Automatic Segmentation of Left Atrial Geometry from Contrast-Enhanced Magnetic Resonance Images Using a Probabilistic Atlas

Karim, Rashed; Juli, Christoph; Malcolme-Lawes, Louisa; Wyn-Davies, D.; Kanagaratnam, Prapa; Peters, Nicholas S.; Rueckert, Daniel

doi:10.1007/978-3-642-15835-3_14

Cited by 14 publications

(6 citation statements)

References 14 publications

(21 reference statements)

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“…It compares favorably with the previous methods, e.g., 5 s in [4], 5-45 s in [1], 30 s in [6], and more than two hours in [5].…”

Section: Methodsmentioning

confidence: 66%

“…In practice, non-model based approaches work well on CT and MRI datasets, but they are not robust on challenging C-arm CT. The model based approaches exploit a prior shape of the LA (either in the form of an atlas [4,5] or a mean shape mesh [6]) to guide the segmentation. With a prior shape constraint, they could avoid the leakage around weak or missing boundaries, which plagues the non-model based approaches.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Zheng

John

Boese

et al. 2012

2012 9th IEEE International Symposium on Biomedical Imaging (ISBI)

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A patient-specific left atrium (LA) model extracted from intra-operative C-arm CT plays an important role in surgical planning for transcatheter left atrial fibrillation ablation. Overlaying the LA model onto 2D fluoroscopic images provides valuable visual guidance during surgery. 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 nonoptimal image quality. 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.

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“…It compares favorably with the previous methods, e.g., 5 s in [4], 5-45 s in [1], 30 s in [6], and more than two hours in [5].…”

Section: Methodsmentioning

confidence: 66%

Section: Introductionmentioning

confidence: 99%

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

Zheng

John

Boese

et al. 2012

2012 9th IEEE International Symposium on Biomedical Imaging (ISBI)

View full text Add to dashboard Cite

show abstract

“…It compares favorably with the previous methods, e.g., 5 s in [5], 5-45 s in [2], 30 s in [7], and more than two hours in [6].…”

Section: Methodsmentioning

confidence: 66%

“…In practice, non-model based approaches work well on CT and MRI datasets, but they are not robust on challenging Carm CT. The model based approaches exploit a prior shape of the LA (either in the form of an atlas [5,6] or a mean shape mesh [7]) to guide the segmentation. For example, Manzke et al [7] built a mean shape of the combined structure of the LA chamber and PVs from a training set.…”

Section: Introductionmentioning

confidence: 99%

Multi-part Left Atrium Modeling and Segmentation in C-Arm CT Volumes for Atrial Fibrillation Ablation

Zheng

Wang

John

et al. 2011

Lecture Notes in Computer Science

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Abstract. As a minimally invasive surgery to treat left atrial (LA) fibrillation, catheter based ablation uses high radio-frequency energy to eliminate potential sources of the abnormal electrical events, especially around the ostia of pulmonary veins (PV). Due to large structural variations of the PV drainage pattern, a personalized LA model is helpful to translate a generic ablation strategy to a specific patient's anatomy. Overlaying the LA model onto 2D fluoroscopic images provides valuable visual guidance during surgery. A holistic shape model is not accurate enough to represent the whole shape population of the LA. In this paper, we propose a part based LA model (including the chamber, appendage, and four major PVs) and each part is a much simpler anatomical structure compared to the holistic one. Our approach works on un-gated C-arm CT, where thin boundaries between the LA blood pool and surrounding tissues are often blurred due to the cardiac motion artifacts (which presents a big challenge compared to the highly contrasted gated CT/MRI). To avoid segmentation leakage, the shape prior is exploited in a model based approach to segment the LA parts. However, independent detection of each part is not optimal and its robustness needs further improvement (especially for the appendage and PVs). We propose to enforce a statistical shape constraint during the estimation of pose parameters (position, orientation, and size) of different parts. Our approach is computationally efficient, taking about 1.5 s to process a volume with 256 × 256 × 250 voxels. Experiments on 469 C-arm CT datasets demonstrate its robustness.

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“…However, automatic segmentation of the LA along with the left atrial appendage (LAA) and the pulmonary vein (PV) trunks is a challenge problem, due to the large structural variations in the PV drainage patterns [90] and imaging artifacts. Various approaches have been proposed for the LA segmentation, which can be classified into two categories: model-based [87] , [139] , [141] , [164] – [166] and non-model-based methods [86] , [167] . The former methods exploit the prior LA shape information to guide the segmentation, while the latter methods do not involve any prior knowledge of the LA shape.…”

Section: Computer Vision Techniques In Transcatheter Interventionmentioning

confidence: 99%

Computer Vision Techniques for Transcatheter Intervention

Zhang

Xie

Roach

2015

IEEE J. Transl. Eng. Health Med.

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Minimally invasive transcatheter technologies have demonstrated substantial promise for the diagnosis and the treatment of cardiovascular diseases. For example, transcatheter aortic valve implantation is an alternative to aortic valve replacement for the treatment of severe aortic stenosis, and transcatheter atrial fibrillation ablation is widely used for the treatment and the cure of atrial fibrillation. In addition, catheter-based intravascular ultrasound and optical coherence tomography imaging of coronary arteries provides important information about the coronary lumen, wall, and plaque characteristics. Qualitative and quantitative analysis of these cross-sectional image data will be beneficial to the evaluation and the treatment of coronary artery diseases such as atherosclerosis. In all the phases (preoperative, intraoperative, and postoperative) during the transcatheter intervention procedure, computer vision techniques (e.g., image segmentation and motion tracking) have been largely applied in the field to accomplish tasks like annulus measurement, valve selection, catheter placement control, and vessel centerline extraction. This provides beneficial guidance for the clinicians in surgical planning, disease diagnosis, and treatment assessment. In this paper, we present a systematical review on these state-of-the-art methods. We aim to give a comprehensive overview for researchers in the area of computer vision on the subject of transcatheter intervention. Research in medical computing is multi-disciplinary due to its nature, and hence, it is important to understand the application domain, clinical background, and imaging modality, so that methods and quantitative measurements derived from analyzing the imaging data are appropriate and meaningful. We thus provide an overview on the background information of the transcatheter intervention procedures, as well as a review of the computer vision techniques and methodologies applied in this area.

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Automatic Segmentation of Left Atrial Geometry from Contrast-Enhanced Magnetic Resonance Images Using a Probabilistic Atlas

Cited by 14 publications

References 14 publications

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 with applications to atrial fibrillation ablation

Multi-part Left Atrium Modeling and Segmentation in C-Arm CT Volumes for Atrial Fibrillation Ablation

Computer Vision Techniques for Transcatheter Intervention

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