Bone and Joint Institute
Regression Segmentation for M3 Spinal Images
Document Type
Article
Publication Date
8-1-2015
Journal
IEEE Transactions on Medical Imaging
Volume
34
Issue
8
First Page
1640
Last Page
1648
URL with Digital Object Identifier
10.1109/TMI.2014.2365746
Abstract
© 2015 IEEE. Clinical routine often requires to analyze spinal images of multiple anatomic structures in multiple anatomic planes from multiple imaging modalities (M3 ). Unfortunately, existing methods for segmenting spinal images are still limited to one specific structure, in one specific plane or from one specific modality (S3). In this paper, we propose a novel approach, Regression Segmentation, that is for the first time able to segment M3 spinal images in one single unified framework. This approach formulates the segmentation task innovatively as a boundary regression problem: modeling a highly nonlinear mapping function from substantially diverse M3 images directly to desired object boundaries. Leveraging the advancement of sparse kernel machines, regression segmentation is fulfilled by a multi-dimensional support vector regressor (MSVR) which operates in an implicit, high dimensional feature space where M3 diversity and specificity can be systematically categorized, extracted, and handled. The proposed regression segmentation approach was thoroughly tested on images from 113 clinical subjects including both disc and vertebral structures, in both sagittal and axial planes, and from both MRI and CT modalities. The overall result reaches a high dice similarity index (DSI) 0.912 and a low boundary distance (BD) 0.928 mm. With our unified and expendable framework, an efficient clinical tool for M3 spinal image segmentation can be easily achieved, and will substantially benefit the diagnosis and treatment of spinal diseases.
