Semi-automatic segmentation of vertebral bodies in volumetric MR images using a statistical shape+pose model

2014 ◽  
Author(s):  
Amin Suzani ◽  
Abtin Rasoulian ◽  
Sidney Fels ◽  
Robert N. Rohling ◽  
Purang Abolmaesumi
Keyword(s):  
Automatic Segmentation ◽  
Mr Images ◽  
Statistical Shape ◽  
Vertebral Bodies

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.

scholarly journals Combining Images and T-Staging Information to Improve the Automatic Segmentation of Nasopharyngeal Carcinoma Tumors in MR Images

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 21323-21331
Author(s):  
Mingwei Cai ◽  
Jiazhou Wang ◽  
Qing Yang ◽  
Ying Guo ◽  
Zhen Zhang ◽  
...  
Keyword(s):  
Nasopharyngeal Carcinoma ◽  
Automatic Segmentation ◽  
Mr Images

Automatic Segmentation of Bone Selective MR Images for Visualization and Craniometry of the Cranial Vault

2021 ◽  
Author(s):  
Carrie E. Zimmerman ◽  
Pulkit Khandelwal ◽  
Long Xie ◽  
Hyunyeol Lee ◽  
Hee Kwon Song ◽  
...  
Keyword(s):  
Automatic Segmentation ◽  
Cranial Vault ◽  
Mr Images

Automatic Segmentation of Intracochlear Anatomy in MR Images Using a Weighted Active Shape Model

2021 ◽  
Author(s):  
Yubo Fan ◽  
Rueben A. Banalagay ◽  
Nathan D. Cass ◽  
Jack H. Noble ◽  
Kareem O. Tawfik ◽  
...  
Keyword(s):  
Automatic Segmentation ◽  
Active Shape Model ◽  
Mr Images ◽  
Shape Model ◽  
Active Shape
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