Efficient segmentation of lumbar intervertebral disc from MR images

Abstract

Segmentation of spine Magnetic Resonance Images (MRIs) has become an indispensable process in the diagnosis of lumbar disc degeneration, causing low back pain. Over the last decade of years, computer-directed diagnosis of disease, as well as computer-guided spine surgery, is based on the two-dimensional (2D) analysis of mid-sagittal slice of MRI. This work proposes an automatic strategy to extract the 3D segmentation of the normal disc as well as degenerated lumbar intervertebral discs (IVDs) from T2-weighted Turbo Spin Echo MRI of the spine using Connected Component (CC) analysis algorithm and statistical shape analysis. The challenges faced by the IVD segmentation includes (i) partial volume effects (ii) intensity inhomogeneity (iii) grey level overlap of different soft tissues. The proposed method first pre-processes the dataset and enables it for the application of the CC algorithm. The CC (subsets of pixels of the disc) of the spine MRI is extracted and apply statistical shape analysis for the refinement of the segmentation results to detect IVDs. Experimental results of the proposed method show a robust segmentation, accomplishing the dice similarity index of 92.4% and thus achieving a low error rate. Other performance measures such as Precision, Accuracy, JaccardIdx, JaccardDist, Global Consistency Error, Variation of Information, etc were also evaluated. The algorithm is evaluated quantitatively using adequate experiments on a dataset of 15 MRI scans, of different scenarios such as healthy and degenerate disc and this proposed method is verified as a promising accurate method for the automatic segmentation of IVD.