited the paper.
ISSN:2076-3417
2076-3417
DOI:10.3390/app8091586