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Medical Imaging 2004 Conference

作者： Sénégas, J Cocosco, CA Netsch, T Philips Res Labs D-22335 Hamburg Germany

ISBN: (纸本)0819452831

The quantitative analysis of cardiac cine MRI sequences requires automated, robust, and fast image processing algorithms for the 4D (3D + time) segmentation of the heart chambers. The use of shape models has proven efficient in extracting the cardiac volumes for single phases, but less attention has been focused on incorporating prior knowledge about the cardiac motion. To explicitly address the temporal aspect of the segmentation problem, this paper proposes a full Bayesian model, where the prior information is represented by a cardiac shape and motion model. In this framework, the solution of the segmentation is defined by means of a probability distribution over the parameters of the space-time problem. The computed solution, obtained by means of sequential Monte Carlo techniques, has the advantage of being both spatially and temporally coherent. Furthermore, the method does not require any particular representation of the shape or of the motion model: it is therefore generic and highly flexible.

关键词： functional MR imaging cine cardiac MR sequence cardiac motion model-based segmentation Bayesian model sequential Monte Carlo

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model-based segmentation of multimodal images

Model-based segmentation of multimodal images

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12th International Conference on Computer Analysis of Images and Patterns

作者： Hong, Xin McClean, Sally Scotney, Bryan Morrow, Philip Univ Ulster Sch Comp & Informat Engn Cromore Rd Coleraine BT52 1SA Londonderry North Ireland

ISBN: (纸本)9783540742715

This paper proposes a model-based method for intensity-based segmentation of images acquired from multiple modalities. Pixel intensity within a modality image is represented by a univariate Gaussian distribution mixture in which the components correspond to different segments. The proposed Multi-Modality Expectation-Maximization (MMEM) algorithm then estimates the probability of each segment along with parameters of the Gaussian distributions for each modality by maximum likelihood using the Expectation-Maximization (EM) algorithm. Multimodal images are simultaneously involved in the iterative parameter estimation step. Pixel classes are determined by maximising a posteriori probability contributed from all multimodal images. Experimental results show that the method exploits and fuses complementary information of multimodal images. segmentation can thus be more precise than when using single-modality images.

关键词： data fusion multimodal images model-based segmentation Gaussian mixture maximum likelihood EM algorithm

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model-based segmentation OF THE LEFT MAIN CORONARY BIFURCATION FROM 2D ANGIOGRAMS

MODEL-BASED SEGMENTATION OF THE LEFT MAIN CORONARY BIFURCATI...

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9th IEEE International Symposium on Biomedical Imaging (ISBI) - From Nano to Macro

作者： Lacroix, Romain Florent, Raoul Auvray, Vincent Philips Healthcare Medisys Res Lab F-92156 Suresnes France

ISBN: (纸本)9781457718588

This paper deals with the identification of the left main coronary bifurcation (LMB) in 2D angiograms. We resort to a generic 3D model of the LMB that is projected in the proper angulation to retrieve the expected 2D skeleton of the LMB in the considered image. We derive from it an efficient discriminating filter that allows detecting the LMB, while being robust to anatomical variations. We demonstrate the potential of the method over a large database of 120 angiographic sequences from 15 patients. We also propose a method extension that exploits not only the present angiography, but also the previous ones, as acquired in the course of the exam.

关键词： Coronary arteries model-based segmentation

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segmentation and quantification of the aortic arch using joint 3D model-based segmentation and elastic image registration

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MEDICAL IMAGE ANALYSIS 2012年第6期16卷 1187-1201页

作者： Biesdorf, Andreas Rohr, Karl Feng, Duan von Tengg-Kobligk, Hendrik Rengier, Fabian Boeckler, Dittmar Kauczor, Hans-Ulrich Woerz, Stefan Heidelberg Univ Dept Bioinformat & Funct Genom Biomed Comp Vis Grp BIOQUANTIPMB D-6900 Heidelberg Germany DKFZ Heidelberg Heidelberg Germany Heidelberg Univ Dept Diagnost & Intervent Radiol D-6900 Heidelberg Germany Heidelberg Univ Dept Vasc & Endovasc Surg D-6900 Heidelberg Germany

Accurate quantification of the morphology of vessels is important for diagnosis and treatment of cardiovascular diseases. We introduce a new joint segmentation and registration approach for the quantification of the aortic arch morphology that combines 3D model-based segmentation with elastic image registration. With this combination, the approach benefits from the robustness of model-based segmentation and the accuracy of elastic registration. The approach can cope with a large spectrum of vessel shapes and particularly with pathological shapes that deviate significantly from the underlying model used for segmentation. The performance of the approach has been evaluated on the basis of 3D synthetic images, 3D phantom data, and clinical 3D CTA images including pathologies. We also performed a quantitative comparison with previous approaches. (c) 2012 Elsevier B.V. All rights reserved.

关键词： model-based segmentation Elastic image registration Parametric intensity model Vessel segmentation Aortic arch

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The role of model-based segmentation in the recovery of volumetric parts from range data

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 1997年第3期19卷 259-267页

作者： Dickinson, SJ Metaxas, D Pentland, A RUTGERS STATE UNIV RUTGERS CTR COGNIT SCINEW BRUNSWICKNJ 08903 UNIV PENN DEPT COMP & INFORMAT SCIPHILADELPHIAPA 19104 MIT MEDIA LABVIS & MODELING GRPCAMBRIDGEMA 02139

We present a method for segmenting and estimating the shape of 3D objects from range data. The technique uses model views, or aspects, to constrain the fitting of deformable models to range data. based on an Initial region segmentation of a range image, regions are grouped into aspects corresponding to the volumetric parts that make up an object. The qualitative segmentation of the range image into a set of volumetric parts not only captures the coarse shape of the parts, but qualitatively encodes the orientation of each part through its aspect. Knowledge of a part's coarse shape, its orientation, as well as the mapping between the faces in its aspect and the surfaces on the part provides strong constraints on the fitting of a deformable model (supporting both global and local deformations) to the data. Unlike previous work in physics-based deformable model recovery from range data, the technique does not require presegmented data. Furthermore, occlusion is handled at segmentation time and does not complicate the fitting process, as only 3D points known to belong to a part participate in the fitting of a model to the part. We present the approach in detail and apply it to the recovery of objects from range data.

关键词： shape recovery range data volumetric parts model-based segmentation object recognition deformable superquadrics

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Validation of a model-based segmentation approach to propagating normal anatomic regions of interest through the 10 phases of respiration

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INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS 2008年第3期71卷 900-906页

作者： Ezhil, Muthuveni Starkschall, George Mohan, Radhe Cox, James Komaki, Ritsuko Univ Texas Houston MD Anderson Canc Ctr Dept Radiat Phys Houston TX 77030 USA Univ Texas Houston MD Anderson Canc Ctr Dept Radiat Oncol Houston TX 77030 USA

Purpose: To validate a model-based segmentation (MBS) algorithm in a commercial radiation treatment planning system for use in propagating the contours of normal anatomic regions of interest (ROIs) through the respiratory phases that constitute a four-dimensional (4D) computed tomography (CT) image data set. Methods and Materials: The 41) CT data sets for 12 patients treated for non-small-cell lung cancer were acquired. Five ROIs were selected for delineation: right and left lungs, spinal cord, heart, and esophagus. These ROIs;were manually delineated on the CT data set corresponding to the end-inspiration respiratory phase (0%). An MBS algorithm implemented on the treatment planning system propagated the ROIs sequentially through the respiratory phases that constituted the 4D CT data sets, concluding with the 0% phase data set, which was propagated from the 90 % phase data set. The propagated ROIs on the 0 % phase were compared with the original ROIs on that phase by using visual assessment and a quantitative measure of coincidence. Results: Acceptable propagation accuracy within 1 mm of uncertainty was achieved for lungs and spinal cord. Propagation of the heart produced slightly larger contours that were similar to interphysician variations in contouring the heart. The esophagus was poorly propagated because of lack of tissue contrast and definitive shape. Conclusions: The MBS propagation is a promising tool for efficiently propagating contours through the different phases of respiration. However, propagating the esophagus through this technique may be difficult because of the lack of definitive shape and clearer boundaries from surrounding tissue. (C) 2008 Elsevier Inc.

关键词： model-based segmentation 4D CT images respiratory phases propagation of contours

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A snake for model-based segmentation of biomedical images

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PATTERN RECOGNITION LETTERS 1997年第14期18卷 1529-1538页

作者： Pardo, JM Cabello, D Heras, J Univ Santiago de Compostela Fac Fis Dept Elect & Computac Santiago De Compostela 15706 A Coruna Spain Univ Santiago de Compostela Hosp Xeral Galicia Serv Ciruxia Ortoped Santiago De Compostela Spain

In this work we present a snake based approach for the segmentation of images of computerized tomography (CT) scans, We introduce a new term for the internal energy and another one for external energy which solve common problems associated with classical snakes in this type of images. A simplified minimizing method is also presented. (C) 1997 Elsevier Science B.V.

关键词： deformable models snakes model-based segmentation computerized tomography (CT) images

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A FEATURE-based APPROACH FOR REFINEMENT OF model-based segmentation OF LOW CONTRAST STRUCTURES

A FEATURE-BASED APPROACH FOR REFINEMENT OF MODEL-BASED SEGME...

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33rd Annual International Conference of the IEEE Engineering-in-Medicine-and-Biology-Society (EMBS)

作者： Qazi, Arish A. Kim, John Jaffray, David A. Pekar, Vladimir Princess Margaret Hosp Toronto ON M4X 1K9 Canada Philips Res Markham ON Canada

ISBN: (纸本)9781424441228

Accuracy and robustness are fundamental requirements of any automated method used for segmentation of medical images. model-based segmentation (MBS) is a well established technique, where uncertainties in image content can be to a certain extent compensated by the use of prior shape information. This approach is, however, often problematic in cases where image information does not allow for generating a strong feature response, one example being soft tissue organs in CT data, which typically appear in low contrast. In this paper, we enhance our recently proposed framework for voxel classification-based refinement of MBS using a level-set segmentation technique with shape priors. We also introduce a novel feature weighting methodology that improves the performance of the classifier, demonstrating results superior to the previous feature selection method. Results of fully automated segmentation of low contrast organs in head and neck CT are presented. Compared to our previous approach, we have achieved an increase of up to 22% in segmentation accuracy.

关键词： model-based segmentation classification radiation therapy planning level-sets feature weighting

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Feature-driven model-based segmentation

Feature-driven Model-Based Segmentation

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Conference on Medical Imaging 2011 - Image Processing

作者： Qazi, Arish A. Kim, John Jaffray, David A. Pekar, Vladimir Princess Margaret Hosp Radiat Med Program Toronto ON M4X 1K9 Canada Philips Res North Amer Markham ON Canada

ISBN: (纸本)9780819485045

The accurate delineation of anatomical structures is required in many medical image analysis applications. One example is radiation therapy planning (RTP), where traditional manual delineation is tedious, labor intensive, and can require hours of clinician's valuable time. Majority of automated segmentation methods in RTP belong to either model-based or atlas-based approaches. One substantial limitation of model-based segmentation is that its accuracy may be restricted by the uncertainties in image content, specifically when segmenting low-contrast anatomical structures, e. g. soft tissue organs in computed tomography images. In this paper, we introduce a non-parametric feature enhancement filter which replaces raw intensity image data by a high level probabilistic map which guides the deformable model to reliably segment low-contrast regions. The method is evaluated by segmenting the submandibular and parotid glands in the head and neck region and comparing the results to manual segmentations in terms of the volume overlap. Quantitative results show that we are in overall good agreement with expert segmentations, achieving volume overlap of up to 80%. Qualitatively, we demonstrate that we are able to segment low-contrast regions, which otherwise are difficult to delineate with deformable models relying on distinct object boundaries from the original image data.

关键词： model-based segmentation probability maps voxel classification radiation therapy head and neck cancer

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A statistical shape model of the heart and its application to model-based segmentation

A statistical shape model of the heart and its application t...

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Medical Imaging 2007 Conference

作者： Ordas, Sebastian Oubel, Estanislao Leta, Ruben Carreras, Francesc Frangi, Alejandro F. Pompeu Fabra Univ CILab Pg Circumval Lacio 8 Barcelona Spain Hosp Santa Creu & Sant Pau Cardiol Serv Barcelona Spain

ISBN: (纸本)9780819466297

In the present paper we describe the automatic construction of a statistical shape model of the whole heart built from a training set of 100 Multi-Slice Computed Tomography (MSCT) studies of pathologic and asymptomatic patients, including 15 (temporal) cardiac phases each. With these data sets we were able to build a compact and representative shape model of both inter-subject and temporal variability. A practical limitation in building statistical shape models, and in particular point distribution models (PDM), is the manual delineation of the training set. A key advantage of the proposed method is to overcome this limitation by not requiring them. Another one is the use of MSCT images, which thanks to their excellent anatomical depiction, have allowed for a realistic heart representation, including the four chambers and connected vasculature. The generalization ability of the shape model permits its deformation to unseen anatornies with an acceptable accuracy. Moreover, its compactness allows for having a reduced set of parameters to describe the modeled population. By varying these parameters, the statistical model can generate a set of valid examples. This is especially useful for the generation of synthetic populations of cardiac shapes, that may correspond e.g. to healthy or diseased cases. Finally, an illustrative example of the use of the constructed shape model for cardiac segmentation is provided.

关键词： statistical shape model active shape model model-based segmentation computational heart anatomy heart atlas

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