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检索条件"机构=Image Analytics and Informatics"

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PRECISE SEGMENTATION OF THE LEFT ATRIUM IN C-ARM CT VOLUMES WITH APPLICATIONS TO ATRIAL FIBRILLATION ABLATION

PRECISE SEGMENTATION OF THE LEFT ATRIUM IN C-ARM CT VOLUMES ...

引用

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

作者： Zheng, Yefeng John, Matthias Boese, Jan Comaniciu, Dorin Image Analytics and Informatics Siemens Corporate Research USA

ISBN: (纸本)9781457718588

A patient-specific left atrium (LA) model extracted from intra-operative C-arm CT plays an important role in planning for transcatheter left atrial fibrillation ablation. Overlaying the LA model onto 2D fluoroscopic images provides valuable visual guidance during the intervention. However, automatic segmentation of the LA, together with the left atrial appendage (LAA) and the pulmonary vein (PV) trunks, is challenging due to the large structural variations and imaging artifacts. In this paper we exploit a part based LA model to handle the structural variations and different parts are then merged into a consolidated mesh. The connection region between the PV/LAA and the LA chamber is segmented precisely by enforcing both image boundary delineation accuracy and mesh smoothness. Furthermore, the boundary between parts is optimized to improve the mesh part labeling accuracy.

关键词： Left Atrium Segmentation Atrial Fibrillation Ablation C-arm CT

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ENHANCEMENT OF ORGAN OF INTEREST VIA BACKGROUND SUBTRACTION IN CONE BEAM ROTATIONAL ANGIOCARDIOGRAM

ENHANCEMENT OF ORGAN OF INTEREST VIA BACKGROUND SUBTRACTION ...

引用

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

作者： Chen, Mingqing Zheng, Yefeng Mueller, Kerstin Rohkohl, Christopher Lauritsch, Guenter Boese, Jan Comaniciu, Dorin Image Analytics and Informatics Siemens Corporate Research USA

ISBN: (纸本)9781457718588

The real time X-ray angiography based on C-arm cone beam system is the workhorse imaging modality for interventional cardiac procedures. These images are two-dimensional (2D) projections of three dimensional (3D) objects along the X-ray direction. The organ of interest (OOI), such as left ventricle (LV) endocardium, in the projection image is superimposed with other anatomical structures and often has low contrast. In this study, a novel approach is proposed to isolate the OOI in projection images by subtracting with a background image, which is generated by numerical projection of 3D tomographic image with OOI masked out. Study based on one patient and one pig image is taken. About two to three-fold increase in the contrast-to-noise ratio (CNR) is achieved for LV endocardium, compared to an unprocessed image.

关键词： Subtraction CBCT SART Reprojection Ventricle

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Determination of time-varying pressure field from phase contrast MRI data

引用

Journal of Cardiovascular Magnetic Resonance 2012年第SUPPL 1期14卷 W36-W36页

作者： Itu, Lucian M Sharma, Puneet Gulsun, Mehmet A Mihalef, Viorel Kamen, Ali Greiser, Andreas Image Analytics and Informatics Siemens Corporate Research Princeton NJ United States Siemens AG Erlangen Germany

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Automatic segmentation of the myocardium in cine MR images using deformable registration

Automatic segmentation of the myocardium in cine MR images u...

引用

2nd International Workshop on Statistical Atlases and Computational Models of the Heart: Imaging and Modelling Challenges, STACOM 2011, Held in Conjunction with MICCAI 2011

作者： Jolly, Marie-Pierre Guetter, Christoph Lu, Xiaoguang Xue, Hui Guehring, Jens Siemens Corporate Research Image Analytics and Informatics Princeton NJ United States Siemens AG Healthcare Sector Erlangen Germany

ISBN: (纸本)9783642283253

This paper proposes a system to automatically segment the left ventricle in cardiac MR cine images. Individual frames are segmented using a shortest path algorithm and temporal consistency is enforced through the backward and forward deformation fields of an inverse consistent deformable registration. In addition, a segmentation of the mitral valve plane is obtained from long axis images. This algorithm was applied to 95 datasets as part of the STACOM'11 4D LV Segmentation Challenge. We analyze the results and evaluate the strengths and weaknesses of our system. © 2012 Springer-Verlag.

关键词： Deformation

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Personalized learning-based segmentation of thoracic aorta and main branches for diagnosis and treatment planning

Personalized learning-based segmentation of thoracic aorta a...

引用

2012 9th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2012

作者： Vitanovski, Dime Ralovich, Kristof Ionasec, Razvan Zheng, Yefeng Suehling, Michael Krawtschuk, Waldemar Hornegger, Joachim Comaniciu, Dorin Image Analytics and Medical Informatics Siemens Corporate Research Princeton United States Pattern Recongnition Lab University of Erlangen Nuremeberg Germany

ISBN: (纸本)9781457718588

Coarctation of the aorta (CoA), is an obstruction of the aortic arch present in 5-8% of congenital heart diseases. For children older than a year, CoA is increasingly treated by aortic stenting instead of surgical repair. In pediatric cardiology, CMR is accepted as the standard non-invasive imaging modality to assess the aortic arch in it's entire spatial context [1]. Interpreting such 3D datasets are required to assess the underlying anatomy during both diagnosis and therapy planning phases. However this process is time consuming and varies with operator skills. Within this study we propose for the first time in our knowledge a method of automatic segmentation of the lumen of thoracic aorta and main branches. The personalized model of the aorta and the supra-aortic arteries, automatically estimated from 3D CMR data, will provide better understanding of the complexity of pathology and assist the cardiologist to choose the best treatment and timing of repair. A hierarchical framework based on robust machine-learning algorithms is proposed to estimate the personalized model parameters. Experiments throughout 212 3D CMR volumes demonstrate model estimation error of 3.24 mm and average computation time of 8 sec. combined with clinical evaluation on 32 patients. © 2012 IEEE.

关键词： Repair

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An incompressible log-domain demons algorithm for tracking heart tissue

An incompressible log-domain demons algorithm for tracking h...

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2nd International Workshop on Statistical Atlases and Computational Models of the Heart: Imaging and Modelling Challenges, STACOM 2011, Held in Conjunction with MICCAI 2011

作者： McLeod, Kristin Prakosa, Adityo Mansi, Tommaso Sermesant, Maxime Pennec, Xavier INRIA Méditerranée Asclepios Project Sophia Antipolis France Siemens Corporate Research Image Analytics and Informatics Princeton NJ United States

ISBN: (纸本)9783642283253

We describe an application of the previously proposed iLogDemons algorithm to the STACOM motion-tracking challenge data. The iLogDemons algorithm is a consistent and efficient framework for tracking left-ventricle heart tissue using an elastic incompressible non-linear registration algorithm based on the LogDemons algorithm. This method has shown promising results when applied to previous data-sets. Along with having the advantages of the LogDemons algorithm such as computing deformations that are invertible with smooth inverse, the method has the added advantage of allowing physiological constraints to be added to the deformation model. The registration is entirely performed in the log-domain with the incompressibility constraint strongly ensured and applied directly in the demons minimisation space. Strong incompressibility is ensured by constraining the stationary velocity fields that parameterise the transformations to be divergence-free in the myocardium. The method is applied to a data-set of 15 volunteers and one phantom, each with echocardiography, cine-MR and tagged-MR images. We are able to obtain reasonable results for each modality and good results for echocardiography images with respect to quality of the registration and computed strain curves. © 2012 Springer-Verlag.

关键词： Echocardiography

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Precise segmentation of multiple organs in CT volumes using learning-based approach and information theory

Precise segmentation of multiple organs in CT volumes using ...

引用

15th International Conference on Medical image Computing and Computer-Assisted Intervention, MICCAI 2012

作者： Lu, Chao Zheng, Yefeng Birkbeck, Neil Zhang, Jingdan Kohlberger, Timo Tietjen, Christian Boettger, Thomas Duncan, James S. Zhou, S. Kevin Image Analytics and Informatics Siemens Corporate Research PrincetonNJ United States Computed Tomography Siemens Healthcare Forchheim Germany School of Engineering & Applied Science Yale University New HavenCT United States

ISBN: (纸本)9783642334177

In this paper, we present a novel method by incorporating information theory into the learning-based approach for automatic and accurate pelvic organ segmentation (including the prostate, bladder and rectum). We target 3D CT volumes that are generated using different scanning protocols (e.g., contrast and non-contrast, with and without implant in the prostate, various resolution and position), and the volumes come fromlargely diverse sources (e.g., diseased in different organs). Three key ingredients are combined to solve this challenging segmentation problem. First, marginal space learning (MSL) is applied to efficiently and effectively localize the multiple organs in the largely diverse CT volumes. Second, learning techniques, steerable features, are applied for robust boundary detection. This enables handling of highly heterogeneous texture pattern. Third, a novel information theoretic scheme is incorporated into the boundary inference process. The incorporation of the Jensen-Shannon divergence further drives the mesh to the best fit of the image, thus improves the segmentation performance. The proposed approach is tested on a challenging dataset containing 188 volumes from diverse sources. Our approach not only produces excellent segmentation accuracy, but also runs about eighty times faster than previous state-of-the-art solutions. The proposed method can be applied to CT images to provide visual guidance to physicians during the computer-aided diagnosis, treatment planning and image-guided radiotherapy to treat cancers in pelvic region. © Springer-Verlag Berlin Heidelberg 2012.

关键词： Computer aided diagnosis

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Precise segmentation of the left atrium in C-arm CT volumes with applications to atrial fibrillation ablation

Precise segmentation of the left atrium in C-arm CT volumes ...

引用

IEEE International Symposium on Biomedical Imaging

作者： Yefeng Zheng Matthias John Jan Boese Dorin Comaniciu Image Analytics and Informatics Siemens AG Corporate Research and Development USA Healthcare Sector Siemens AG Germany

ISBN: (纸本)9781457718571

关键词： image segmentation Computed tomography Shape Computational modeling Accuracy Labeling Robustness

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Model-based fusion of CT and non-contrasted 3D C-arm CT: Application to transcatheter valve therapies

Model-based fusion of CT and non-contrasted 3D C-arm CT: App...

引用

2012 9th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2012

作者： Grbic, Sasa Gesell, Christian Lonasec, Razvan John, Matthias Boese, Jan Hornegger, Joachim Navab, Nassir Cotnaniciu, Dorin Image Analytics and Medical Informatics Siemens Corporate Research Princeton United States Computer Aided Medical Procedures Technical University Munich Germany Siemens AG Healthcare Sector Forchheim Germany Pattern Recongnition Lab University of Erlangen Nuremeberg Germany

ISBN: (纸本)9781457718588

In recent years transcatheter valve therapies are beginning to replace invasive surgical procedures. As there is no direct view and access to the affected anatomy advanced imaging techniques such as 3D rotational angiography (C-arm CT) and real-time fluoroscopy are used for intra-operative guidance. However, intra-operative modalities have limited image quality of the soft tissue and a reliable assessment of the cardiac anatomy can only be made by injecting contrast agent, which is harmful to the patient and requires complex acquisition protocols. We propose a novel method to align pre-operative and intra-operative data by using surrogate anatomical structures which are visible in both modalities without adding contrast agent. The trachea bifurcation model is used as a surrogate structure and the model parameters are estimated using robust machine learning algorithms. High quality patient specific models can be extracted from the pre-operative CT and mapped to the intra-operative 3D C-arm CT for guidance. In addition we learn a weighted mapping function for the trachea bifurcation model extracted from the pre-operative and intra-operative images which minimizes the mapping error in respect to the anatomy of interest. Experiments performed on 28 patient pairs of CT and contrasted 3D C-arm CT data sets assure an accuracy of the mapped aortic valve model of 9.08 × 7.31 deg and 7.5 × 7 3.22mm. © 2012 IEEE.

关键词： Learning algorithms

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Personalized learning-based segmentation of thoracic aorta and main branches for diagnosis and treatment planning

Personalized learning-based segmentation of thoracic aorta a...

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IEEE International Symposium on Biomedical Imaging

作者： Dime Vitanovski Kristof Ralovich Razvan Ionasec Yefeng Zheng Michael Suehling Waldemar Krawtschuk Joachim Hornegger Dorin Comaniciu Image Analytics and Medical Informatics Siemens AG Corporate Research and Development Princeton USA Pattern Recongnition Laboratory University of Erlangen-Nuremberg Germany

Coarctation of the aorta (CoA), is an obstruction of the aortic arch present in 5-8% of congenital heart diseases. For children older than a year, CoA is increasingly treated by aortic stenting instead of surgical repair. In pediatric cardiology, CMR is accepted as the standard non-invasive imaging modality to assess the aortic arch in it's entire spatial context [1]. Interpreting such 3D datasets are required to assess the underlying anatomy during both diagnosis and therapy planning phases. However this process is time consuming and varies with operator skills. Within this study we propose - for the first time in our knowledge - a method of automatic segmentation of the lumen of thoracic aorta and main branches. The personalized model of the aorta and the supra-aortic arteries, automatically estimated from 3D CMR data, will provide better understanding of the complexity of pathology and assist the cardiologist to choose the best treatment and timing of repair. A hierarchical framework based on robust machine-learning algorithms is proposed to estimate the personalized model parameters. Experiments throughout 212 3D CMR volumes demonstrate model estimation error of 3.24 mm and average computation time of 8 sec. combined with clinical evaluation on 32 patients.

关键词： Biomedical imaging Arteries Detectors Computational modeling image segmentation Heart Valves

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