Relevant bibliographies by topics / Echocardiography segmentation

Academic literature on the topic 'Echocardiography segmentation'

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Published: 7 July 2024
Last updated: 7 July 2024

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Journal articles on the topic "Echocardiography segmentation":

1

Liao, Minqi, Yifan Lian, Yongzhao Yao, Lihua Chen, Fei Gao, Long Xu, Xin Huang, Xinxing Feng, and Suxia Guo. "Left Ventricle Segmentation in Echocardiography with Transformer." Diagnostics 13, no. 14 (July 13, 2023): 2365. http://dx.doi.org/10.3390/diagnostics13142365.

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Left ventricular ejection fraction (LVEF) plays as an essential role in the assessment of cardiac function, providing quantitative data support for the medical diagnosis of heart disease. Robust evaluation of the ejection fraction relies on accurate left ventricular (LV) segmentation of echocardiograms. Because human bias and expensive labor cost exist in manual echocardiographic analysis, computer algorithms of deep-learning have been developed to help human experts in segmentation tasks. Most of the previous work is based on the convolutional neural networks (CNN) structure and has achieved good results. However, the region occupied by the left ventricle is large for echocardiography. Therefore, the limited receptive field of CNN leaves much room for improvement in the effectiveness of LV segmentation. In recent years, Vision Transformer models have demonstrated their effectiveness and universality in traditional semantic segmentation tasks. Inspired by this, we propose two models that use two different pure Transformers as the basic framework for LV segmentation in echocardiography: one combines Swin Transformer and K-Net, and the other uses Segformer. We evaluate these two models on the EchoNet-Dynamic dataset of LV segmentation and compare the quantitative metrics with other models for LV segmentation. The experimental results show that the mean Dice similarity of the two models scores are 92.92% and 92.79%, respectively, which outperform most of the previous mainstream CNN models. In addition, we found that for some samples that were not easily segmented, whereas both our models successfully recognized the valve region and separated left ventricle and left atrium, the CNN model segmented them together as a single part. Therefore, it becomes possible for us to obtain accurate segmentation results through simple post-processing, by filtering out the parts with the largest circumference or pixel square. These promising results prove the effectiveness of the two models and reveal the potential of Transformer structure in echocardiographic segmentation.
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Huang, Helin, Zhenyi Ge, Hairui Wang, Jing Wu, Chunqiang Hu, Nan Li, Xiaomei Wu, and Cuizhen Pan. "Segmentation of Echocardiography Based on Deep Learning Model." Electronics 11, no. 11 (May 27, 2022): 1714. http://dx.doi.org/10.3390/electronics11111714.

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In order to achieve the classification of mitral regurgitation, a deep learning network VDS-UNET was designed to automatically segment the critical regions of echocardiography with three sections of apical two-chamber, apical three-chamber, and apical four-chamber. First, an expert-labeled dataset of 153 echocardiographic videos and 2183 images from 49 subjects was constructed. Then, the convolution layer in the VGG16 network was used to replace the contraction path in the original UNet network to extract image features, and depth supervision was added to the expansion path to achieve the segmentation of LA, LV, and MV. The results showed that the Dice coefficients of LA, LV, and MV were 0.935, 0.915, and 0.757, respectively. The proposed deep learning network can achieve simultaneous and accurate segmentation of LA, LV, and MV in multi-section echocardiography, laying a foundation for quantitative measurement of clinical parameters related to mitral regurgitation.
3

Ono, Shunzaburo, Masaaki Komatsu, Akira Sakai, Hideki Arima, Mie Ochida, Rina Aoyama, Suguru Yasutomi, et al. "Automated Endocardial Border Detection and Left Ventricular Functional Assessment in Echocardiography Using Deep Learning." Biomedicines 10, no. 5 (May 6, 2022): 1082. http://dx.doi.org/10.3390/biomedicines10051082.

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Endocardial border detection is a key step in assessing left ventricular systolic function in echocardiography. However, this process is still not sufficiently accurate, and manual retracing is often required, causing time-consuming and intra-/inter-observer variability in clinical practice. To address these clinical issues, more accurate and normalized automatic endocardial border detection would be valuable. Here, we develop a deep learning-based method for automated endocardial border detection and left ventricular functional assessment in two-dimensional echocardiographic videos. First, segmentation of the left ventricular cavity was performed in the six representative projections for a cardiac cycle. We employed four segmentation methods: U-Net, UNet++, UNet3+, and Deep Residual U-Net. UNet++ and UNet3+ showed a sufficiently high performance in the mean value of intersection over union and Dice coefficient. The accuracy of the four segmentation methods was then evaluated by calculating the mean value for the estimation error of the echocardiographic indexes. UNet++ was superior to the other segmentation methods, with the acceptable mean estimation error of the left ventricular ejection fraction of 10.8%, global longitudinal strain of 8.5%, and global circumferential strain of 5.8%, respectively. Our method using UNet++ demonstrated the best performance. This method may potentially support examiners and improve the workflow in echocardiography.
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Chen, Tongwaner, Menghua Xia, Yi Huang, Jing Jiao, and Yuanyuan Wang. "Cross-Domain Echocardiography Segmentation with Multi-Space Joint Adaptation." Sensors 23, no. 3 (January 28, 2023): 1479. http://dx.doi.org/10.3390/s23031479.

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The segmentation of the left ventricle endocardium (LVendo) and the left ventricle epicardium (LVepi) in echocardiography plays an important role in clinical diagnosis. Recently, deep neural networks have been the most commonly used approach for echocardiography segmentation. However, the performance of a well-trained segmentation network may degrade in unseen domain datasets due to the distribution shift of the data. Adaptation algorithms can improve the generalization of deep neural networks to different domains. In this paper, we present a multi-space adaptation-segmentation-joint framework, named MACS, for cross-domain echocardiography segmentation. It adopts a generative adversarial architecture; the generator fulfills the segmentation task and the multi-space discriminators align the two domains on both the feature space and output space. We evaluated the MACS method on two echocardiography datasets from different medical centers and vendors, the publicly available CAMUS dataset and our self-acquired dataset. The experimental results indicated that the MACS could handle unseen domain datasets well, without requirements for manual annotations, and improve the generalization performance by 2.2% in the Dice metric.
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Wilczewska, Aleksandra, Szymon Cygan, and Jakub Żmigrodzki. "Segmentation Enhanced Elastic Image Registration for 2D Speckle Tracking Echocardiography—Performance Study In Silico." Ultrasonic Imaging 44, no. 1 (January 2022): 39–54. http://dx.doi.org/10.1177/01617346211068812.

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Although the two dimensional Speckle Tracking Echocardiography has gained a strong position among medical diagnostic techniques in cardiology, it still requires further developments to improve its repeatability and reliability. Few works have attempted to incorporate the left ventricle segmentation results in the process of displacements and strain estimation to improve its performance. We proposed the use of mask information as an additional penalty in the elastic image registration based displacements estimation. This approach was studied using a short axis view synthetic echocardiographic data, segmented using an active contour method. The obtained masks were distorted to a different degree, using different methods to assess the influence of the segmentation quality on the displacements and strain estimation process. The results of displacements and circumferential strain estimations show, that even though the method is dependent on the mask quality, the potential loss in accuracy due to the poor segmentation quality is much lower than the potential accuracy gain in cases where the segmentation performs well.
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Tuncay, V., N. Prakken, P. M. A. van Ooijen, R. P. J. Budde, T. Leiner, and M. Oudkerk. "Semiautomatic, Quantitative Measurement of Aortic Valve Area Using CTA: Validation and Comparison with Transthoracic Echocardiography." BioMed Research International 2015 (2015): 1–9. http://dx.doi.org/10.1155/2015/648283.

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Objective. The aim of this work was to develop a fast and robust (semi)automatic segmentation technique of the aortic valve area (AVA) MDCT datasets.Methods. The algorithm starts with detection and cropping of Sinus of Valsalva on MPR image. The cropped image is then binarized and seed points are manually selected to create an initial contour. The contour moves automatically towards the edge of aortic AVA to obtain a segmentation of the AVA. AVA was segmented semiautomatically and manually by two observers in multiphase cardiac CT scans of 25 patients. Validation of the algorithm was obtained by comparing to Transthoracic Echocardiography (TTE). Intra- and interobserver variability were calculated by relative differences. Differences between TTE and MDCT manual and semiautomatic measurements were assessed by Bland-Altman analysis. Time required for manual and semiautomatic segmentations was recorded.Results. Mean differences from TTE were −0.19 (95% CI: −0.74 to 0.34) cm2for manual and −0.10 (95% CI: −0.45 to 0.25) cm2for semiautomatic measurements. Intra- and interobserver variability were 8.4 ± 7.1% and 27.6 ± 16.0% for manual, and 5.8 ± 4.5% and 16.8 ± 12.7% for semiautomatic measurements, respectively.Conclusion. Newly developed semiautomatic segmentation provides an accurate, more reproducible, and faster AVA segmentation result.
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El rai, Marwa Chendeb, Muna Darweesh, and Mina Al-Saad. "Semi-Supervised Segmentation of Echocardiography Videos Using Graph Signal Processing." Electronics 11, no. 21 (October 26, 2022): 3462. http://dx.doi.org/10.3390/electronics11213462.

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Machine learning and computer vision algorithms can provide a precise and automated interpretation of medical videos. The segmentation of the left ventricle of echocardiography videos plays an essential role in cardiology for carrying out clinical cardiac diagnosis and monitoring the patient’s condition. Most of the developed deep learning algorithms for video segmentation require an enormous amount of labeled data to generate accurate results. Thus, there is a need to develop new semi-supervised segmentation methods due to the scarcity and costly labeled data. In recent research, semi-supervised learning approaches based on graph signal processing emerged in computer vision due to their ability to avail the geometrical structure of data. Video object segmentation can be considered as a node classification problem. In this paper, we propose a new approach called GraphECV based on the use of graph signal processing for semi-supervised learning of video object segmentation applied for the segmentation of the left ventricle in echordiography videos. GraphECV includes instance segmentation, extraction of temporal, texture and statistical features to represent the nodes, construction of a graph using K-nearest neighbors, graph sampling to embed the graph with small amount of labeled nodes or graph signals, and finally a semi-supervised learning approach based on the minimization of the Sobolov norm of graph signals. The new algorithm is evaluated using two publicly available echocardiography videos, EchoNet-Dynamic and CAMUS datasets. The proposed approach outperforms other state-of-the-art methods under challenging background conditions.
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Huang, Jun, Aiyue Huang, Ruqin Xu, Musheng Wu, Peng Wang, and Qing Wang. "Automatic Segmentation and Assessment of Valvular Regurgitations with Color Doppler Echocardiography Images: A VABC-UNet-Based Framework." Bioengineering 10, no. 11 (November 16, 2023): 1319. http://dx.doi.org/10.3390/bioengineering10111319.

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This study investigated the automatic segmentation and classification of mitral regurgitation (MR) and tricuspid regurgitation (TR) using a deep learning-based method, aiming to improve the efficiency and accuracy of diagnosis of valvular regurgitations. A VABC-UNet model was proposed consisting of VGG16 encoder, U-Net decoder, batch normalization, attention block and deepened convolution layer based on the U-Net backbone. Then, a VABC-UNet-based assessment framework was established for automatic segmentation, classification, and evaluation of valvular regurgitations. A total of 315 color Doppler echocardiography images of MR and/or TR in an apical four-chamber view were collected, including 35 images in the test dataset and 280 images in the training dataset. In comparison with the classic U-Net and VGG16-UNet models, the segmentation performance of the VABC-UNet model was evaluated via four metrics: Dice, Jaccard, Precision, and Recall. According to the features of regurgitation jet and atrium, the regurgitation could automatically be classified into MR or TR, and evaluated to mild, moderate, moderate–severe, or severe grade by the framework. The results show that the VABC-UNet model has a superior performance in the segmentation of valvular regurgitation jets and atria to the other two models and consequently a higher accuracy of classification and evaluation. There were fewer pseudo- and over-segmentations by the VABC-UNet model and the values of the metrics significantly improved (p < 0.05). The proposed VABC-UNet-based framework achieves automatic segmentation, classification, and evaluation of MR and TR, having potential to assist radiologists in clinical decision making of the regurgitations in valvular heart diseases.
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Cai Ming, Huang Xiaoyang, Wang Boliang, and Su Maolong. "Automatic Mitral Valve Leaflet Scallops Segmentation in Echocardiography." International Journal of Advancements in Computing Technology 5, no. 8 (April 30, 2013): 687–94. http://dx.doi.org/10.4156/ijact.vol5.issue8.78.

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Skalski, Andrzej, and Paweł Turcza. "Heart Segmentation in Echo Images." Metrology and Measurement Systems 18, no. 2 (January 1, 2011): 305–14. http://dx.doi.org/10.2478/v10178-011-0012-y.

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Heart Segmentation in Echo ImagesCardiovascular system diseases are the major causes of mortality in the world. The most important and widely used tool for assessing the heart state is echocardiography (also abbreviated as ECHO). ECHO images are used e.g. for location of any damage of heart tissues, in calculation of cardiac tissue displacement at any arbitrary point and to derive useful heart parameters like size and shape, cardiac output, ejection fraction, pumping capacity. In this paper, a robust algorithm for heart shape estimation (segmentation) in ECHO images is proposed. It is based on the recently introduced variant of the level set method called level set without edges. This variant takes advantage of the intensity value of area information instead of module of gradient which is typically used. Such approach guarantees stability and correctness of algorithm working on the border between object and background with small absolute value of image gradient. To reassure meaningful results, the image segmentation is proceeded with automatic Region of Interest (ROI) calculation. The main idea of ROI calculations is to receive a triangle-like part of the acquired ECHO image, using linear Hough transform, thresholding and simple mathematics. Additionally, in order to improve the images quality, an anisotropic diffusion filter, before ROI calculation, was used. The proposed method has been tested on real echocardiographic image sequences. Derived results confirm the effectiveness of the presented method.
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Journal articles Dissertations / Theses

Dissertations / Theses on the topic "Echocardiography segmentation":

1

Hang, Xiyi. "Compression and segmentation of three-dimensional echocardiography." Connect to this title online, 2004. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1089835123.

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Thesis (Ph. D.)--Ohio State University, 2004.
Title from first page of PDF file. Document formatted into pages; contains xvii, 151 p.; also includes graphics (some col.). Includes bibliographical references (p. 145-151). Available online via OhioLINK's ETD Center
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Dydenko, Igor Friboulet Denis. "Segmentation dynamique en échocardiographie ultrasonore radiofréquence ynamic segmentation in ultrasound radiofrequency echocardiography /." Villeurbanne : Doc'INSA, 2005. http://docinsa.insa-lyon.fr/these/pont.php?id=dydenko.

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Thèse doctorat : Images et Systèmes : Villeurbanne, INSA : 2003.
Thèse rédigée en anglais. Résumé en français en début de chaque chapitre. Titre provenant de l'écran-titre. Bibliogr. p. 216-232. Publications de l'auteur p. 214-215.
3

Zabair, Adeala Tuffail. "Segmentation of stress echocardiography sequences using a patient-specific prior." Thesis, University of Oxford, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.534181.

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Verhoek, Michael. "Fast segmentation of the LV myocardium in real-time 3D echocardiography." Thesis, University of Oxford, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.566050.

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Heart disease is a major cause of death in western countries. In order to diagnose and monitor heart disease, 3D echocardiography is an important tool, as it provides a fast, relatively low-cost, portable and harmless way of imaging the moving heart. Segmentation of cardiac walls is an indispensable method of obtaining quantitative measures of heart function. However segmentation of ultrasound images has its challenges: image quality is often relatively low and current segmentation methods are often not fast. It is desirable to make the segmentation technique as fast as possible, making quantitative heart function measures available at the time of recording. In this thesis, we test two state-of-the-art fast segmentation techniques to address this issue; furthermore, we develop a novel technique for finding the best segmentation propagation strategy between points of time in a cardiac image sequence. The first fast method is Graph Cuts (GC), an energy minimisation technique that represents the image as a graph. We test this method on static 3D echocardiography to segment the myocardium, varying the importance of the regulariser function. We look at edge measures, position constraints and tissue characterisation and find that GC is relatively fast and accurate. The second fast method is Random Forests (RFos), a discriminative classifier using binary decision trees, used in machine learning. To our knowledge, we are the first to test this method for myocardial segmentation on 2D and 3D static echocardiography. We investigate the number of trees, image features used, some internal parameters, and compare with intensity thresholding. We conclude that RFos are very fast and more accurate than GC segmentation. The static RFo method is subsequently applied to all time frames. We describe a novel optical flow based propagation technique that improves the static results by propagating the results from well-performing time frames to less-performing frames. We describe a learning algorithm that learns for each frame which propagation strategy is best. Furthermore, we look at the influence of the number of images and of the training set available per tree, and we compare against other methods that use motion information. Finally, we perform the same propagation learning method on the static GC results, concluding that the propagation method improves the static results in this case as well. We compare the dynamic GC results with the dynamic RFo results and find that RFos are more accurate and faster than GC.
5

Hovda, Sigve. "New Doppler-Based Imaging Methods in Echocardiography with Applications in Blood/Tissue Segmentation." Doctoral thesis, Norwegian University of Science and Technology, Faculty of Information Technology, Mathematics and Electrical Engineering, 2007. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-1500.

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Part 1: The bandwidth of the ultrasound Doppler signal is proposed as a classification function of blood and tissue signal in transthoracial echocardiography of the left ventricle. The new echocardiographic mode, Bandwidth Imaging, utilizes the difference in motion between tissue and blood. Specifically, Bandwidth Imaging is the absolute value of the normalized autocorrelation function with lag one. Bandwidth Imaging is therefore linearly dependent on the the square of the bandwidth estimated from the Doppler spectrum. A 2-tap Finite Impulse Response high-pass filter is used prior to autocorrelation calculation to account for the high level of DC clutter noise in the apical regions. Reasonable pulse strategies are discussed and several images of Bandwidth Imaging are included. An in vivo experiment is presented, where the apparent error rate of Bandwidth Imaging is compared with apparent error rate of Second-Harmonic Imaging on 15 healthy men. The apparent error rate is calculated from signal from all myocardial wall segments defined in \cite{Cer02}. The ground truth of the position of the myocardial wall segments is determined by manual tracing of endocardium in Second-Harmonic Imaging. A hypotheses test of Bandwidth Imaging having lower apparent error rate than

Second-Harmonic Imaging is proved for a p-value of 0.94 in 3 segments of end diastole and 1 segment in end systole on non averaged data. When data is averaged by a structural element of 5 radial, 3 lateral and 4 temporal samples, the numbers of segments are increased to 9 in end diastole and to 6 in end systole. These segments are mostly located in apical and anterior wall regions. Further, a global measure GM is defined as the proportion of misclassified area in the regions close to endocardium in an image. The hypothesis test of Second-Harmonic Imaging having lower GM than Bandwidth Imaging is proved for a p-value of 0.94 in the four-chamber view in end systole in any type of averaging. On the other side, the hypothesis test of Bandwidth Imaging having lower GM than Second-Harmonic Imaging is proved for a p-value of 0.94 in long-axis view in end diastole in any type of averaging. Moreover, if images are averaged by the above structural element the test indicates that Bandwidth Imaging has a lower apparent error rate than Second-Harmonic Imaging in all views and times (end diastole or end systole), except in four-chamber view in end systole. This experiment indicates that Bandwidth Imaging can supply additional information for automatic border detection routines on endocardium.

Part 2: Knowledge Based Imaging is suggested as a method to distinguish blood from tissue signal in transthoracial echocardiography. This method utilizes the maximum likelihood function to classify blood and tissue signal. Knowledge Based Imaging uses the same pulse strategy as Bandwidth Imaging, but is significantly more difficult to implement. Therefore, Knowledge Based Imaging and Bandwidth Imaging are compared with Fundamental Imaging by a computer simulation based on a parametric model of the signal. The rate apparent error rate is calculated in any reasonable tissue to blood signal ratio, tissue to white noise ratio and clutter to white noise ratio. Fundamental Imaging classifies well when tissue to blood signal ratio is high and tissue to white noise ratio is higher than clutter to white noise ratio. Knowledge Based Imaging classifies also well in this environment. In addition, Knowledge Based Imaging classifies well whenever blood to white noise ratio is above 30 dB. This is the case, even when clutter to white noise ratio is higher than tissue to white noise ratio and tissue to blood signal ratio is zero. Bandwidth Imaging performs similar to Knowledge Based Imaging, but blood to white noise ratio has to be 20 dB higher for a reasonable classification. Also the highpass filter coefficient prior to Bandwidth Imaging calculation is discussed by the simulations. Some images of different parameter settings of Knowledge Based Imaging are visually compared with Second-Harmonic Imaging, Fundamental Imaging and Bandwidth Imaging. Changing parameters of Knowledge Based Imaging can make the image look similar to both Bandwidth Imaging and Fundamental Imaging.

6

Icenogle, David A. "Development of virtual mitral valve leaflet models from three-dimensional echocardiography." Thesis, Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/48994.

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Mitral valve (MV) disease is responsible for approximately 2,581 deaths and 41,000 hospital discharges each year in the US. Mitral regurgitation (MR), retrograde blood from through the MV, is often an indicator of MV disease. Surgical repair of MVs is preferred over replacement, as it is correlated with better patient quality of life. However, replacement rates are still near 40% because MV surgical repair expertise is not spread across all hospitals. In addition, 15-80% of surgical repair patients have recurrent MR within 10 years. Quantitative patient-specific models could aid these issues by providing less experienced surgeons with additional information before surgery and a quantitative map of patient valve changes after surgery. Real-time 3D echocardiography (RT3DE) can provide high quality 3D images of MVs and has been used to generate quantitative models previously. However, there is not currently an efficient, dynamic, and validated method that is fast enough to use in common practice. To fill this need, a tool to generate quantitative 3D models of mitral valve leaflets from RT3DE in an efficient manner was created. Then an in vitro echocardiography correction scheme was devised and a dynamic, in vitro validation of the tool was performed. The tool demonstrated that it could generate dynamic, complex MV geometry accurately and more efficiently than current methods available. In addition, the ability for mesh interpolation techniques to reduce segmentation time was demonstrated. The tool generated by this study provides a method to quickly and accurately generate MV geometry that could be applied to dynamic patient specific geometry to aid surgical decisions and track patient geometry changes after surgery.
7

Walimbe, Vivek S. "Interactive, quantitative 3D stress echocardiography and myocardial perfusion spect for improved diagnosis of coronary artery disease." Columbus, Ohio : Ohio State University, 2006. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1154710169.

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Dindoyal, I. "Foetal echocardiographic segmentation." Thesis, University College London (University of London), 2010. http://discovery.ucl.ac.uk/20169/.

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Congenital heart disease affects just under one percentage of all live births [1]. Those defects that manifest themselves as changes to the cardiac chamber volumes are the motivation for the research presented in this thesis. Blood volume measurements in vivo require delineation of the cardiac chambers and manual tracing of foetal cardiac chambers is very time consuming and operator dependent. This thesis presents a multi region based level set snake deformable model applied in both 2D and 3D which can automatically adapt to some extent towards ultrasound noise such as attenuation, speckle and partial occlusion artefacts. The algorithm presented is named Mumford Shah Sarti Collision Detection (MSSCD). The level set methods presented in this thesis have an optional shape prior term for constraining the segmentation by a template registered to the image in the presence of shadowing and heavy noise. When applied to real data in the absence of the template the MSSCD algorithm is initialised from seed primitives placed at the centre of each cardiac chamber. The voxel statistics inside the chamber is determined before evolution. The MSSCD stops at open boundaries between two chambers as the two approaching level set fronts meet. This has significance when determining volumes for all cardiac compartments since cardiac indices assume that each chamber is treated in isolation. Comparison of the segmentation results from the implemented snakes including a previous level set method in the foetal cardiac literature show that in both 2D and 3D on both real and synthetic data, the MSSCD formulation is better suited to these types of data. All the algorithms tested in this thesis are within 2mm error to manually traced segmentation of the foetal cardiac datasets. This corresponds to less than 10% of the length of a foetal heart. In addition to comparison with manual tracings all the amorphous deformable model segmentations in this thesis are validated using a physical phantom. The volume estimation of the phantom by the MSSCD segmentation is to within 13% of the physically determined volume.
9

Barbosa, Daniel. "Automated assessment of cardiac morphology and function : An integrated B-spline framework for real-time segmentation and tracking of the left ventricle." Thesis, Lyon, INSA, 2013. http://www.theses.fr/2013ISAL0111.

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L’objectif principal de cette thèse est le développement de techniques de segmentation et de suivi totalement automatisées du ventricule gauche (VG) en RT3DE. Du fait de la nature difficile et complexe des données RT3DE, l’application directe des algorithmes classiques de vision par ordinateur est le plus souvent impossible. Les solutions proposées ont donc été formalisées et implémentées de sorte à satisfaire les contraintes suivantes : elles doivent permettre une analyse complètement automatique (ou presque) et le temps de calcul nécessaire doit être faible afin de pouvoir fonctionner en temps réel pour une utilisation clinique optimale. Dans ce contexte, nous avons donc proposé un nouveau cadre ou les derniers développements en segmentation d’images par ensembles de niveaux peuvent être aisément intégrés, tout en évitant les temps de calcul importants associés à ce type d’algorithmes. La validation clinique de cette approche a été effectuée en deux temps. Tout d’abord, les performances des outils développés ont été évaluées dans un contexte global se focalisant sur l’utilisation en routine clinique. Dans un second temps, la précision de la position estimée du contour du ventricule gauche a été mesurée. Enfin, les méthodes proposées ont été intégrées dans une suite logicielle utilisée à des fins de recherche. Afin de permettre une utilisation quotidienne efficace, des solutions conviviales ont été proposées incluant notamment un outil interactif pour corriger la segmentation du VG
The fundamental goal of the present thesis was the development of automatic strategies for left ventricular (LV) segmentation and tracking in RT3DE data. Given the challenging nature of RT3DE data, classical computer vision algorithms often face complications when applied to ultrasound. Furthermore, the proposed solutions were formalized and built to respect the following requirements: they should allow (nearly) fully automatic analysis and their computational burden should be low, thus enabling real-time processing for optimal online clinical use. With this in mind, we have proposed a novel segmentation framework where the latest developments in level-set-based image segmentation algorithms could be straightforwardly integrated, while avoiding the heavy computational burden often associated with level-set algorithms. Furthermore, a strong validation component was included in order to assess the performance of the proposed algorithms in realistic scenarios comprising clinical data. First, the performance of the developed tools was evaluated from a global perspective, focusing on its use in clinical daily practice. Secondly, also the spatial accuracy of the estimated left ventricular boundaries was assessed. As a final step, we aimed at the integration of the developed methods in an in-house developed software suite used for research purposes. This included user-friendly solutions for efficient daily use, namely user interactive tools to adjust the segmented left ventricular boundaries
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Souza, André Fernando Lourenço de. "Abordagens para a segmentação de coronárias em ecocardiografia." Universidade de São Paulo, 2010. http://www.teses.usp.br/teses/disponiveis/3/3142/tde-20102010-123221/.

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A Ecocardiografia continua sendo a técnica de captura de imagens mais promissora, não-invasiva, sem radiação ionizante e de baixo custo para avaliação de condições cardíacas. Porém, é afetada consideravelmente por ruídos do tipo speckle, que são difíceis de serem filtrados. Por isso fez-se necessário fazer a escolha certa entre filtragem e segmentador para a obtenção de resultados melhores na segmentação de estruturas. O objetivo dessa pesquisa foi estudar essa combinação entre filtro e segmentador. Para isso, foi desenvolvido um sistema segmentador, a fim de sistematizar essa avaliação. Foram implementados dois filtros para atenuar o efeito do ruído speckle - Linear Scaling Mean Variance (LSMV) e o filtro de Chitwong - testados em imagens simuladas. Foram simuladas 60 imagens com 300 por 300 pixels, 3 modelos, 4 espessuras e 5 níveis de contrastes diferentes, todas com ruído speckle. Além disso, foram feitos testes com a combinação de filtros. Logo após, foi implementado um algoritmo de conectividade Fuzzy para fazer a segmentação e um sistema avaliador, seguindo os critérios descritos por Loizou, que faz a contagem de verdadeiro-positivos (VP) e falso-positivos (FP). Foi verificado que o filtro LSMV é a melhor opção para segmentação por conectividade Fuzzy. Foram obtidas taxas de VP e FP na ordem de 95% e 5%, respectivamente, e acurácia em torno de 95%. Para imagens ruidosas com alto contraste, aplicando a segmentação sem filtragem, a acurácia obtida foi na ordem de 60%.
The echocardiography is the imaging technique that remains most promising, noninvasive, no ionizing radiation and inexpensive to assess heart conditions. On the other hand, is considerably affected by noises, such as speckle, that are very difficult to be filtered. That is why it is necessary to make the right choice of filter and segmentation method to obtain the best results on image segmentation. The goal was evaluate this filter and segmentation method combination. For that, it was developed a segmentation system, to help the assessment. Two filters were implemented to mitigate the effect of speckle noise Linear Scaling Mean Variance (LSMV) and the filter presented by Chitwong - to be tested in simulated images. We simulated 60 images, with size 300 by 300 pixels, 3 models, 4 thicknesses and 5 different levels of contrast, all with speckle noise. In addition, tests were made with a combination of filters. Furthermore, it was implemented a Fuzzy Connectedness algorithm and an evaluation system, following the criteria described by Loizou, which makes the true positives (TP) and false positives (FP) counting. It was found that the LSMV filter is the best option for Fuzzy Connectedness. We obtained rates of TP and FP of 95% and 5% using LSMV, and accuracy of 95%. Using high contrast noisy images, without filtering, we obtained the accuracy in order of 60%.
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Books on the topic "Echocardiography segmentation":

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Lancellotti, Patrizio, and Bernard Cosyns. Assessment of the Left Ventricular Systolic Function. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780198713623.003.0004.

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Evaluation of ventricular systolic function and cavity dimensions is an essential part of the echocardiographic examination. Treatment strategy and decisionmaking for a patient’s condition is affected by systolic function. Echocardiography plays a major in monitoring the effects of therapy. Appropriate knowledge about how to assess left ventricular size, shape and function is thus crucial. This chapter demonstrates left chamber quantification through various measurements of left ventricular size and dimensions, left ventricular mass, left ventricularglobal function, regional wall motion, left ventricular segmentation, global left ventricular remodelling, and left atrial measurements. Techniques, advantages, and limitations of different methods and echocardiographic examinations are given throughout.

Book chapters on the topic "Echocardiography segmentation":

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Picano, Eugenio. "Segmentation of the Left Ventricle." In Stress Echocardiography, 91–104. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-76466-3_7.

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Picano, Eugenio. "Segmentation of the Left Ventricle." In Stress Echocardiography, 46–51. Berlin, Heidelberg: Springer Berlin Heidelberg, 1992. http://dx.doi.org/10.1007/978-3-662-13061-2_5.

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Picano, Eugenio. "Segmentation of the Left Ventricle." In Stress Echocardiography, 51–56. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/978-3-662-10090-5_6.

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Picano, Eugenio. "Segmentation of the Left Ventricle." In Stress Echocardiography, 57–65. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-662-05096-5_6.

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Picano, Eugenio. "Segmentation of the Left Ventricle." In Stress Echocardiography, 61–66. Berlin, Heidelberg: Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/978-3-662-02979-4_6.

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Badano, Luigi P., and Eugenio Picano. "Standardized Myocardial Segmentation of the Left Ventricle." In Stress Echocardiography, 105–19. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-20958-6_7.

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Feng, Zishun, Joseph A. Sivak, and Ashok K. Krishnamurthy. "Improving Echocardiography Segmentation by Polar Transformation." In Statistical Atlases and Computational Models of the Heart. Regular and CMRxMotion Challenge Papers, 133–42. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-23443-9_13.

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Saeed, Mohamed, Rand Muhtaseb, and Mohammad Yaqub. "Contrastive Pretraining for Echocardiography Segmentation with Limited Data." In Medical Image Understanding and Analysis, 680–91. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-12053-4_50.

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Guo, Libao, Yujin Hu, Baiying Lei, Jie Du, Muyi Mao, Zelong Jin, Bei Xia, and Tianfu Wang. "Dual Network Generative Adversarial Networks for Pediatric Echocardiography Segmentation." In Smart Ultrasound Imaging and Perinatal, Preterm and Paediatric Image Analysis, 113–22. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32875-7_13.

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Martin, Sébastien, Vincent Daanen, Olivier Chavanon, and Jocelyne Troccaz. "Fast Segmentation of the Mitral Valve Leaflet in Echocardiography." In Computer Vision Approaches to Medical Image Analysis, 225–35. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11889762_20.

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Conference papers on the topic "Echocardiography segmentation":

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Mazaheri, Samaneh, Puteri Suhaiza Binti Sulaiman, Rahmita Wirza, Fatimah Khalid, Suhaini Kadiman, Mohd Zamrin Dimon, and Rohollah Moosavi Tayebi. "Echocardiography Image Segmentation: A Survey." In 2013 International Conference on Advanced Computer Science Applications and Technologies (ACSAT). IEEE, 2013. http://dx.doi.org/10.1109/acsat.2013.71.

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Tran, Tung, Joshua V. Stough, Xiaoyan Zhang, and Christopher M. Haggerty. "Bayesian Optimization of 2D Echocardiography Segmentation." In 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI). IEEE, 2021. http://dx.doi.org/10.1109/isbi48211.2021.9433868.

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Vyas, Saurabh, Ryan Mukherjee, Federico Sosa, and Philippe Burlina. "Endocardium segmentation in 3D Transesophageal Echocardiography." In 2013 IEEE 10th International Symposium on Biomedical Imaging (ISBI 2013). IEEE, 2013. http://dx.doi.org/10.1109/isbi.2013.6556411.

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Chechani, Shubham, Rahul Suresh, and Kedar A. Patwardhan. "Aortic root segmentation in 4D transesophageal echocardiography." In Computer-Aided Diagnosis, edited by Kensaku Mori and Nicholas Petrick. SPIE, 2018. http://dx.doi.org/10.1117/12.2293056.

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Monkam, Patrice, Songbai Jin, and Wenkai Lu. "Multi-task learning framework for echocardiography segmentation." In 2022 IEEE International Ultrasonics Symposium (IUS). IEEE, 2022. http://dx.doi.org/10.1109/ius54386.2022.9957223.

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Nakphu, Nonthaporn, Dyah Ekashanti Octorina Dewi, Muhammad Qurhanul Rizqie, Eko Supriyanto, Ahmad 'Athif Mohd Faudzi, Dolwin Ching Ching Kho, Suhaini Kadiman, and Panrasee Rittipravat. "Apical four-chamber echocardiography segmentation using Marker-controlled Watershed segmentation." In 2014 IEEE Conference on Biomedical Engineering and Sciences (IECBES). IEEE, 2014. http://dx.doi.org/10.1109/iecbes.2014.7047583.

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Milićević, Bogdan, Miljan Milošević, Mina Vasković Jovanović, Vladimir Milovanović, Nenad Filipovic, and Miloš Kojić. "Overview of Left Ventricular Segmentation in Ultrasound Images." In 2nd International Conference on Chemo and Bioinformatics. Institute for Information Technologies, University of Kragujevac, 2023. http://dx.doi.org/10.46793/iccbi23.359m.

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Due to its great temporal resolution and quick acquisition periods, two-dimensional echocardiography, or shorter 2D echo, is the most used non-invasive approach for assessing heart disease. It offers a grayscale image that anatomical details can be extracted from to evaluate heart functioning. The initial stage in quantifying cardiac function in 2D echo is the segmentation of the left ventricular (LV) walls. The primary boundary identification methods used for 2D echo at the moment are semi-automatic or manual delineation carried out by professionals. However, manual or semi-automatic approaches take a lot of time and are subjective, which makes them vulnerable to both intra- and inter-observer variability. Many researchers have tried to automate the process of left ventricle segmentation. The extensive use of deep learning algorithms has lately changed medical image analysis. The revolution has primarily been powered by supervised machine learning with convolutional neural networks. In this paper, we will provide a short overview of some of the popular deep-learning techniques for left ventricular segmentation in two-dimensional echocardiography.
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Carnahan, Patrick, Elvis Chen, and Terry Peters. "From 4D Transesophageal Echocardiography to Patient Specific Mitral Valve Models." In THE HAMLYN SYMPOSIUM ON MEDICAL ROBOTICS. The Hamlyn Centre, Imperial College London London, UK, 2023. http://dx.doi.org/10.31256/hsmr2023.77.

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Mitral valve regurgitation is the most common valvular disease, affecting 10% of the population over 75 years old [1]. Current standard of care diagnostic imaging for mitral valve procedures primarily consists of trans- esophageal echocardiography (TEE) as it provides a clear view of the mitral valve leaflets and surrounding tissue. Heart simulator technology has been adopted widely by both industry for evaluation of technolo- gies for imaging heart valves [2], and academia for the assessment of modelled heart valves [3]. Recently, developments have been made on a workflow to cre- ate 3D, patient-specific valve models directly from trans-esophageal echocardiography (TEE) images. When viewed dynamically using TEE within a pulse duplicator simulator, it has been demonstrated that these models result in pathology-specific TEE images similar to those acquired from the patient’s valves in-vivo [4]. However, producing a mesh model of the valve geometry from TEE imaging remains a challenge. Previously, produc- ing a valve model included a labor intensive series of steps including manual leaflet segmentation, and computer-aided design (CAD) manipulation to derive a 3D printable mold from a raw segmentation. Our objective is to automate the workflow and reduce the labor requirements for producing these valve models. To address the leaflet segmentation problem, we developed DeepMitral, a fully automatic valve leaflet segmentation tool. Following leaflet segmentation, we have developed tools for automatically deriving mesh models that can easily be integrated into a mold base.
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Galluzzo, F., D. Barbosa, H. Houle, N. Speciale, D. Friboulet, J. D'hooge, and O. Bernard. "A GPU level-set segmentation framework for 3D Echocardiography." In 2012 IEEE International Ultrasonics Symposium. IEEE, 2012. http://dx.doi.org/10.1109/ultsym.2012.0661.

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Chen, Yida, Xiaoyan Zhang, Christopher M. Haggerty, and Joshua V. Stough. "Assessing the generalizability of temporally coherent echocardiography video segmentation." In Image Processing, edited by Bennett A. Landman and Ivana Išgum. SPIE, 2021. http://dx.doi.org/10.1117/12.2580874.

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