Focused shape models for hip joint segmentation in 3D magnetic resonance images

Chandra, Shekhar S.; Xia, Ying; Engstrom, Craig; Crozier, Stuart; Schwarz, Raphael; Fripp, Jürgen

doi:10.1016/j.media.2014.02.002

Cited by 62 publications

(53 citation statements)

References 45 publications

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“…Recent literature witnessed the successful applications of population based methods in automatic hip CT/MR image segmentation (Ehrhardte et al, 2004;Pettersson et al, 2006;Lamecker et al, 2004;Kainmueller et al, 2008;Seim et al, 2008;Kainmueller et al, 2009;Yokota et al, 2009;Schmid et al, 2011;Yokota et al, 2013;Xia et al, 2013;Chandra et al, 2014). The population based methods can be energy minimization problem.…”

Section: Introductionmentioning

confidence: 99%

MASCG: Multi-Atlas Segmentation Constrained Graph method for accurate segmentation of hip CT images

Chu

Bai

et al. 2015

Medical Image Analysis

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Section: Introductionmentioning

confidence: 99%

MASCG: Multi-Atlas Segmentation Constrained Graph method for accurate segmentation of hip CT images

Chu

Bai

et al. 2015

Medical Image Analysis

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“…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.…”

Section: Clinical Backgroundmentioning

confidence: 99%

“…Constraining the deformations using a global IVD shape morphology would limit the accuracy on abnormal IVDs with local deformations. To minimize this, a focused shape models of the posterior IVD region were used to improve the segmentation performance [7]. The region of focus was defined to encompass the area where the clinically most relevant herniation lesions occur (Fig.…”

Section: Image Segmentationmentioning

confidence: 99%

“…However, these techniques still require the presence of similar shape instances in the training set. Focused shape models of Chandra et al [7] enable to increase shape modeling sensitivity by targeting specific anatomical subregions. This strategy should hypothetically help to improve the modeling of A c c e p t e d M a n u s c r i p t 'common' anomalous (e.g.…”

Section: Introductionmentioning

confidence: 99%

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

Neubert

Fripp

Engstrom

et al. 2015

Computerized Medical Imaging and Graphics

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Please cite this article as: Aleš Neubert, Jurgen Fripp, Craig Engstrom, Daniel Schwarz, Marc-André Weber, Stuart Crozier, Statistical shape model reconstruction with sparse anomalous deformations: application to intervertebral disc herniation, Computerized Medical Imaging and Graphics (2015), http://dx.doi.org/10. 1016/j.compmedimag.2015.05.002 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. AbstractMany medical image processing techniques rely on accurate shape modeling of anatomical features. The presence of shape abnormalities challenges traditional processing algorithms based on strong morphological priors. In this work, a sparse shape reconstruction from a statistical shape model is presented. It combines the advantages of traditional statistical shape models (defining a 'normal' shape space) and previously presented sparse shape composition (providing localized descriptors of anomalies). The algorithm was incorporated into our image segmentation and classification software. Evaluation was performed on simulated and clinical MRI data from 22 sciatica patients with intervertebral disc herniation, containing 35 herniated and 97 normal discs. Moderate to high correlation (R = 0.73) was achieved * Corresponding author Room 512, General Purpose South Building (78 A c c e p t e d M a n u s c r i p t between simulated and detected herniations. The sparse reconstruction provided novel quantitative features describing the herniation morphology and MRI signal appearance in three dimensions (3D). The proposed descriptors of local disc morphology resulted to the 3D segmentation accuracy of 1.07±1.00 mm (mean absolute vertex-to-vertex mesh distance over the posterior disc region), and improved the intervertebral disc classification from 0.888 to 0.931 (area under receiver operating curve). The results show that the sparse shape reconstruction may improve computer-aided diagnosis of pathological conditions presenting local morphological alterations, as seen in intervertebral disc herniation.

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“…Yaqub et al [61] investigated the role of feature selection and weighted voting within the random forest classification framework for 3D volumetric segmentation. Chandra et al [62] integrated the weighted shape priors into the deformable models for hip joint segmentation in 3D MR images. Jiang et al [63] proposed a 3D brain tumor segmentation method by learning the population- and patient-specific feature sets of multimodal MR images.…”

Section: Introductionmentioning

confidence: 99%

A robust statistics driven volume-scalable active contour for segmenting anatomical structures in volumetric medical images with complex conditions

Wang

2016

BioMed Eng OnLine

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BackgroundAccurate segmentation of anatomical structures in medical images is a critical step in the development of computer assisted intervention systems. However, complex image conditions, such as intensity inhomogeneity, noise and weak object boundary, often cause considerable difficulties in medical image segmentation. To cope with these difficulties, we propose a novel robust statistics driven volume-scalable active contour framework, to extract desired object boundary from magnetic resonance (MR) and computed tomography (CT) imagery in 3D.MethodsWe define an energy functional in terms of the initial seeded labels and two fitting functions that are derived from object local robust statistics features. This energy is then incorporated into a level set scheme which drives the active contour evolving and converging at the desired position of the object boundary. Due to the local robust statistics and the volume scaling function in the energy fitting term, the object features in local volumes are learned adaptively to guide the motion of the contours, which thereby guarantees the capability of our method to cope with intensity inhomogeneity, noise and weak boundary. In addition, the initialization of active contour is simplified by select several seeds in the object and/or background to eliminate the sensitivity to initialization.ResultsThe proposed method was applied to extensive public available volumetric medical images with challenging image conditions. The segmentation results of various anatomical structures, such as white matter (WM), atrium, caudate nucleus and brain tumor, were evaluated quantitatively by comparing with the corresponding ground truths. It was found that the proposed method achieves consistent and coherent segmentation accuracy of 0.9246 ± 0.0068 for WM, 0.9043 ± 0.0131 for liver tumors, 0.8725 ± 0.0374 for caudate nucleus, 0.8802 ± 0.0595 for brain tumors, etc., measured by Dice similarity coefficients value for the overlap between the algorithm one and the ground truth. Further comparative experimental results showed desirable performances of the proposed method over several well-known segmentation methods in terms of accuracy and robustness.ConclusionWe proposed an approach to accurate segment volumetric medical images with complex conditions. The accuracy of segmentation, robustness to noise and contour initialization were validated on the basis of extensive MR and CT volumes.

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Focused shape models for hip joint segmentation in 3D magnetic resonance images

Cited by 62 publications

References 45 publications

MASCG: Multi-Atlas Segmentation Constrained Graph method for accurate segmentation of hip CT images

MASCG: Multi-Atlas Segmentation Constrained Graph method for accurate segmentation of hip CT images

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

A robust statistics driven volume-scalable active contour for segmenting anatomical structures in volumetric medical images with complex conditions

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