Lumbar Disc Localization and Labeling with a Probabilistic Model on Both Pixel and Object Features

Corso, Jason J.; Alomari, Raja’ S.; Chaudhary, Vipin

doi:10.1007/978-3-540-85988-8_25

Cited by 70 publications

(55 citation statements)

References 10 publications

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“…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.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Glocker

Feulner

Criminisi

et al. 2012

Lecture Notes in Computer Science

138

127

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Abstract. This paper presents a new method for automatic localization and identification of vertebrae in arbitrary field-of-view CT scans. No assumptions are made about which section of the spine is visible or to which extent. Thus, our approach is more general than previous work while being computationally efficient. Our algorithm is based on regression forests and probabilistic graphical models. The discriminative, regression part aims at roughly detecting the visible part of the spine. Accurate localization and identification of individual vertebrae is achieved through a generative model capturing spinal shape and appearance. The system is evaluated quantitatively on 200 CT scans, the largest dataset reported for this purpose. We obtain an overall median localization error of less than 6mm, with an identification rate of 81%.

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Section: Introductionmentioning

confidence: 99%

“…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].…”

Section: Introductionmentioning

confidence: 99%

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

Glocker

Feulner

Criminisi

et al. 2012

Lecture Notes in Computer Science

138

127

View full text Add to dashboard Cite

show abstract

“…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.…”

Section: Proposed Methodsmentioning

confidence: 99%

“…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…”

Section: Introductionmentioning

confidence: 99%

Lumbar Spine Disc Herniation Diagnosis with a Joint Shape Model

Alomari

Corso

Chaudhary

et al. 2014

Lecture Notes in Computational Vision and Biomechanics

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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.

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“…Corso et al [2] argue that a two-level probabilistic model is required to separate pixel-level properties from object-level geometric and contextual properties. They propose a generative graphical model with latent disk variables which they solve by generalized expectation maximization (EM).…”

Section: Related Workmentioning

confidence: 99%

Detection of 3D Spinal Geometry Using Iterated Marginal Space Learning

Kelm

Zhou

Suehling

et al. 2011

Medical Computer Vision. Recognition Techniques and Applications in Medical Imaging

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Abstract. Determining spinal geometry and in particular the position and orientation of the intervertebral disks is an integral part of nearly every spinal examination with Computed Tomography (CT) and Magnetic Resonance (MR) imaging. It is particularly important for the standardized alignment of the scan geometry with the spine. In this paper, we present a novel method that combines Marginal Space Learning (MSL), a recently introduced concept for efficient discriminative object detection, with a generative anatomical network that incorporates relative pose information for the detection of multiple objects. It is used to simultaneously detect and label the intervertebral disks in a given spinal image volume. While a novel iterative version of MSL is used to quickly generate candidate detections comprising position, orientation, and scale of the disks with high sensitivity, the anatomical network selects the most likely candidates using a learned prior on the individual nine dimensional transformation spaces. Since the proposed approach is learning-based it can be trained for MR or CT alike. Experimental results based on 42 MR volumes show that our system not only achieves superior accuracy but also is the fastest system of its kind in the literature -on average, the spinal disks of a whole spine are detected in 11.5s with 98.6% sensitivity and 7.3% false positive detections. An average position error of 2.4mm and angular error of 3.9 • is achieved.

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Lumbar Disc Localization and Labeling with a Probabilistic Model on Both Pixel and Object Features

Cited by 70 publications

References 10 publications

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

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

Lumbar Spine Disc Herniation Diagnosis with a Joint Shape Model

Detection of 3D Spinal Geometry Using Iterated Marginal Space Learning

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