Automatic myocardium segmentation in late-enhancement MRI

Ciofolo, Cybèle; Fradkin, Maxim; Mory, Benoît; Hautvast, Gilion; Breeuwer, Marcel

doi:10.1109/isbi.2008.4540973

Cited by 54 publications

(56 citation statements)

References 11 publications

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“…Our work is different from the aforementioned ones [1,3] in three respects: (i) we fully utilize shared information between corresponding cine and LGE images by registering the cine image to the LGE image in an affine-to-nonrigid manner, including both shape and intensity information; (ii) instead of using conventional similarity metrics such as mutual information and cross-correlation, experimentally we choose pattern intensity [9] which leads to accurate nonrigid registration of corresponding cine and LGE images; and (iii) at the finest level of segmentation, we propose to detect endocardial edges by adaptively selecting one of the two cases: normal endocardium and sub-endocardial scars, as well as, incorporate a new effective thickness constraint into the evolution scheme based on the simplex mesh [2] geometry. The rest of the paper is organized as follows.…”

Section: Introductionmentioning

confidence: 73%

“…To the best of our knowledge, there has been little research aimed at fully automatic myocardial segmentation in LGE images, and there is no commercially or publicly available automatic segmentation tool for clinical use. Most of the existing approaches utilize pre-delineated myocardial contours in the corresponding cine MRI as a priori knowledge [1,3]. Such an approach is reasonable because the patient is asked to stay still during the entire acquisition process and there are many methods available for automatic segmentation of cine MRI [5].…”

Section: Introductionmentioning

confidence: 99%

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Myocardial Segmentation of Late Gadolinium Enhanced MR Images by Propagation of Contours from Cine MR Images

Dong

Sun

Chai

et al. 2011

Lecture Notes in Computer Science

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Abstract. Automatic segmentation of myocardium in Late Gadolinium Enhanced (LGE) Cardiac MR (CMR) images is often difficult due to the intensity heterogeneity resulting from accumulation of contrast agent in infarcted areas. In this paper, we propose an automatic segmentation framework that fully utilizes shared information between corresponding cine and LGE images of a same patient. Given myocardial contours in cine CMR images, the proposed framework achieves accurate segmentation of LGE CMR images in a coarse-to-fine manner. Affine registration is first performed between the corresponding cine and LGE image pair, followed by nonrigid registration, and finally local deformation of myocardial contours driven by forces derived from local features of the LGE image. Experimental results on real patient data with expert outlined ground truth show that the proposed framework can generate accurate and reliable results for myocardial segmentation of LGE CMR images.

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Section: Introductionmentioning

confidence: 73%

Section: Introductionmentioning

confidence: 99%

Myocardial Segmentation of Late Gadolinium Enhanced MR Images by Propagation of Contours from Cine MR Images

Dong

Sun

Chai

et al. 2011

Lecture Notes in Computer Science

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“…For this reason, the template is initialized using the segmentation result that is automatically obtained in the SA images acquired in the same examination as the LA views [9]. The SA result consists in two 3D meshes representing the inner and outer myocardium walls in the stack of SA images.…”

Section: Initializationmentioning

confidence: 99%

“…al. [8] and more recently, we also proposed a new automatic approach [9]. But to the best of our knowledge, no method has yet been reported for segmenting LE LA images, which is the objective of this work.…”

Section: Introductionmentioning

confidence: 99%

Segmentation of Pathologic Hearts in Long-Axis Late-Enhancement MRI

Ciofolo

Fradkin

2008

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2008

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Abstract. We propose a new method to segment long-axis cardiac MR images acquired with a late-enhancement protocol. Detecting the myocardium boundaries is difficult in these images because healthy myocardium appears dark while the intensity of enhanced areas ranges from gray to white, depending on the myocardial damage. In this context, geometrical template deformation, alternated with the update of a damaged tissue map, allows us to include abnormal myocardium parts in the final segmentation. The template and map are initialized using short-axis images and the deformation parameters are adapted according to the type of enhancement pattern. Good segmentation results are obtained on a database of real pathologic heart images presenting various types of abnormal myocardium tissues.

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“…Very limited number of research studies [38], where the LV wall is segmented interactively using a live-wire-algorithm [38]. Elagouni et al [39] proposed a framework for pathological tissue segmentation where the LV wall is segmented using a segmentation method proposed by Ciofolo et al [40]. In this method, the LV wall is segmented based on 2D geometric template deformation and shape prior.…”

Section: Segmentation Of the LV Wall From Ce-cmrimentioning

confidence: 99%

Developing advanced mathematical models for detecting abnormalities in 2D/3D medical structures.

Elnakib¹

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DEDICATIONThis dissertation is dedicated to my mother, my father, and my wife for their love, patience, and support during the completion of this endeavor.iii ACKNOWLEDGMENTSIn the Name of Allah, the most beneficent, the most merciful. All deepest thanks are due to Allah, the compassionate for the uncountable gifts given to me.I would like to express my sincere gratitude to Professor El-Baz, my dissertation advisor, for the immeasurable amount of support and guidance he has provided throughout this study. He gave me the opportunity to work in his distinguished research group and provided me with an exciting working environment that facilitate many opportunities to develop new ideas and working on promising applications. Dr. El-Baz is always willing to give, share, and appreciate. I knew that I could always expect full credit for my accomplishments. He always supported me with novel ideas that helped me to publish my works in the top international conference and journal papers. It is worthy to mention that Professor El-Baz is a great advisor as well as a good friend who helps any Ph.D. student who works with him in high energy level. I cannot thank him enough, and shall remain grateful for all he has done. Detecting abnormalities in two-dimensional (2D) and three-dimensional (3D) medical structures is among the most interesting and challenging research areas in the medical imaging field. Obtaining the desired accurate automated quantification of abnormalities in medical structures is still very challenging. This is due to a large and constantly growing number of different objects of interest and associated abnormalities, large variations of their appearances and shapes in images, different medical imaging modalities, and associated changes of signal homogeneity and noise for each object. The main objective of this dissertation is to address these problems and to provide proper mathematical models and techniques that are capable of analyzing low and high resolution medical data and providing an accurate, automated analysis of the abnormalities in medical structures in terms of their area/volume, shape, and associated abnormal functionality.This dissertation presents different preliminary mathematical models and techniques that are applied in three case studies: (i) detecting abnormal tissue in vi the left ventricle (LV) wall of the heart from delayed contrast-enhanced cardiac magnetic resonance images (MRI), (ii) detecting local cardiac diseases based on estimating the functional strain metric from cardiac cine MRI, and (iii) identifying the abnormalities in the corpus callosum (CC) brain structure-the largest fiber bundle that connects the two hemispheres in the brain-for subjects that suffer from developmental brain disorders. For detecting the abnormal tissue in the heart, a graph-cut mathematical optimization model with a cost function that accounts for the object's visual appearance and shape is used to segment the the inner cavity. The model is further integrated with a geometric model (i.e., a fast marching...

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Automatic myocardium segmentation in late-enhancement MRI

Cited by 54 publications

References 11 publications

Myocardial Segmentation of Late Gadolinium Enhanced MR Images by Propagation of Contours from Cine MR Images

Myocardial Segmentation of Late Gadolinium Enhanced MR Images by Propagation of Contours from Cine MR Images

Segmentation of Pathologic Hearts in Long-Axis Late-Enhancement MRI

Developing advanced mathematical models for detecting abnormalities in 2D/3D medical structures.

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