Vertebral Body Segmentation of Spine MR Images Using Superpixels

2015 ◽  
Author(s):  
Paulo Duarte Barbieri ◽  
Glauco Vitor Pedrosa ◽  
Agma Juci Machado Traina ◽  
Marcello Henrique Nogueira-Barbosa
Keyword(s):  
Vertebral Body ◽  
Mr Images ◽  
Body Segmentation ◽  
Spine Mr ◽  
Vertebral Body Segmentation

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

2018 ◽  
Vol 8 (9) ◽  
pp. 1586 ◽  
Author(s):  
Sewon Kim ◽  
Won Bae ◽  
Koichi Masuda ◽  
Christine Chung ◽  
Dosik Hwang
Keyword(s):  
Lumbar Spine ◽  
Vertebral Body ◽  
Automatic Segmentation ◽  
Dice Similarity Coefficient ◽  
Mr Images ◽  
Segmentation Accuracy ◽  
Body Segmentation ◽  
Spine Mr ◽  
Vertebral Bodies ◽  
Automatic Algorithm

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.

Deep Learning based Vertebral Body Segmentation with Extraction of Spinal Measurements and Disorder Disease Classification

2022 ◽  
Vol 71 ◽  
pp. 103230
Author(s):  
Rao Farhat Masood ◽  
Imtiaz Ahmad Taj ◽  
Muhammad Babar Khan ◽  
Muhammad Asad Qureshi ◽  
Taimur Hassan
Keyword(s):  
Deep Learning ◽  
Vertebral Body ◽  
Disease Classification ◽  
Body Segmentation ◽  
Vertebral Body Segmentation

scholarly journals Cube-Cut: Vertebral Body Segmentation in MRI-Data through Cubic-Shaped Divergences

PLoS ONE ◽  
2014 ◽  
Vol 9 (4) ◽  
pp. e93389 ◽  
Author(s):  
Robert Schwarzenberg ◽  
Bernd Freisleben ◽  
Christopher Nimsky ◽  
Jan Egger
Keyword(s):  
Vertebral Body ◽  
Body Segmentation ◽  
Vertebral Body Segmentation

3D Vertebral Body Segmentation Using Shape Based Graph Cuts

2010 ◽  
Author(s):  
Melih S. Aslan ◽  
Asem Ali ◽  
Aly A. Farag ◽  
Ham Rara ◽  
Ben Arnold ◽  
...  
Keyword(s):  
Vertebral Body ◽  
Graph Cuts ◽  
Body Segmentation ◽  
Vertebral Body Segmentation
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