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3D Cascaded Convolutional Networks for Multi-vertebrae Segmentation

[ Vol. 16 , Issue. 3 ]

Author(s):

Liu Xia, Liu Xiao, Gan Quan and Wang Bo*   Pages 231 - 240 ( 10 )

Abstract:


Background: Automatic approach to vertebrae segmentation from computed tomography (CT) images is very important in clinical applications. As the intricate appearance and variable architecture of vertebrae across the population, cognate constructions in close vicinity, pathology, and the interconnection between vertebrae and ribs, it is a challenge to propose a 3D automatic vertebrae CT image segmentation method.

Objective: The purpose of this study was to propose an automatic multi-vertebrae segmentation method for spinal CT images.

Methods: Firstly, CLAHE-Threshold-Expansion was preprocessed to improve image quality and reduce input voxel points. Then, 3D coarse segmentation fully convolutional network and cascaded finely segmentation convolutional neural network were used to complete multi-vertebrae segmentation and classification.

Results: The results of this paper were compared with the other methods on the same datasets. Experimental results demonstrated that the Dice similarity coefficient (DSC) in this paper is 94.84%, higher than the V-net and 3D U-net.

Conclusion: Method of this paper has certain advantages in automatically and accurately segmenting vertebrae regions of CT images. Due to the easy acquisition of spine CT images. It was proven to be more conducive to clinical application of treatment that uses our segmentation model to obtain vertebrae regions, combining with the subsequent 3D reconstruction and printing work.

Keywords:

CT Image, 3D vertebra segmentation, FCN, CNN, ribs, spine.

Affiliation:

School of Automation, Harbin University of Science and Technology, Harbin 150001, School of Automation, Harbin University of Science and Technology, Harbin 150001, School of Automation, Harbin University of Science and Technology, Harbin 150001, School of Automation, Harbin University of Science and Technology, Harbin 150001

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