NDLI: Efficient segmentation of lumbar intervertebral disc from MR images

Content Provider	IET Digital Library
Author	M, Leena Silvoster Kumar, Retnaswami Mathusoothana S.
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.
Starting Page	3076
Ending Page	3083
Page Count	8
ISSN	17519659
Volume Number	14
e-ISSN	17519667
Issue Number	Issue 13, Nov (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/14/13
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2019.0971
Journal	IET Image Processing
Publisher Date	2020-10-14
Access Restriction	Open
Rights Holder	© Crown Copyright
Subject Keyword	15 MRI Scans Automatic Segmentation Automatic Strategy Biology And Medical Computing Biomechanics Biomedical Magnetic Resonance Imaging Biomedical MRI Bone CC Algorithm Computer Vision And Image Processing Technique Computer-directed Diagnosis Computer-guided Spine Surgery Connected Component Analysis Algorithm Degenerate Disc Degenerated Lumbar Intervertebral Discs Different Soft Tissues Diseases Efficient Segmentation Healthy Disc Image Segmentation Indispensable Process IVD Segmentation Low Back Pain Low Error Rate Lumbar Disc Degeneration Lumbar Intervertebral Disc Medical Image Processing Medical Magnetic Resonance Imaging And Spectroscopy MR Image Neurophysiology Normal Disc Optical, Image And Video Signal Processing Orthopaedics Patient Diagnostic Method And Instrumentation Robust Segmentation Spectroscopy Spine Magnetic Resonance Image Spine MRI Statistical Shape Analysis Statistics Surfactant T2-weighted Turbo Spin Echo MRI Two-dimensional Analysis
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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