Spine Detection and Labeling Using a Parts-Based Graphical Model

Schmidt, Stefan; Kappes, Jörg Hendrik; Bergtholdt, Martin; Pekar, Vladimir; Dries, Sebastian P. M.; Bystrov, Daniel; Schnörr, Christoph

doi:10.1007/978-3-540-73273-0_11

Cited by 114 publications

(76 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A common approach for vertebra (in CT) and intervertebral disks (in MRI) is to employ a multi-stage approach. In the first stage a detector in the form of a filter [1,2], a single/multi-class classifier [3][4][5][6][7][8] or a model-based Hough transform [9] is used to detect potential vertebra candidates. As these candidates may contain many false positive responses a second stage is applied to add robustness.…”

Section: Introductionmentioning

confidence: 99%

“…In [2], a clever search is performed based on prior information through the candidates, while [1,4,5] fit a low order polynomial curve to the candidates to remove outliers. 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

141

127

View full text Add to dashboard Cite

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%.

show abstract

Section: Introductionmentioning

confidence: 99%

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

141

127

View full text Add to dashboard Cite

show abstract

“…Compared with the best cross validation results by Schmidt et al [10], the results obtained with our approach are significantly better. While a competitive but still smaller sensitivity of 97% is reported, they only achieve a position error of 5.1mm.…”

Section: Resultsmentioning

confidence: 49%

“…Furthermore, no orientation estimates are provided and the approach takes several minutes to run. Furthremore, in contrast to Schmidt et al [10], we did not perform any posterior search at the positions of missing disks which could further increase our sensitivity.…”

Section: Resultsmentioning

confidence: 97%

See 1 more Smart Citation

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

View full text Add to dashboard Cite

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.

show abstract

Significance of Artificial Intelligence in Spinal Disorder Treatment

2024

Artificial Intelligence for Bone Disorder

View full text Add to dashboard Cite

Spine Detection and Labeling Using a Parts-Based Graphical Model

Cited by 114 publications

References 13 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

Detection of 3D Spinal Geometry Using Iterated Marginal Space Learning

Significance of Artificial Intelligence in Spinal Disorder Treatment

Contact Info

Product

Resources

About