Lumbar Spine Disc Herniation Diagnosis with a Joint Shape Model

Abstract

Abstract. Lower Back Pain (LBP) is the second most common neurological ailment in the United States after the headache. It costs over $100 Billion annually in treatment and related rehabilitation costs including worker compensation. In fact, it is the most common reason for lost wages and missed work days. Degenerative Disc Disease (DDD) is the major abnormality that causes LBP. Moreover, Magnetic Resonance Imaging (MRI) test is the main clinically approved noninvasive imaging modality for the diagnosis of DDD. However, there is over 50% inter-and intra-observer variability in the MRI interpretation that urges the need for standardized mechanisms in MRI interpretation. In this paper, we propose a Computer Aided Diagnosis (CAD) System for Disc Degenerative Disease detection from clinical Magnetic Resonance Imaging (MRI). This CAD produces a reproducible and clinically accurate diagnosis of the DDD for lumbar spine. We design a classifier to automatically detect degenerated disc (also clinically known as Herniation) using shape potentials. We extract these shape potentials by jointly applying an active shape model (ASM) and a gradient vector flow snake model (GVF-snake). The ASM roughly segments the disc by the detection of a certain point distribution around the disc. Then, we use this point distribution to initialize a GVF-snake model to delineate the posterior disc segment. We then extract the set of shape potentials for our Gibbs-based classifier. The whole work flow is fully automated given the full clinical MRI. We validate our model on 65 clinical MRI cases (6 discs each) and achieve an average of 93.9% classification accuracy. Our shape-based classifier is superior in classification accuracy compared to the state-of-the-art work on this problem that reports 86% and 91% on 34 and 33 cases, respectively.

Lumbar Spine Discs Labeling Using Axial View MRI Based on the Pixels Coordinate and Gray Level Features

“…al. [7], [15] proposed a probabilistic model for automatic herniation detection work by combining the appearance and shape features of the lumbar intervertebral discs. The technique models the shape depending on both the T1-weighted and T2-weighted co-registered sagittal views for building a 2D feature image.…”

A Framework on a Computer Assisted and Systematic Methodology for Detection of Chronic Lower Back Pain Using Artificial Intelligence and Computer Graphics Technologies

“…Clinical studies have indicated that morphological characteristics of lumbar discs and their signal intensity on a patient's MRI image have close relationship to the clinical outcome [23]. To this end, computer vision and artificial intelligence algorithms can be utilised to exploit these facts by analysing the MR images, calculating appropriate image features (or feature descriptors), and classifying them to decide if any particular regions in the image belong to problematic areas.…”

“…al. [11], [29] proposed a probabilistic model for automatic herniation detection that incorporates appearance and shape features of the lumbar intervertebral discs. The technique models the shape of the disc using both the T1-weighted and T2-weighted co-registered sagittal views for building a 2 dimensional (2D) feature image.…”

Statistical shape model reconstruction with sparse anomalous deformations: Application to intervertebral disc herniation

“…Herniation detection has been performed using features derived from the appearance of the IVD in magnetic resonance imaging (MRI) (e.g. raw intensity [17], mean intensities of IVD subregions [18], textural features [18,17]) and morphological features describing the IVD shape in a two-dimensional (2D) cross-section (major and minor axis [19,18], statistical models of global shape variations [20], geodesic distance to a healthy shape space [21]). The presented output of the classification is usually a binary decision about the normality of the IVD.…”

“…The SSMs have also been a popular approach to modeling the prior shape constraints in the lumbar spine. They have been employed in the MRI segmentation of IVDs [27,28,20], vertebrae [29,28] and other musculoskeletal structures [7,30,31] but their use in CAD of IVD herniation remain largely unexplored.…”