Automated 3D heart segmentation by search rays for building individual conductor models

Kim, Jaeil; Kim, Seokyeol; Kim, Ki Woong; Park, Jinah

doi:10.1117/12.813880

Cited by 2 publications

(3 citation statements)

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“…The estimated source power by the spatial filtering is generally localized adjacent to the sensors and centered of the object. The depthnormalization by (9) compensates the power of deep sources, but the spatial filtering results tend to still be localized close to the sensors and centered of the object. On the other hand, the coherence mapping method is to compare the similarity of waveforms derived at between the pivot point of the heart and the whole points.…”

Section: B Coherence Mappingmentioning

confidence: 97%

“…First, a complex segmentation process from anatomical imaging modalities such as X-ray computed tomography (CT) or magnetic resonance imaging (MRI) is required. Although several research groups have suggested an automated segmentation method of cardiac compartments to advance the manual segmentation, the implementation of imaging techniques is inevitable [6]- [9]. Second, patients are exposed to an excessive radiation in X-ray or high magnetic field in MRI.…”

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confidence: 99%

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Three-Dimensional Reconstruction of a Cardiac Outline by Magnetocardiography

Kim

Lim

et al. 2015

IEEE Trans. Biomed. Eng.

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A 3-D cardiac visualization is significantly helpful toward clinical applications of magnetocardiography (MCG), but the cardiac reconstruction requires a segmentation process using additional image modalities. This paper proposes a 3-D cardiac outline reconstruction method using only MCG measurement data without further imaging techniques. The cardiac outline was reconstructed by a combination of both spatial filtering and coherence mapping method. The strength of cardiac activities was first estimated by the array-gain constraint minimum-norm spatial filter with recursively updated gram matrix (AGMN-RUG). Then, waveforms were reconstructed at whole source grids, and the maximum source points of an atrium and ventricle were selected as a reference, respectively. Next, the coherence between each maximum source point and whole source points was compared by the coherence mapping method. A reconstructed cardiac outline was validated by comparing with an overlapped volume ratio when the reconstructed volume was identically matched with the original volume. The results obtained by the AGMN-RUG were compared to the results by other spatial filters. The accuracy of numerical simulation and phantom experiment by the AGMN-RUG was superior 10% and 8%, respectively, than the accuracy by the standardized low-resolution electromagnetic tomography. This accuracy demonstrated the efficacy of the proposed 3-D cardiac reconstruction method.

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Section: B Coherence Mappingmentioning

confidence: 97%

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confidence: 99%

Three-Dimensional Reconstruction of a Cardiac Outline by Magnetocardiography

Kim

Lim

et al. 2015

IEEE Trans. Biomed. Eng.

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“…Automatic heart localization, based on machine learning (Zheng, 2008), was provided. A recursive ray casting mechanism for cardiac CTA scans (Kim, 2009) and thoracic CT data (Lorenz, 2005) was also used. The method is based on intensities analysis and anatomy knowledge.…”

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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Automated 3D heart segmentation by search rays for building individual conductor models

Cited by 2 publications

References 0 publications

Three-Dimensional Reconstruction of a Cardiac Outline by Magnetocardiography

Three-Dimensional Reconstruction of a Cardiac Outline by Magnetocardiography

Automatic Human Heart Search on Ct-Scans

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