Fully Automatic Detection of Salient Features in 3-D Transesophageal Images

Curiale, Ariel Hernán; Haak, Alexander; Vegas-Sánchez-Ferrero, Gonzalo; Ren, Ben; Aja‐Fernández, Santiago; Bosch, Johan G.

doi:10.1016/j.ultrasmedbio.2014.07.014

Cited by 4 publications

(5 citation statements)

References 48 publications

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“…Median values were calculated to give an easier comparison with a similar article by Curiale et al., 11 in which Hough transform was used to find the mitral and aortic valves. In that paper, the same median errors were 6.3 mm and 24.8 deg, respectively.…”

Section: Discussionmentioning

confidence: 99%

“…There have been several papers on automatic placement of the standard view in both TTE and TEE images. For 2D images, automatic determination of standard views images has been done by Gao et al 7 and østvik et al, 8 and for 3D images, automatic placement of the standard view has been studied by Orderud et al 9 and Lu et al 10 on TTE images and by Curiale et al 11 on TEE images. The latter work used Hough-transforms to find circular structures and compared that to known estimates of the size and thickness of the mitral and aortic valves.…”

Section: Introductionmentioning

confidence: 99%

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Automatic alignment of standard views for transesophageal echocardiographic images

et al. 2022

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Purpose: 3D transesophageal echocardiography (TEE) has become an important modality for pre-and peri-operative imaging of valvular heart disease. TEE can give excellent visualization of valve morphology in 3D rendering. As a convention, 3D TEE images are reformatted in three standard views. We describe a method for automatic calculation of parameters needed to define the standard views from 3D TEE images using no manual input.Approach: An algorithm was designed to find the center of the mitral valve and the left ventricular outflow tract (OT). These parameters defined the three-chamber view. The problem was modeled as a state estimation problem in which a 3D model was deformed based on shape priors and edge detection using a Kalman filter. This algorithm is capable of running in real time after initialization. Results:The algorithm was validated by comparing the automatic alignments of 106 TEE images against manually placed landmarks. The median error for determining the mitral valve center was 7.1 mm, and the median error for determining the left ventricular OT orientation was 13.5 deg. Conclusion:The algorithm is an accurate tool for automating the process of finding standard views for TEE images of the mitral valve.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automatic alignment of standard views for transesophageal echocardiographic images

et al. 2022

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“…Note that an automatic method for initialization has been recently proposed by Curiale et al and tested on the same data set as used in this study (Curiale et al 2014). This automatic initialization yielded segmentation results similar to those of our manual initialization despite the fact that the generated landmark points were quite different (median Euclidean distance about 6 to 12 mm).…”

Section: Limitationsmentioning

confidence: 99%

Improved Segmentation of Multiple Cavities of the Heart in Wide-View 3-D Transesophageal Echocardiograms

Haak

Ren

Mulder

et al. 2015

Ultrasound in Medicine & Biology

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“…Several significant works in recent literature are focused on these challenging issues on MV leaflet segmentations. Curiale et al 14 presented a robust detection scheme to initialize a multicavity segmentation approach without any user interaction. Sotaquira et al 15 presented a semiautomatic method for mitral annulus and mitral leaflets segmentation using a nine-point user interaction to characterize the MV morphology.…”

Section: Introductionmentioning

confidence: 99%

“…Although these segmentation methods were reported to be effective, they suffer from issues including computational costs 16,17,19 and relatively low segmentation accuracy. 14,15 The objective of this paper is to develop an effective segmentation method for delineating diastolic MV at leaflet regions from 3-D ultrasound. To achieve this, we first present a signal dropout correction approach to recover incomplete segmentation information.…”

Section: Introductionmentioning

confidence: 99%

Signal dropout correction-based ultrasound segmentation for diastolic mitral valve modeling

et al. 2018

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Three-dimensional ultrasound segmentation of mitral valve (MV) at diastole is helpful for duplicating geometry and pathology in a patient-specific dynamic phantom. The major challenge is the signal dropout at leaflet regions in transesophageal echocardiography image data. Conventional segmentation approaches suffer from missing sonographic data leading to inaccurate MV modeling at leaflet regions. This paper proposes a signal dropout correction-based ultrasound segmentation method for diastolic MV modeling. The proposed method combines signal dropout correction, image fusion, continuous max-flow segmentation, and active contour segmentation techniques. The signal dropout correction approach is developed to recover the missing segmentation information. Once the signal dropout regions of TEE image data are recovered, the MV model can be accurately duplicated. Compared with other methods in current literature, the proposed algorithm exhibits lower computational cost. The experimental results show that the proposed algorithm gives competitive results for diastolic MV modeling compared with conventional segmentation algorithms, evaluated in terms of accuracy and efficiency.

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Fully Automatic Detection of Salient Features in 3-D Transesophageal Images

Cited by 4 publications

References 48 publications

Automatic alignment of standard views for transesophageal echocardiographic images

Automatic alignment of standard views for transesophageal echocardiographic images

Improved Segmentation of Multiple Cavities of the Heart in Wide-View 3-D Transesophageal Echocardiograms

Signal dropout correction-based ultrasound segmentation for diastolic mitral valve modeling

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