Improving Segmentation of the Left Ventricle Using a Two-Component Statistical Model

Zambal, Sebastian; Hladůvka, Jiřı́; Bühler, Katja

doi:10.1007/11866565_19

Cited by 30 publications

(27 citation statements)

References 12 publications

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“…AAM and ASM (active shape models) were combined in [25], with extensions to the time domain in [26]. Some of the works on LV segmentation also show results for RV segmentation using deformable models [27,28] and atlasbased methods [29].…”

Section: Knowledge-based Cardiac Segmentationmentioning

confidence: 99%

Cardiac MRI Segmentation Using Mutual Context Information from Left and Right Ventricle

Mahapatra

2013

J Digit Imaging

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In this paper, we propose a graphcut method to segment the cardiac right ventricle (RV) and left ventricle (LV) by using context information from each other. Contextual information is very helpful in medical image segmentation because the relative arrangement of different organs is the same. In addition to the conventional log-likelihood penalty, we also include a "context penalty" that captures the geometric relationship between the RV and LV. Contextual information for the RV is obtained by learning its geometrical relationship with respect to the LV. Similarly, RV provides geometrical context information for LV segmentation. The smoothness cost is formulated as a function of the learned context which helps in accurate labeling of pixels. Experimental results on real patient datasets from the STACOM database show the efficacy of our method in accurately segmenting the LV and RV. We also conduct experiments on simulated datasets to investigate our method's robustness to noise and inaccurate segmentations.

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Section: Knowledge-based Cardiac Segmentationmentioning

confidence: 99%

Cardiac MRI Segmentation Using Mutual Context Information from Left and Right Ventricle

Mahapatra

2013

J Digit Imaging

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“…Again all AAMs suffer the same limitations as the shape models with regards to the variation and building of the training sets. Strengths of AAM and ASM can be combined in a hybrid model Zambal et al, 2006;Zhang et al, 2010b).…”

Section: Introductionmentioning

confidence: 99%

Heart Localization from Magnetic Resonance Images Sequence

Farag¹,

Fakhreldin²

2012

Journal of Computer Science

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Problem statement: Heart localization is an important step in cardiac Magnetic Resonance Images (MRI) analysis. This study aims to locate the moving heart region from MRI sequence of images. Approach: The idea is to use the motion detection techniques to isolate the heart region from the background image and then apply morphological operations to construct a moving heart region mask. The mask is then applied to the MRI image to separate the Region Of Interest (ROI) that includes the heart. The K-means clustering algorithm is applied to the ROI to segment the heart walls. Results: Experimental results have shown that the performance of the proposed technique is superior to other MRI heart segmentation techniques in both complexity and accuracy. Conclusion: The proposed technique can be used as a pre segmentation step in any other future heart segmentation techniques to increase their accuracy through the localization of the moving heart region. The presented technique is fully automated technique and superior compared to other segmentation techniques.

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“…However, a recent trend in the literature [17], [19], [20] has gone some way in reassessing the approach to higher dimensional modelling and segmentation problems. Instead of creating a single model, subsections of the overall variation are captured in separate models which are then combined at segmentation-time.…”

Section: Introductionmentioning

confidence: 99%

“…In this way, the full variation of the LV is still captured, but the dimensionality of each model is lower than an equivalent single-model approach and therefore has less training requirements, simpler aligning and is better able to encapsulate the statistical variation of the training set. Zambal et al [19] describe a 3D left ventricle segmentation scheme, where the LV is modelled using a set of 2D Active Appearance Models (AAMs), each capturing a specific section of the ventricle. These models are interconnected with a separate 3D shape model that controls the global positioning and scale during segmentation.…”

Section: Introductionmentioning

confidence: 99%

A Novel Model-Based 3D${+}$Time Left Ventricular Segmentation Technique

O’Brien

Ghita

Whelan

2011

IEEE Trans. Med. Imaging

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Abstract-A common approach to model-based segmentation is to assume a top-down modelling strategy. However, this is not feasible for complex 3D+time structures such as the cardiac left ventricle due to increased training requirements, aligning difficulties and local minima in resulting models. As our main contribution, we present an alternate bottom-up modelling approach. By combining the variation captured in multiple dimensionally-targeted models at segmentation-time we create a scalable segmentation framework that does not suffer from the 'curse of dimensionality'. Our second contribution involves a flexible contour coupling technique that allows our segmentation method to adapt to unseen contour configurations outside the training set. This is used to identify the endo-and epi-cardium contours of the left ventricle by coupling them at segmentationtime, instead of at model-time. We apply our approach to 33 3D+time MRI cardiac datasets and perform comprehensive evaluation against several state-of-the-art works. Quantitative evaluation illustrates that our method requires significantly less training than state-of-the-art model-based methods, while maintaining or improving segmentation accuracy.Index Terms-active shape model (ASM), spatio-temporal left ventricle dynamics, contour coupling, ASM optimisation.

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Improving Segmentation of the Left Ventricle Using a Two-Component Statistical Model

Cited by 30 publications

References 12 publications

Cardiac MRI Segmentation Using Mutual Context Information from Left and Right Ventricle

Cardiac MRI Segmentation Using Mutual Context Information from Left and Right Ventricle

Heart Localization from Magnetic Resonance Images Sequence

A Novel Model-Based 3D${+}$Time Left Ventricular Segmentation Technique

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