Automatic cardiac region-of-interest computation in cine 3D structural MRI

Cocosco, Chris A.; Netsch, Thomas; Sénégas, Julien; Bystrov, Daniel; Niessen, Wiro J.; Viergever, Max A.

doi:10.1016/j.ics.2004.03.179

Cited by 20 publications

(23 citation statements)

References 11 publications

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“…However, this manual step can be replaced by automatic methods. 19 An important step is the selection of a suited atlas. Therefore, we apply the affine pre-registration for each atlas and transfer the associated label image to the patient coordinate system to obtain the initial object boundaries.…”

Section: Cardiac Lv+rv Segmentation In Cine Mrimentioning

confidence: 99%

Joint multi-object registration and segmentation of left and right cardiac ventricles in 4D cine MRI

et al. 2014

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The diagnosis of cardiac function based on cine MRI requires the segmentation of cardiac structures in the images, but the problem of automatic cardiac segmentation is still open, due to the imaging characteristics of cardiac MR images and the anatomical variability of the heart.In this paper, we present a variational framework for joint segmentation and registration of multiple structures of the heart. To enable the simultaneous segmentation and registration of multiple objects, a shape prior term is introduced into a region competition approach for multi-object level set segmentation. The proposed algorithm is applied for simultaneous segmentation of the myocardium as well as the left and right ventricular blood pool in short axis cine MRI images. Two experiments are performed: first, intra-patient 4D segmentation with a given initial segmentation for one time-point in a 4D sequence, and second, a multi-atlas segmentation strategy is applied to unseen patient data. Evaluation of segmentation accuracy is done by overlap coefficients and surface distances. An evaluation based on clinical 4D cine MRI images of 25 patients shows the benefit of the combined approach compared to sole registration and sole segmentation. PURPOSECardiovascular diseases are reported to be the leading cause of death in the western countries. 1 Cine MRI has been proven to be an accurate and reproducible imaging modality for the diagnosis of cardiac function, 2 however, the computation of quantitative measures as ventricle volumes, masses and ejection fraction requires the segmentation of the left (LV) and right (RV) ventricles from cine MR images. Despite of numerous automatic cardiac segmentation approaches in the literature, the problem of LV+RV segmentation in MRI is still open, due to the characteristics of cardiac MR images and to the great variability of the images among patients. 3 Cardiac segmentation algorithms for 4D cine MRI images include intensity-based methods, deformable models, statistical shape and appearance models and atlas-patient registration methods (see Petitjean et al. 3 for an overview). Different 3D and 4D registration-based algorithms have been reported to show good correlations to manual ground-truth segmentations. 4-7 However, the variability of shape and appearance of the heart in MRI is a challenge for registration-based heart segmentation approaches.In this paper, we propose an algorithm for the simultaneous level set-based segmentation and non-linear registration of multiple objects in two images. Some approaches for joint registration and segmentation can be found in the literature, but most of these methods are designed for the segmentation of a single object 8-10 or are constrained to linear registration. 11 In our approach, each object to segment is represented by a level set function and region competition 12 is used as segmentation approach. The joint segmentation and registration problem is solved by minimizing a variational functional that incorporates prior shape knowledge, prior intensity informat...

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Section: Cardiac Lv+rv Segmentation In Cine Mrimentioning

confidence: 99%

Joint multi-object registration and segmentation of left and right cardiac ventricles in 4D cine MRI

et al. 2014

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“…Note that a preprocessing inspired of [13] has been used to create a region of interest around the ventricles. No postprocessing has been allowed after automatic segmentation.…”

Section: Experimental Protocolmentioning

confidence: 99%

Joint segmentation of right and left cardiac ventricles using multi-label graph cut

Grosgeorge

Petitjean

Ruan

2014

2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI)

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Segmenting the left ventricle (LV) and the right ventricle (RV) in magnetic resonance (MR) images is required for cardiac function assessment. In particular, the segmentation of the RV is a difficult task due to low contrast with surrounding tissues and high shape variability. To overcome these problems, we introduce a fully automatic segmentation method based on multi-label graph cuts, that makes use of a probabilistic shape model. The shape model is obtained by merging several atlases after their non-rigid registration on the unseen image. This prior is then incorporated into the multi-label graph cut framework in order to guide the segmentation. Our automatic segmentation method has been applied on 754 MR images. We show that encouraging results can be obtained for this challenging application.

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“…summation of difference). This is because the heart is almost the only moving organ in all images [7]. …”

Section: Calculating the Feature Map (Summation Of Difference Map)mentioning

confidence: 99%

“…There are different techniques used in localizing the ROI of the cardiac structure, some of these techniques depend on localizing the left ventricle cavity by clustering [10], and other one used the variation of standard deviation of the heart in stack of images due to the muscle movement of the left ventricle during contraction [11,7].…”

Section: Introductionmentioning

confidence: 99%

Region of Interest Localization of Cardiac Structure from Cine MRI Images

Hadhoud

Eladawy

Farag

2012

2012 Ninth International Conference on Information Technology - New Generations

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In this paper, we present a new method for localizing the position of the heart from Cine MRI images. This method depends on calculating the summation of the difference between images of a specific slice that we work on it, then applying thresholding on the resulting image to get the region of interest. To validate our method, we compare between our results and results of another method used in localizing the position of the heart which depends on the standard deviation. We worked on images of 14 different patients, come from STACOM database. We achieved by this method good results in determining the position of the heart. This method has different advantages like: it works on different contrast images, fast, and fully automatic.

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Automatic cardiac region-of-interest computation in cine 3D structural MRI

Cited by 20 publications

References 11 publications

Joint multi-object registration and segmentation of left and right cardiac ventricles in 4D cine MRI

Joint multi-object registration and segmentation of left and right cardiac ventricles in 4D cine MRI

Joint segmentation of right and left cardiac ventricles using multi-label graph cut

Region of Interest Localization of Cardiac Structure from Cine MRI Images

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