An Atlas-Based Segmentation Propagation Framework Using Locally Affine Registration – Application to Automatic Whole Heart Segmentation

Zhuang, Xiahai; Rhode, Kawal; Arridge, Simon R.; Razavi, Reza; Hill, Derek L. G.; Hawkes, David J.; Ourselin, Sébastien

doi:10.1007/978-3-540-85990-1_51

Cited by 46 publications

(45 citation statements)

References 10 publications

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“…The core of our framework is based on two contributions extending the current segmentation-propagation frameworks, namely a Locally Affine Registration Method (LARM) [20] and a nonrigid registration using free-form deformations with adaptive control point status (ACPS FFDs) [21].…”

Section: B Contribution Of This Workmentioning

confidence: 99%

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

Zhuang

Rhode

Razavi

et al. 2010

IEEE Trans. Med. Imaging

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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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Section: B Contribution Of This Workmentioning

confidence: 99%

“…We hence propose a new algorithm for inverting the transformation based on Dynamic Resampling And distance Weighting interpolation (DRAW) [20]. DRAW is generic, i.e., widely applicable to any diffeomorphic transformations.…”

Section: B Contribution Of This Workmentioning

confidence: 99%

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

Zhuang

Rhode

Razavi

et al. 2010

IEEE Trans. Med. Imaging

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189

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“…Therefore, standard segmentation methods based solely on intensity information cannot yield accurate results. To overcome these difficulties, most of the existing methods use either atlasbased techniques [11,15,18,[28][29][30] or prior geometric properties [12,17,[31][32][33], e.g., the shape of the RV is learned a priori from a finite training set. The main drawbacks of shape or atlas based approaches are (1) the requirement for large, manually segmented training sets; and (2) the high dependence of the results on the specific choice of a training set, which can lead to biases towards a particular cardiac pathology or towards the properties of normal subjects, e.g., the mean RV shape within the training data.…”

Section: Introductionmentioning

confidence: 99%

Right ventricular segmentation in cardiac MRI with moving mesh correspondences

Punithakumar

Noga

Ayed

et al. 2015

Computerized Medical Imaging and Graphics

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a b s t r a c tThis study investigates automatic propagation of the right ventricle (RV) endocardial and epicardial boundaries in 4D (3D+time) magnetic resonance imaging (MRI) sequences. Based on a moving mesh (or grid generation) framework, the proposed algorithm detects the endocardium and epicardium within each cardiac phase via point-to-point correspondences. The proposed method has the following advantages over prior RV segmentation works: (1) it removes the need for a time-consuming, manually built training set; (2) it does not make prior assumptions as to the intensity distributions or shape; (3) it provides a sequence of corresponding points over time, a comprehensive input that can be very useful in cardiac applications other than segmentation, e.g., regional wall motion analysis; and (4) it is more flexible for congenital heart disease where the RV undergoes high variations in shape. Furthermore, the proposed method allows comprehensive RV volumetric analysis over the complete cardiac cycle as well as automatic detections of end-systolic and end-diastolic phases because it provides a segmentation for each time step. Evaluated quantitatively over the 48-subject data set of the MICCAI 2012 RV segmentation challenge, the proposed method yielded an average Dice score of 0.84 ± 0.11 for the epicardium and 0.79 ± 0.17 for the endocardium. Further, quantitative evaluations of the proposed approach in comparisons to manual contours over 23 infant hypoplastic left heart syndrome patients yielded a Dice score of 0.82 ± 0.14, which demonstrates the robustness of the algorithm.

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“…Common image processing techniques used for organ segmentation include atlas-based techniques and other supervised, semi-automatic methods [5][6][7][8]. While these could be applied to the automated image analysis of adrenal glands, drawbacks include the availability of accurate deformable registration techniques, issues associated with the registration algorithms, and their inability to provide a completely automated solution.…”

Section: Introductionmentioning

confidence: 99%

Adrenal Gland Abnormality Detection Using Random Forest Classification

et al. 2013

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Adrenal abnormalities are commonly identified on computed tomography (CT) and are seen in at least 5 % of CT examinations of the thorax and abdomen. Previous studies have suggested that evaluation of Hounsfield units within a region of interest or a histogram analysis of a region of interest can be used to determine the likelihood that an adrenal gland is abnormal. However, the selection of a region of interest can be arbitrary and operator dependent. We hypothesize that segmenting the entire adrenal gland automatically without any human intervention and then performing a histogram analysis can accurately detect adrenal abnormality. We use the random forest classification framework to automatically perform a pixel-wise classification of an entire CT volume (abdomen and pelvis) into three classes namely right adrenal, left adrenal, and background. Once we obtain this classification, we perform histogram analysis to detect adrenal abnormality. The combination of these methods resulted in a sensitivity and specificity of 80 and 90 %, respectively, when analyzing 20 adrenal glands seen on volumetric CT datasets for abnormality.

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An Atlas-Based Segmentation Propagation Framework Using Locally Affine Registration – Application to Automatic Whole Heart Segmentation

Cited by 46 publications

References 10 publications

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

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

Right ventricular segmentation in cardiac MRI with moving mesh correspondences

Adrenal Gland Abnormality Detection Using Random Forest Classification

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