Labeling of Lumbar Discs Using Both Pixel- and Object-Level Features With a Two-Level Probabilistic Model

Abstract

Abstract-Backbone anatomical structure detection and labeling is a necessary step for various analysis tasks of the vertebral column. Appearance, shape and geometry measurements are necessary for abnormality detection locally at each disc and vertebrae (such as herniation) as well as globally for the whole spine (such as spinal scoliosis). We propose a two-level probabilistic model for the localization of discs from clinical magnetic resonance imaging (MRI) data that captures both pixel-and object-level features. Using a Gibbs distribution, we model appearance and spatial information at the pixel level, and at the object level, we model the spatial distribution of the discs and the relative distances between them. We use generalized expectation-maximization for optimization, which achieves efficient convergence of disc labels. Our two-level model allows the assumption of conditional independence at the pixel-level to enhance efficiency while maintaining robustness. We use a dataset that contains 105 MRI clinical normal and abnormal cases for the lumbar area. We thoroughly test our model and achieve encouraging results on normal and abnormal cases.

Automatic Localization and Identification of Vertebrae in Arbitrary Field-of-View CT Scans

“…In [3,6,8,9] the authors add prior information via graphical models, such as Hidden Markov Models (HMMs) [10], and infer the maximum a-posteriori (MAP) estimate for the vertebrae locations. In contrast, [11,12] use a fully generative model, and inference is achieved via generalized expectation-maximization, while in [7] deformable templates are used for segmentation and subsequent identification.…”

“…They either focus on a specific region, such as lumbar or thoracic, or need to modify their models based on the expected spine region. In [11,12] approximate alignment between scans is assumed. To the best of our knowledge the only work that explicitly handles arbitrary scans is [9].…”

Lumbar Spine Disc Herniation Diagnosis with a Joint Shape Model

“…The disc-labels make it plausible to separate the disc variables from the image intensities, i.e., the disc-label L variables capture the local pixel-level intensity models while the disc variables D capture the high-level geometric and contextual models of the full set of discs. The optimization is solved with a generalized expectation minimization (gEM) algorithm [15,16]. Fig.…”

“…This section explains each step: Disc Localization: The system automatically locates the middle sagittal slice from the MRI volume by index. Then our automatic method starts by a localization step that provides a point inside each disc using the two-level probabilistic model proposed by Corso et al [15,16]. Their model labels the set of discs with high level labels…”

A Novel Method VESTAL to Label Lumber Vertebrae and Intervertebral Discs

“…The second most common neurological ailment in the world after headache is lower back pain [1]. Most of the employees, specifically the youngsters and the middle aged are suffering from neck and lower back pain.…”