Automatic image‐driven segmentation of the ventricles in cardiac cine MRI

Cocosco, Chris A.; Niessen, Wiro J.; Netsch, Thomas; Vonken, Evert-Jan P.A.; Lund, Gunnar; Stork, Alexander; Viergever, Max A.

doi:10.1002/jmri.21451

Cited by 79 publications

(46 citation statements)

References 34 publications

(87 reference statements)

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“…These works deal with different strategies for approaching the segmentation task, including image-driven algorithms [10][11][12][13], probabilistic atlases [14,15], fuzzy clustering [16], deformable models [17][18][19], neural networks [20], active appearance models [21,22], anatomical-based landmarks [23], or level set and its variations [24,25]. A comprehensive review of techniques commonly used in cardiac image segmentation can be found in Kang et al [5].…”

Section: Introductionmentioning

confidence: 99%

Automatic image-based segmentation of the heart from CT scans

et al. 2014

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The segmentation of the heart is usually demanded in the clinical practice for computing functional parameters in patients, such as ejection fraction, cardiac output, peak ejection rate, or filling rate. Because of the time required, the manual delineation is typically limited to the left ventricle at the end-diastolic and end-systolic phases, which is insufficient for computing some of these parameters (e.g., peak ejection rate or filling rate). Common computer-aided (semi-)automated approaches for the segmentation task are computationally demanding, and an initialization step is frequently needed. This work is intended to address the aforementioned problems by providing an image-driven method for the accurate segmentation of the heart from computed tomography scans. The resulting algorithm is fast and fully automatic (even the region of interest is delimited without human intervention). The proposed methodology relies on image processing and analysis techniques (such as multi-thresholding based on statistical local and global parameters, mathematical morphology, and image filtering) and also on prior knowledge about the cardiac structures involved. Segmentation results are validated through the comparison with manually delineated ground truth, both qualitatively (no noticeable errors found after visual inspection) and quantitatively (mass overlapping over 90%).

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Section: Introductionmentioning

confidence: 99%

Automatic image-based segmentation of the heart from CT scans

et al. 2014

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“…Some of the works on LV segmentation also show results for RV segmentation. These include image-based approaches like thresholding [28][29][30], pixel classification approaches [31,32], deformable models [33][34][35][36], active appearance models [27,37], and atlas based methods [19,23,38].…”

Section: Introductionmentioning

confidence: 99%

Cardiac Image Segmentation from Cine Cardiac MRI Using Graph Cuts and Shape Priors

Mahapatra

2013

J Digit Imaging

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In this paper, we propose a novel method for segmentation of the left ventricle, right ventricle, and myocardium from cine cardiac magnetic resonance images of the STACOM database. Our method incorporates prior shape information in a graph cut framework to achieve segmentation. Poor edge information and large within-patient shape variation of the different parts necessitates the inclusion of prior shape information. But large interpatient shape variability makes it difficult to have a generalized shape model. Therefore, for every dataset the shape prior is chosen as a single image clearly showing the different parts. Prior shape information is obtained from a combination of distance functions and orientation angle histograms of each pixel relative to the prior shape. To account for shape changes, pixels near the boundary are allowed to change their labels by appropriate formulation of the penalty and smoothness costs. Our method consists of two stages. In the first stage, segmentation is performed using only intensity information which is the starting point for the second stage combining intensity and shape information to get the final segmentation. Experimental results on different subsets of 30 real patient datasets show higher segmentation accuracy in using shape information and our method's superior performance over other competing methods.

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“…The best result reported was 2.06 0.58 mm (mean surface distance). Cocosco et al [39] validated their segmentation results using regression of segmented volumes and reported (left ventricle) and (right ventricle). Kurkure et al [29] reported a mean Dice number of 0.855 0.123 for ventricle segmentation from cine cardiac MR images.…”

Section: Performance Of the Proposed Frameworkmentioning

confidence: 93%

A Registration-Based Propagation Framework for Automatic Whole Heart Segmentation of Cardiac MRI

Zhuang

Rhode

Razavi

et al. 2010

IEEE Trans. Med. Imaging

189

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Abstract-Magnetic resonance (MR) imaging has become a routine modality for the determination of patient cardiac morphology. The extraction of this information can be important for the development of new clinical applications as well as the planning and guidance of cardiac interventional procedures. To avoid inter-and intra-observer variability of manual delineation, it is highly desirable to develop an automatic technique for whole heart segmentation of cardiac magnetic resonance images. However, automating this process is complicated by the limited quality of acquired images and large shape variation of the heart between subjects. In this paper, we propose a fully automatic whole heart segmentation framework based on two new image registration algorithms: the locally affine registration method (LARM) and the free-form deformations with adaptive control point status (ACPS FFDs). LARM provides the correspondence of anatomical substructures such as the four chambers and great vessels of the heart, while the registration using ACPS FFDs refines the local details using a constrained optimization scheme. We validated our proposed segmentation framework on 37 cardiac MR volumes on the end-diastolic phase, displaying a wide diversity of morphology and pathology, and achieved a mean accuracy of 2.14 0.63 mm (rms surface distance) and a maximal error of 4.31 mm.Index Terms-Atlas, cardiac magnetic resonance imaging (MRI), dynamic resampling and distance weighting interpolation (DRAW), free-form deformations, image registration, inverse transformation, locally affine registration, locally affine registration method (LARM), whole heart segmentation.

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Automatic image‐driven segmentation of the ventricles in cardiac cine MRI

Cited by 79 publications

References 34 publications

Automatic image-based segmentation of the heart from CT scans

Automatic image-based segmentation of the heart from CT scans

Cardiac Image Segmentation from Cine Cardiac MRI Using Graph Cuts and Shape Priors

A Registration-Based Propagation Framework for Automatic Whole Heart Segmentation of Cardiac MRI

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