Optimizing GHT-Based Heart Localization in an Automatic Segmentation Chain

Saalbach, Axel; Wächter-Stehle, Irina; Kneser, Reinhard; Mollus, Sabine; Peters, Jörg; Weese, Jürgen

doi:10.1007/978-3-642-23626-6_57

Cited by 4 publications

(2 citation statements)

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“…The first step is the initialization of the barycenter of the model based on a generalized Hough transform (GHT) [9]. In a second step, the mesh is deformed parametrically, by applying individual similarity transformations for the LAPV and the trachea mesh parts to minimize the external energy E ext .…”

Section: Multi-stage Segmentationmentioning

confidence: 99%

Limited Angle C-Arm Tomography and Segmentation for Guidance of Atrial Fibrillation Ablation Procedures

Schäfer

Meyer

Bullens

et al. 2012

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012

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Abstract. Angiographic projections of the left atrium (LA) and the pulmonary veins (PV) acquired with a rotational C-arm system are used for 3D image reconstruction and subsequent automatic segmentation of the LA and PV to be used as roadmap in fluoroscopy guided LA ablation procedures. Acquisition of projections at high oblique angulations may be problematic due to increased collision danger of the detector with the right shoulder of the patient. We investigate the accuracy of image reconstruction and model based roadmap segmentation using limited angle C-arm tomography. The reduction of the angular range from 200• to 150• leads only to a moderate increase of the segmentation error from 1.5 mm to 2.0 mm if matched conditions are used in the segmentation, i.e. the model based segmentation is trained on images reconstructed with the same angular range as the test images. The minor decrease in accuracy may be outweighed by clinical workflow improvement, gained when large C-arm angulations can be avoided.

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Section: Multi-stage Segmentationmentioning

confidence: 99%

Limited Angle C-Arm Tomography and Segmentation for Guidance of Atrial Fibrillation Ablation Procedures

Schäfer

Meyer

Bullens

et al. 2012

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012

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“…The method uses existing chest X-rays and their manually marked heart boundaries as models, and estimates the heart boundary of a patient X-ray by registering the model X-rays to the patient X-ray. A heart localization method based on Computed Tomography Angiography (CTA) images and Generalized Hough Transformation (Saalbach, 2011) was proposed. The method implies templates and learning usage and sufficiently time-consuming execution.…”

Section: Introductionmentioning

confidence: 99%

Automatic Human Heart Search on Ct-Scans

Dubrovskaya¹

2019

International Conferences Interfaces and Human Computer Interaction 2019; Game and Entertainment Technologies 2019; And Compute

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Here is proposed a solution for actual problem of heart allocation on tomography data. One of the most popular tomography types used for various heart investigations is CT, thus the technology proposed here is initially developed for CT data. The main part of the algorithm consists of four stages: preliminary source volume limitation, forming hypotheses of lungs location, determination of approximate surrounding heart cube and searching for a point within the cube which is for sure inside the heart. The algorithm also includes a preliminary stage: coordinate system allocation. This stage is essential when position of a scanned patient is unknown relative to a tomograph. During the stage we find, where are left and right, top and bottom, front and back sides of human body. The stage consists also of four parts itself: definition of Ox and Oy axes and determination of Oy, Oz and Ox directions, by analyzing anatomy of a patient, with regard to standard human anatomy. The method is fast and doesn`t require any human guidance and preliminary adjustment. The whole method is convenient as automatic preprocessing for unlimited scope of problems, e.g. segmentation, surface or solid model reconstruction, etc. The proposed method provides flexible way for further local heart positioning refinement and works even for static, not timed, tomography data.

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