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Статті в журналах з теми "Echocardiography segmentation"

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Автор: Grafiati

Опубліковано: 7 липня 2024

Оновлено: 7 липня 2024

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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.

2

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.

4

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.

5

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.

6

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.

7

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.

8

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.

9

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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10

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.

11

Barrile, V., F. Cotroneo, E. Genovese, E. Barrile, and G. Bilotta. "AN AI SEGMENTER ON MEDICAL IMAGING FOR GEOMATICS APPLICATIONS CONSISTING OF A TWO-STATE PIPELINE, SNNS NETWORK AND WATERSHED ALGORITHM." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-2/W3-2023 (May 12, 2023): 21–26. http://dx.doi.org/10.5194/isprs-archives-xlviii-2-w3-2023-21-2023.

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Abstract. As is well known, image segmentation is widely used in the fields of echocardiography and diagnostic and interventional radiology. The delineation of structural components of various organs from 2D images is a technique used in the medical field in order to identify intervention targets with increasing precision and accuracy. In recent decades, the automation of this task has been the subject of intensive research. In particular, to improve the segmentation of such images, investigations have focused on the use of neural networks, and in particular convolutional neural networks (CNNs). However, most existing CNN-based methods can produce unsatisfactory segmentation masks without precise object boundaries (Wang, Chen, Ji, Fan & Ye Li, 2022); this is mainly due to the shadows and high noise in these images. To address the problem of automated image segmentation, this work proposes a pipeline technique with two stages (applied primarily to the echocardiographic domain): the first consisting of a Self-normalising Neural Networks (SNNs) performs image denoising, while the second applies a Watershed segmentation algorithm on the cleaned image. The latter is a technique successfully applied in geomatics and land surveying. The proposed methodology may be of interest both in the medical field and in the field of Geomatics where segmentation and classification operations are required in different application areas.

12

Rachmatullah, M. N., Siti Nurmaini, A. I. Sapitri, A. Darmawahyuni, B. Tutuko, and Firdaus Firdaus. "Convolutional neural network for semantic segmentation of fetal echocardiography based on four-chamber view." Bulletin of Electrical Engineering and Informatics 10, no. 4 (August 1, 2021): 1987–96. http://dx.doi.org/10.11591/eei.v10i4.3060.

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The acute shortage of trained and experienced sonographers causes the detection of congenital heart defects (CHDs) extremely difficult. In order to minimize this difficulty, an accurate fetal heart segmentation to the early location of such structural heart abnormalities prior to delivery is essential. However, the segmentation process is not an easy task due to the small size of the fetal heart structure. Moreover, the manual task for identifying the standard cardiac planes, primarily based on a four-chamber view, requires a well-trained clinician and experience. In this paper, a CNN method using U-Net architecture was proposed to automate fetal cardiac standard planes segmentation from ultrasound images. A total of 519 fetal cardiac images was obtained from three videos. All data is divided into training and testing data. The testing data consist of 106 slices of the four-chamber segmentation tasks, i.e. atrial septal defect (ASD), ventricular septal defect (VSD), and normal. The segmentation of the post-processing method is needed to enhanced the segmentation result. In this paper, a combination technique with U-Net and Otsu thresholding gives the best performances with 99.48%-pixel accuracy, 96.73% mean accuracy, 94.92% mean intersection over union, and 0.21% segmentation error. In the future, the implementation of Deep Learning in the study of CHDs holds significant potential for identifying novel CHDs in heterogeneous fetal hearts.

13

Cai, Junfeng, Xiahai Zhuang, Yuanyuan Nie, Zhe Luo, and Lixu Gu. "Real-time aortic valve segmentation from transesophageal echocardiography sequence." International Journal of Computer Assisted Radiology and Surgery 10, no. 4 (August 3, 2014): 447–58. http://dx.doi.org/10.1007/s11548-014-1104-y.

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14

Valanrani, B. Arockia. "PREDICTING CARDIAC ISSUES FROM ECHOCARDIOGRAMS: A LITERATURE REVIEW USING DEEP LEARNING AND MACHINE LEARNING TECHNIQUES." international journal of advanced research in computer science 15, no. 1 (February 20, 2024): 5–13. http://dx.doi.org/10.26483/ijarcs.v15i1.7040.

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Cardiovascular disease (CVD) has a substantial impact on overall health, well-being, and life-expectancy. Echocardiography is a widely used imaging technique in cardiovascular medicine, utilizing various medical imaging technology to visualize heart chambers and valve’s motion activity. In order to diagnose and treat complicated cardiovascular problems, it takes high-resolution images of the heart and its surroundings. However, it has limitations such as long procedure times, multiple measurement values, complex analyses, individualized assessments, operator subjectivity, and wide observation ranges. This makes it challenging for sonographers to accurately detect and diagnose heart diseases. In recent days, Deep Learning (DL) is increasingly used in clinical computer-assisted systems for disease detection, feature segmentation, functional evaluation, and diagnosis. It is an alternate technique for accurate detection and treatment of cardiovascular disorders; it improves the diagnostic capacities of echocardiography by identifying pathological conditions, extracting anatomically significant data, measuring cardio-motion, and calculating echo image quality. This paper presents a detailed review of various DL frameworks developed to analyse different cardiac views using echocardiography for improving the prediction and diagnosis of CVD. At the outset, a variety of echocardiography systems linked with DL-based segmentation and classification are reviewed briefly. Afterwards, a comparison research is carried out to gain insight into the shortcomings of those algorithms and provide a fresh approach to improve the accuracy of cardiac view categorization in echocardiography systems.

15

Dong, Suyu, Gongning Luo, Kuanquan Wang, Shaodong Cao, Qince Li, and Henggui Zhang. "A Combined Fully Convolutional Networks and Deformable Model for Automatic Left Ventricle Segmentation Based on 3D Echocardiography." BioMed Research International 2018 (September 10, 2018): 1–16. http://dx.doi.org/10.1155/2018/5682365.

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Segmentation of the left ventricle (LV) from three-dimensional echocardiography (3DE) plays a key role in the clinical diagnosis of the LV function. In this work, we proposed a new automatic method for the segmentation of LV, based on the fully convolutional networks (FCN) and deformable model. This method implemented a coarse-to-fine framework. Firstly, a new deep fusion network based on feature fusion and transfer learning, combining the residual modules, was proposed to achieve coarse segmentation of LV on 3DE. Secondly, we proposed a method of geometrical model initialization for a deformable model based on the results of coarse segmentation. Thirdly, the deformable model was implemented to further optimize the segmentation results with a regularization item to avoid the leakage between left atria and left ventricle to achieve the goal of fine segmentation of LV. Numerical experiments have demonstrated that the proposed method outperforms the state-of-the-art methods on the challenging CETUS benchmark in the segmentation accuracy and has a potential for practical applications.

16

Shoaib, Muhammad Ali, Joon Huang Chuah, Raza Ali, Samiappan Dhanalakshmi, Yan Chai Hum, Azira Khalil, and Khin Wee Lai. "Fully Automatic Left Ventricle Segmentation Using Bilateral Lightweight Deep Neural Network." Life 13, no. 1 (January 1, 2023): 124. http://dx.doi.org/10.3390/life13010124.

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The segmentation of the left ventricle (LV) is one of the fundamental procedures that must be performed to obtain quantitative measures of the heart, such as its volume, area, and ejection fraction. In clinical practice, the delineation of LV is still often conducted semi-automatically, leaving it open to operator subjectivity. The automatic LV segmentation from echocardiography images is a challenging task due to poorly defined boundaries and operator dependency. Recent research has demonstrated that deep learning has the capability to employ the segmentation process automatically. However, the well-known state-of-the-art segmentation models still lack in terms of accuracy and speed. This study aims to develop a single-stage lightweight segmentation model that precisely and rapidly segments the LV from 2D echocardiography images. In this research, a backbone network is used to acquire both low-level and high-level features. Two parallel blocks, known as the spatial feature unit and the channel feature unit, are employed for the enhancement and improvement of these features. The refined features are merged by an integrated unit to segment the LV. The performance of the model and the time taken to segment the LV are compared to other established segmentation models, DeepLab, FCN, and Mask RCNN. The model achieved the highest values of the dice similarity index (0.9446), intersection over union (0.8445), and accuracy (0.9742). The evaluation metrics and processing time demonstrate that the proposed model not only provides superior quantitative results but also trains and segments the LV in less time, indicating its improved performance over competing segmentation models.

17

Teng, Long, ZhongLiang Fu, Qian Ma, Yu Yao, Bing Zhang, Kai Zhu, and Ping Li. "Interactive Echocardiography Translation Using Few-Shot GAN Transfer Learning." Computational and Mathematical Methods in Medicine 2020 (March 19, 2020): 1–9. http://dx.doi.org/10.1155/2020/1487035.

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Background. Interactive echocardiography translation is an efficient educational function to master cardiac anatomy. It strengthens the student’s understanding by pixel-level translation between echocardiography and theoretically sketch images. Previous research studies split it into two aspects of image segmentation and synthesis. This split makes it hard to achieve pixel-level corresponding translation. Besides, it is also challenging to leverage deep-learning-based methods in each phase where a handful of annotations are available. Methods. To address interactive translation with limited annotations, we present a two-step transfer learning approach. Firstly, we train two independent parent networks, the ultrasound to sketch (U2S) parent network and the sketch to ultrasound (S2U) parent network. U2S translation is similar to a segmentation task with sector boundary inference. Therefore, the U2S parent network is trained with the U-Net network on the public segmentation dataset of VOC2012. S2U aims at recovering ultrasound texture. So, the S2U parent network is decoder networks that generate ultrasound data from random input. After pretraining the parent networks, an encoder network is attached to the S2U parent network to translate ultrasound images into sketch images. We jointly transfer learning U2S and S2U within the CGAN framework. Results and conclusion. Quantitative and qualitative contrast from 1-shot, 5-shot, and 10-shot transfer learning show the effectiveness of the proposed algorithm. The interactive translation is achieved with few-shot transfer learning. Thus, the development of new applications from scratch is accelerated. Our few-shot transfer learning has great potential in the biomedical computer-aided image translation field, where annotation data are extremely precious.

18

Mortada, MHD Jafar, Selene Tomassini, Haidar Anbar, Micaela Morettini, Laura Burattini, and Agnese Sbrollini. "Segmentation of Anatomical Structures of the Left Heart from Echocardiographic Images Using Deep Learning." Diagnostics 13, no. 10 (May 9, 2023): 1683. http://dx.doi.org/10.3390/diagnostics13101683.

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Knowledge about the anatomical structures of the left heart, specifically the atrium (LA) and ventricle (i.e., endocardium—Vendo—and epicardium—LVepi) is essential for the evaluation of cardiac functionality. Manual segmentation of cardiac structures from echocardiography is the baseline reference, but results are user-dependent and time-consuming. With the aim of supporting clinical practice, this paper presents a new deep-learning (DL)-based tool for segmenting anatomical structures of the left heart from echocardiographic images. Specifically, it was designed as a combination of two convolutional neural networks, the YOLOv7 algorithm and a U-Net, and it aims to automatically segment an echocardiographic image into LVendo, LVepi and LA. The DL-based tool was trained and tested on the Cardiac Acquisitions for Multi-Structure Ultrasound Segmentation (CAMUS) dataset of the University Hospital of St. Etienne, which consists of echocardiographic images from 450 patients. For each patient, apical two- and four-chamber views at end-systole and end-diastole were acquired and annotated by clinicians. Globally, our DL-based tool was able to segment LVendo, LVepi and LA, providing Dice similarity coefficients equal to 92.63%, 85.59%, and 87.57%, respectively. In conclusion, the presented DL-based tool proved to be reliable in automatically segmenting the anatomical structures of the left heart and supporting the cardiological clinical practice.

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Nurmaini, Siti, Muhammad Naufal Rachmatullah, Ade Iriani Sapitri, Annisa Darmawahyuni, Bambang Tutuko, Firdaus Firdaus, Radiyati Umi Partan, and Nuswil Bernolian. "Deep Learning-Based Computer-Aided Fetal Echocardiography: Application to Heart Standard View Segmentation for Congenital Heart Defects Detection." Sensors 21, no. 23 (November 30, 2021): 8007. http://dx.doi.org/10.3390/s21238007.

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Accurate segmentation of fetal heart in echocardiography images is essential for detecting the structural abnormalities such as congenital heart defects (CHDs). Due to the wide variations attributed to different factors, such as maternal obesity, abdominal scars, amniotic fluid volume, and great vessel connections, this process is still a challenging problem. CHDs detection with expertise in general are substandard; the accuracy of measurements remains highly dependent on humans’ training, skills, and experience. To make such a process automatic, this study proposes deep learning-based computer-aided fetal heart echocardiography examinations with an instance segmentation approach, which inherently segments the four standard heart views and detects the defect simultaneously. We conducted several experiments with 1149 fetal heart images for predicting 24 objects, including four shapes of fetal heart standard views, 17 objects of heart-chambers in each view, and three cases of congenital heart defect. The result showed that the proposed model performed satisfactory performance for standard views segmentation, with a 79.97% intersection over union and 89.70% Dice coefficient similarity. It also performed well in the CHDs detection, with mean average precision around 98.30% for intra-patient variation and 82.42% for inter-patient variation. We believe that automatic segmentation and detection techniques could make an important contribution toward improving congenital heart disease diagnosis rates.

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Ye, Zi, Yogan Jaya Kumar, Fengyan Song, Guanxi Li, and Suyu Zhang. "Bi-DCNet: Bilateral Network with Dilated Convolutions for Left Ventricle Segmentation." Life 13, no. 4 (April 18, 2023): 1040. http://dx.doi.org/10.3390/life13041040.

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Left ventricular segmentation is a vital and necessary procedure for assessing cardiac systolic and diastolic function, while echocardiography is an indispensable diagnostic technique that enables cardiac functionality assessment. However, manually labeling the left ventricular region on echocardiography images is time consuming and leads to observer bias. Recent research has demonstrated that deep learning has the capability to employ the segmentation process automatically. However, on the downside, it still ignores the contribution of all semantic information through the segmentation process. This study proposes a deep neural network architecture based on BiSeNet, named Bi-DCNet. This model comprises a spatial path and a context path, with the former responsible for spatial feature (low-level) acquisition and the latter responsible for contextual semantic feature (high-level) exploitation. Moreover, it incorporates feature extraction through the integration of dilated convolutions to achieve a larger receptive field to capture multi-scale information. The EchoNet-Dynamic dataset was utilized to assess the proposed model, and this is the first bilateral-structured network implemented on this large clinical video dataset for accomplishing the segmentation of the left ventricle. As demonstrated by the experimental outcomes, our method obtained 0.9228 and 0.8576 in DSC and IoU, respectively, proving the structure’s effectiveness.

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Kim, Dong Ok, Minsu Chae, and HwaMin Lee. "Revolutionizing Echocardiography: A Comparative Study of Advanced AI Models for Precise Left Ventricular Segmentation." International Journal on Advanced Science, Engineering and Information Technology 14, no. 3 (June 5, 2024): 835–40. http://dx.doi.org/10.18517/ijaseit.14.3.18073.

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Cardiovascular diseases, a leading cause of global mortality, underscore the urgency for refined diagnostic techniques. Among these, cardiomyopathies characterized by abnormal heart wall thickening present a formidable challenge, exacerbated by aging populations and the side effects of chemotherapy. Traditional echocardiogram analysis, demanding considerable time and expertise, now faces overwhelming pressure due to escalating demands for cardiac care. This study addresses these challenges by harnessing the potential of Convolutional Neural Networks, specifically YOLOv8, U-Net, and Attention U-Net, leveraging the EchoNet-Dynamic dataset from Stanford University Hospital to segment echocardiographic images. Our investigation aimed to optimize and compare these models for segmenting the left ventricle in echocardiography images, a crucial step for quantifying key cardiac parameters. We demonstrate the superiority of U-Net and Attention U-Net over YOLOv8, with Attention U-Net achieving the highest Dice Coefficient Score due to its focus on relevant features via attention mechanisms. This finding highlights the importance of model specificity in medical image segmentation and points to attention mechanisms. The integration of AI in echocardiography represents a pivotal shift toward precision medicine, improving diagnostic accuracy and operational efficiency. Our results advocate for the continued development and application of AI-driven models, underscoring their potential to transform cardiovascular diagnostics through enhanced precision and multimodal data integration. This study validates the effectiveness of state-of-the-art AI models in cardiac function assessment and paves the way for their implementation in clinical settings, thereby contributing significantly to the advancement of cardiac healthcare delivery.

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Stoean, Catalin, Nebojsa Bacanin, Wiesław Paja, Ruxandra Stoean, Dominic Iliescu, Ciprian Patru, and Rodica Nagy. "Semantic segmentation of fetal heart components in second trimester echocardiography." Procedia Computer Science 207 (2022): 3085–92. http://dx.doi.org/10.1016/j.procs.2022.09.366.

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Mazaheri, Samaneh, Puteri Suhaiza Binti Sulaiman, Rahmita Wirza, Mohd Zamrin Dimon, Fatima Khalid, and Rohollah Moosavi Tayebi. "Segmentation Methods of Echocardiography Images for Left Ventricle Boundary Detection." Journal of Computer Science 11, no. 9 (September 1, 2015): 957–70. http://dx.doi.org/10.3844/jcssp.2015.957.970.

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Sigit, Riyanto, Calvin Alfa Roji, Tri Harsono, and Son Kuswadi. "Improved echocardiography segmentation using active shape model and optical flow." TELKOMNIKA (Telecommunication Computing Electronics and Control) 17, no. 2 (April 1, 2019): 809. http://dx.doi.org/10.12928/telkomnika.v17i2.11821.

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Danilov, V. V., I. P. Skirnevskiy, and O. M. Gerget. "Segmentation of anatomical structures of the heart based on echocardiography." Journal of Physics: Conference Series 803 (January 2017): 012031. http://dx.doi.org/10.1088/1742-6596/803/1/012031.

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Corinzia, Luca, Fabian Laumer, Alessandro Candreva, Maurizio Taramasso, Francesco Maisano, and Joachim M. Buhmann. "Neural collaborative filtering for unsupervised mitral valve segmentation in echocardiography." Artificial Intelligence in Medicine 110 (November 2020): 101975. http://dx.doi.org/10.1016/j.artmed.2020.101975.

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Celestin, B. E., S. P. Bagherzadeh, E. Santana, M. Frost, I. Mathias, A. J. Sweatt, R. Zamanian, et al. "Echocardiography in Pulmonary Arterial Hypertension Using Deep Learning Segmentation Algorithms." Journal of Heart and Lung Transplantation 43, no. 4 (April 2024): S410. http://dx.doi.org/10.1016/j.healun.2024.02.1312.

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Balasubramani, Madankumar, Chih-Wei Sung, Mu-Yang Hsieh, Edward Pei-Chuan Huang, Jiann-Shing Shieh, and Maysam F. Abbod. "Automated Left Ventricle Segmentation in Echocardiography Using YOLO: A Deep Learning Approach for Enhanced Cardiac Function Assessment." Electronics 13, no. 13 (July 1, 2024): 2587. http://dx.doi.org/10.3390/electronics13132587.

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Accurate segmentation of the left ventricle (LV) using echocardiogram (Echo) images is essential for cardiovascular analysis. Conventional techniques are labor-intensive and exhibit inter-observer variability. Deep learning has emerged as a powerful tool for automated medical image segmentation, offering advantages in speed and potentially superior accuracy. This study explores the efficacy of employing a YOLO (You Only Look Once) segmentation model for automated LV segmentation in Echo images. YOLO, a cutting-edge object detection model, achieves exceptional speed–accuracy balance through its well-designed architecture. It utilizes efficient dilated convolutional layers and bottleneck blocks for feature extraction while incorporating innovations like path aggregation and spatial attention mechanisms. These attributes make YOLO a compelling candidate for adaptation to LV segmentation in Echo images. We posit that by fine-tuning a pre-trained YOLO-based model on a well-annotated Echo image dataset, we can leverage the model’s strengths in real-time processing and precise object localization to achieve robust LV segmentation. The proposed approach entails fine-tuning a pre-trained YOLO model on a rigorously labeled Echo image dataset. Model performance has been evaluated using established metrics such as mean Average Precision (mAP) at an Intersection over Union (IoU) threshold of 50% (mAP50) with 98.31% and across a range of IoU thresholds from 50% to 95% (mAP50:95) with 75.27%. Successful implementation of YOLO for LV segmentation has the potential to significantly expedite and standardize Echo image analysis. This advancement could translate to improved clinical decision-making and enhanced patient care.

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Wu, Huisi, Jingyin Lin, Wende Xie, and Jing Qin. "Super-efficient Echocardiography Video Segmentation via Proxy- and Kernel-Based Semi-supervised Learning." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 3 (June 26, 2023): 2803–11. http://dx.doi.org/10.1609/aaai.v37i3.25381.

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Automatic segmentation of left ventricular endocardium in echocardiography videos is critical for assessing various cardiac functions and improving the diagnosis of cardiac diseases. It is yet a challenging task due to heavy speckle noise, significant shape variability of cardiac structure, and limited labeled data. Particularly, the real-time demand in clinical practice makes this task even harder. In this paper, we propose a novel proxy- and kernel-based semi-supervised segmentation network (PKEcho-Net) to comprehensively address these challenges. We first propose a multi-scale region proxy (MRP) mechanism to model the region-wise contexts, in which a learnable region proxy with an arbitrary shape is developed in each layer of the encoder, allowing the network to identify homogeneous semantics and hence alleviate the influence of speckle noise on segmentation. To sufficiently and efficiently exploit temporal consistency, different from traditional methods which only utilize the temporal contexts of two neighboring frames via feature warping or self-attention mechanism, we formulate the semi-supervised segmentation with a group of learnable kernels, which can naturally and uniformly encode the appearances of left ventricular endocardium, as well as extracting the inter-frame contexts across the whole video to resist the fast shape variability of cardiac structures. Extensive experiments have been conducted on two famous public echocardiography video datasets, EchoNet-Dynamic and CAMUS. Our model achieves the best performance-efficiency trade-off when compared with other state-of-the-art approaches, attaining comparative accuracy with a much faster speed. The code is available at https://github.com/JingyinLin/PKEcho-Net.

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Zhuang, Zhemin, Pengcheng Jin, Alex Noel Joseph Raj, Ye Yuan, and Shuxin Zhuang. "Automatic Segmentation of Left Ventricle in Echocardiography Based on YOLOv3 Model to Achieve Constraint and Positioning." Computational and Mathematical Methods in Medicine 2021 (May 16, 2021): 1–11. http://dx.doi.org/10.1155/2021/3772129.

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Cardiovascular disease (CVD) is the most common type of disease and has a high fatality rate in humans. Early diagnosis is critical for the prognosis of CVD. Before using myocardial tissue strain, strain rate, and other indicators to evaluate and analyze cardiac function, accurate segmentation of the left ventricle (LV) endocardium is vital for ensuring the accuracy of subsequent diagnosis. For accurate segmentation of the LV endocardium, this paper proposes the extraction of the LV region features based on the YOLOv3 model to locate the positions of the apex and bottom of the LV, as well as that of the LV region; thereafter, the subimages of the LV can be obtained, and based on the Markov random field (MRF) model, preliminary identification and binarization of the myocardium of the LV subimages can be realized. Finally, under the constraints of the three aforementioned positions of the LV, precise segmentation and extraction of the LV endocardium can be achieved using nonlinear least-squares curve fitting and edge approximation. The experiments show that the proposed segmentation evaluation indices of the method, including computation speed (fps), Dice, mean absolute distance (MAD), and Hausdorff distance (HD), can reach 2.1–2.25 fps, 93.57 ± 1.97 % , 2.57 ± 0.89 mm, and 6.68 ± 1.78 mm, respectively. This indicates that the suggested method has better segmentation accuracy and robustness than existing techniques.

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Shiri, M., H. Behnam, H. Yeganegi, Z. A. Sani, and N. Nematollahi. "TRACKABLE-SPECKLE DETECTION USING A DUAL-PATH CONVOLUTIONAL NEURAL NETWORK FOR NODES SELECTION IN SPECKLE TRACKING ECHOCARDIOGRAPHY." Asian Journal Of Medical Technology 2, no. 2 (August 5, 2022): 33–54. http://dx.doi.org/10.32896/ajmedtech.v2n2.33-54.

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Speckle tracking echocardiography (STE) is widely used to quaantify regional motion and deformation of heart tissues. Before tracking, a segmentation step is first carried out, and only a set of nodes in the segmented model are tracked. However, a random selection of the nodes even after tissue segmentation could lead to an inaccurate estimation. In this paper, a convolutional neural network (CNN)-based method is presented to detect trackable speckle spots that have important properties of the texture for speckle tracking. The proposed CNN was trained and validated on 29500 ultrasound manually labelled image patches extracted from the echocardiography of 65 people. Using the proposed network, in silico experiments for automatic node selection were conducted to investigate the applicability of the proposed method in speckle tracking. The results were statistically highly significant (P<0.001) and demonstrated that the proposed method has the least tracking error among various existing methods.

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Han, Guowei, Tianliang Jin, Li Zhang, Chen Guo, Hua Gui, Risu Na, Xuesong Wang, and Haihua Bai. "Adoption of Compound Echocardiography under Artificial Intelligence Algorithm in Fetal Congenial Heart Disease Screening during Gestation." Applied Bionics and Biomechanics 2022 (June 1, 2022): 1–8. http://dx.doi.org/10.1155/2022/6410103.

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This research was aimed at exploring the diagnostic and screening effect of composite echocardiography based on the artificial intelligence (AI) segmentation algorithm on fetal congenital heart disease (CHD) during pregnancy, so as to reduce the birth rate of newborns with CHD. A total of 204 fetuses with abnormal heart conditions were divided into group II, group C (optimized with the AI algorithm), and group W (not optimized with the AI algorithm). In addition, 9,453 fetuses with normal heart conditions were included in group I. The abnormal distribution of fetal heart and the difference of cardiac Z score between group II and group I were analyzed, and the diagnostic value of group C and group W for CHD was compared. The results showed that the segmentation details of the proposed algorithm were better than those of the convolutional neural network (CNN), and the Dice coefficient, precision, and recall values were higher than those of the CNN. In fetal CHD, the incidence of abnormal ultrasonic manifestations was ventricular septal defect (98/48.04%), abnormal right subclavian artery (29/14.22%), and persistent left superior vena cava (25/12.25%). The diagnostic sensitivity (75.0% vs. 51.5%), specificity (99.6% vs. 99.2%), accuracy (99.0% vs. 98.2%), negative predictive value (88.5% vs. 78.5%), and positive predictive value (99% vs. 57.7%) of echocardiography segmentation in group C were significantly higher than those in group W. To sum up, echocardiography segmented by the AI algorithm could obviously improve the diagnostic efficiency of fetal CHD during gestation. Cardiac ultrasound parameters of children with CHD changed greatly.

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Azizi, Fityan, Mgs M. Luthfi Ramadhan, and Wisnu Jatmiko. "Encoder-Decoder with Atrous Spatial Pyramid Pooling for Left Ventricle Segmentation in Echocardiography." Jurnal Ilmu Komputer dan Informasi 16, no. 2 (July 3, 2023): 163–69. http://dx.doi.org/10.21609/jiki.v16i2.1165.

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Assessment of cardiac function using echocardiography is an essential and widely used method. Assessment by manually labeling the left ventricle area can generally be time-consuming, error-prone, and has interobserver variability. Thus, automatic delineation of the left ventricle area is necessary so that the assessment can be carried out effectively and efficiently. In this study, encoder-decoder based deep learning model for left ventricle segmentation in echocardiography was developed using the effective CNN U-Net encoder and combined with the deeplabv3+ decoder which has efficient performance and is able to produce sharper and more accurate segmentation results. Furthermore, the Atrous Spatial Pyramid Pooling module were added to the encoder to improve feature extraction. Tested on the Echonet-Dynamic dataset, the proposed model gives better results than the U-Net, DeeplabV3+, and DeeplabV3 models by producing a dice similarity coefficient of 92.87%. The experimental results show that combining the U-Net encoder and DeeplabV3+ decoder is able to provide increased performance compared to previous studies.

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Amer, Alyaa, Xujiong Ye, and Faraz Janan. "ResDUnet: A Deep Learning-Based Left Ventricle Segmentation Method for Echocardiography." IEEE Access 9 (2021): 159755–63. http://dx.doi.org/10.1109/access.2021.3122256.

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Andreassen, Borge Solli, Federico Veronesi, Olivier Gerard, Anne H. Schistad Solberg, and Eigil Samset. "Mitral Annulus Segmentation Using Deep Learning in 3-D Transesophageal Echocardiography." IEEE Journal of Biomedical and Health Informatics 24, no. 4 (April 2020): 994–1003. http://dx.doi.org/10.1109/jbhi.2019.2959430.

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Hu, Yujin, Bei Xia, Muyi Mao, Zelong Jin, Jie Du, Libao Guo, Alejandro F. Frangi, Baiying Lei, and Tianfu Wang. "AIDAN: An Attention-Guided Dual-Path Network for Pediatric Echocardiography Segmentation." IEEE Access 8 (2020): 29176–87. http://dx.doi.org/10.1109/access.2020.2971383.

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Bernard, Olivier, Johan G. Bosch, Brecht Heyde, Martino Alessandrini, Daniel Barbosa, Sorina Camarasu-Pop, Frederic Cervenansky, et al. "Standardized Evaluation System for Left Ventricular Segmentation Algorithms in 3D Echocardiography." IEEE Transactions on Medical Imaging 35, no. 4 (April 2016): 967–77. http://dx.doi.org/10.1109/tmi.2015.2503890.

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Pearlman, P. C., H. D. Tagare, B. A. Lin, A. J. Sinusas, and J. S. Duncan. "Segmentation of 3D radio frequency echocardiography using a spatio-temporal predictor." Medical Image Analysis 16, no. 2 (February 2012): 351–60. http://dx.doi.org/10.1016/j.media.2011.09.002.

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Dong, Suyu, Gongning Luo, Clara Tam, Wei Wang, Kuanquan Wang, Shaodong Cao, Bo Chen, Henggui Zhang, and Shuo Li. "Deep Atlas Network for Efficient 3D Left Ventricle Segmentation on Echocardiography." Medical Image Analysis 61 (April 2020): 101638. http://dx.doi.org/10.1016/j.media.2020.101638.

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Bersvendsen, Jørn, Fredrik Orderud, Øyvind Lie, Richard John Massey, Kristian Fosså, Raúl San José Estépar, Stig Urheim, and Eigil Samset. "Semiautomated biventricular segmentation in three-dimensional echocardiography by coupled deformable surfaces." Journal of Medical Imaging 4, no. 2 (May 24, 2017): 024005. http://dx.doi.org/10.1117/1.jmi.4.2.024005.

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Shekhar, R., V. Zagrodsky, and V. Walimbe. "3D Stress echocardiography: development of novel visualization, registration and segmentation algorithms." International Congress Series 1268 (June 2004): 1072–77. http://dx.doi.org/10.1016/j.ics.2004.03.107.

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Batool, Samana, Imtiaz Ahmad Taj, and Mubeen Ghafoor. "Ejection Fraction Estimation from Echocardiograms Using Optimal Left Ventricle Feature Extraction Based on Clinical Methods." Diagnostics 13, no. 13 (June 24, 2023): 2155. http://dx.doi.org/10.3390/diagnostics13132155.

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Echocardiography is one of the imaging systems most often utilized for assessing heart anatomy and function. Left ventricle ejection fraction (LVEF) is an important clinical variable assessed from echocardiography via the measurement of left ventricle (LV) parameters. Significant inter-observer and intra-observer variability is seen when LVEF is quantified by cardiologists using huge echocardiography data. Machine learning algorithms have the capability to analyze such extensive datasets and identify intricate patterns of structure and function of the heart that highly skilled observers might overlook, hence paving the way for computer-assisted diagnostics in this field. In this study, LV segmentation is performed on echocardiogram data followed by feature extraction from the left ventricle based on clinical methods. The extracted features are then subjected to analysis using both neural networks and traditional machine learning algorithms to estimate the LVEF. The results indicate that employing machine learning techniques on the extracted features from the left ventricle leads to higher accuracy than the utilization of Simpson’s method for estimating the LVEF. The evaluations are performed on a publicly available echocardiogram dataset, EchoNet-Dynamic. The best results are obtained when DeepLab, a convolutional neural network architecture, is used for LV segmentation along with Long Short-Term Memory Networks (LSTM) for the regression of LVEF, obtaining a dice similarity coefficient of 0.92 and a mean absolute error of 5.736%.

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Huang, Mu-Shiang, Chi-Shiang Wang, Jung-Hsien Chiang, Ping-Yen Liu, and Wei-Chuan Tsai. "Automated Recognition of Regional Wall Motion Abnormalities Through Deep Neural Network Interpretation of Transthoracic Echocardiography." Circulation 142, no. 16 (October 20, 2020): 1510–20. http://dx.doi.org/10.1161/circulationaha.120.047530.

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Background: Automated interpretation of echocardiography by deep neural networks could support clinical reporting and improve efficiency. Whereas previous studies have evaluated spatial relationships using still frame images, we aimed to train and test a deep neural network for video analysis by combining spatial and temporal information, to automate the recognition of left ventricular regional wall motion abnormalities. Methods: We collected a series of transthoracic echocardiography examinations performed between July 2017 and April 2018 in 2 tertiary care hospitals. Regional wall abnormalities were defined by experienced physiologists and confirmed by trained cardiologists. First, we developed a 3-dimensional convolutional neural network model for view selection ensuring stringent image quality control. Second, a U-net model segmented images to annotate the location of each left ventricular wall. Third, a final 3-dimensional convolutional neural network model evaluated echocardiographic videos from 4 standard views, before and after segmentation, and calculated a wall motion abnormality confidence level (0–1) for each segment. To evaluate model stability, we performed 5-fold cross-validation and external validation. Results: In a series of 10 638 echocardiograms, our view selection model identified 6454 (61%) examinations with sufficient image quality in all standard views. In this training set, 2740 frames were annotated to develop the segmentation model, which achieved a Dice similarity coefficient of 0.756. External validation was performed in 1756 examinations from an independent hospital. A regional wall motion abnormality was observed in 8.9% and 4.9% in the training and external validation datasets, respectively. The final model recognized regional wall motion abnormalities in the cross-validation and external validation datasets with an area under the receiver operating characteristic curve of 0.912 (95% CI, 0.896–0.928) and 0.891 (95% CI, 0.834–0.948), respectively. In the external validation dataset, the sensitivity was 81.8% (95% CI, 73.8%–88.2%), and specificity was 81.6% (95% CI, 80.4%–82.8%). Conclusions: In echocardiographic examinations of sufficient image quality, it is feasible for deep neural networks to automate the recognition of regional wall motion abnormalities using temporal and spatial information from moving images. Further investigation is required to optimize model performance and evaluate clinical applications.

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Maman, S. Ghanbari, A. Shalbaf, H. Behnam, Z. Alizadeh Sani, and M. Shojaei Fard. "FULLY AUTOMATIC SEGMENTATION OF LEFT VENTRICLE IN A SEQUENCE OF ECHOCARDIOGRAPHY IMAGES OF ONE CARDIAC CYCLE BY DYNAMIC DIRECTIONAL VECTOR FIELD CONVOLUTION (DDVFC) METHOD AND MANIFOLD LEARNING." Biomedical Engineering: Applications, Basis and Communications 25, no. 02 (April 2013): 1350022. http://dx.doi.org/10.4015/s1016237213500221.

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In this paper, an automatic method for segmentation of the left ventricle in two-dimensional (2D) echocardiography images of one cardiac cycle is proposed. In the first step of this method, using a mean image of a sequence of echocardiography images and its statistical properties the approximate region of left ventricle (LV) is extracted. Then the coordinate of extracted rectangular (ROI) is applied on all frames of sequences automatically. The mean image extracted ROI is used for defining the initial contour by scanning from the center point in polar coordinate. In the next step, all the extracted ROIs from the frames are mapped in a 2D space using the nonlinear dimension reduction manifold learning method. Using the properties of the manifold map end diastole (ED) and end systole (ES) frames are determined. Segmentation of the frames begins from ES frame, utilizing the dynamic directional vector field convolution (DDVFC) level set method with the initial contour as mentioned above. Final contour of each segmented frame is used as the initial contour of the next frame. Maximum range of the active contour motion is limited by a percent of the Euclidean distance between the point corresponds the current frame and the previous one in the resultant manifold. The results obtained from our method are quantitatively evaluated to those obtained by the gold contours drawn by a cardiologist on 489 echocardiographic images of seven volunteers using four distance measures: Hausdorff distance, average distance, area difference and area coverage error. We have also compared our results with the results of applying only DDVFC method. Comparing the implementation of only the DDVFC method, the results show final contours by proposed method are more close to contours drawn by a cardiologist.

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Kang, Seungyoung, Sun Ju Kim, Hong Gi Ahn, Kyoung-Chul Cha, and Sejung Yang. "Left ventricle segmentation in transesophageal echocardiography images using a deep neural network." PLOS ONE 18, no. 1 (January 20, 2023): e0280485. http://dx.doi.org/10.1371/journal.pone.0280485.

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Purpose There has been little progress in research on the best anatomical position for effective chest compressions and cardiac function during cardiopulmonary resuscitation (CPR). This study aimed to divide the left ventricle (LV) into segments to determine the best position for effective chest compressions using the LV systolic function seen during CPR. Methods We used transesophageal echocardiography images acquired during CPR. A deep neural network with an attention mechanism and a residual feature aggregation module were applied to the images to segment the LV. The results were compared between the proposed model and U-Net. Results The results of the proposed model showed higher performance in most metrics when compared to U-Net: dice coefficient (0.899±0.017 vs. 0.792±0.027, p<0.05); intersection of union (0.822±0.026 vs. 0.668±0.034, p<0.05); recall (0.904±0.023 vs. 0.757±0.037, p<0.05); precision (0.901±0.021 vs. 0.859±0.034, p>0.05). There was a significant difference between the proposed model and U-Net. Conclusion Compared to U-Net, the proposed model showed better performance for all metrics. This model would allow us to evaluate the systolic function of the heart during CPR in greater detail by segmenting the LV more accurately.

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Ginty, Olivia K., John M. Moore, Yuanwei Xu, Wenyao Xia, Satoru Fujii, Daniel Bainbridge, Terry M. Peters, Bob B. Kiaii, and Michael W. A. Chu. "Dynamic Patient-Specific Three-Dimensional Simulation of Mitral Repair." Innovations: Technology and Techniques in Cardiothoracic and Vascular Surgery 13, no. 1 (January 2018): 11–22. http://dx.doi.org/10.1097/imi.0000000000000463.

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Objective Planned mitral repair strategies are generally established from preoperative echocardiography; however, specific details of the repair are often determined intraoperatively. We propose that three-dimensional printed, patient-specific, dynamic mitral valve models may help surgeons plan and trial all the details of a specific patient's mitral repair preoperatively. Methods Using preoperative echocardiography, segmentation, modeling software, and three-dimensional printing, we created dynamic, high-fidelity, patient-specific mitral valve models including the subvalvular apparatus. We assessed the accuracy of 10 patient mitral valve models anatomically and functionally in a heart phantom simulator, both objectively by blinded echocardiographic assessment, and subjectively by two mitral repair experts. After this, we attempted model mitral repair and compared the outcomes with postoperative echocardiography. Results Model measurements were accurate when compared with patients on anterior-posterior diameter, circumference, and anterior leaflet length; however, less accurate on posterior leaflet length. On subjective assessment, Likert scores were high at 3.8 ± 0.4 and 3.4 ± 0.7, suggesting good fidelity of the dynamic model echocardiogram and functional model in the phantom to the preoperative three-dimensional echocardiogram, respectively. Mitral repair was successful in all 10 models with significant reduction in mitral insufficiency. In two models, mitral repair was performed twice, using two different surgical techniques to assess which provided a better outcome. When compared with the actual patient mitral repair outcome, the repaired models compared favorably. Conclusions Complex mitral valve modeling seems to predict an individual patient's mitral anatomy well, before surgery. Further investigation is required to determine whether deliberate preoperative practice can improve mitral repair outcomes.

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Wahlang, Imayanmosha, Sk Mahmudul Hassan, Arnab Kumar Maji, Goutam Saha, Michal Jasinski, Zbigniew Leonowicz, and Elzbieta Jasinska. "Classification of Valvular Regurgitation Using Echocardiography." Applied Sciences 12, no. 20 (October 17, 2022): 10461. http://dx.doi.org/10.3390/app122010461.

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Echocardiography (echo) is a commonly utilized tool in the diagnosis of various forms of valvular heart disease for its ability to detect types of cardiac regurgitation. Regurgitation represents irregularities in cardiac function and the early detection of regurgitation is necessary to avoid invasive cardiovascular surgery. In this paper, we focussed on the classification of regurgitations from videographic echo images. Three different types of regurgitation are considered in this work, namely, aortic regurgitation (AR), mitral regurgitation (MR), and tricuspid regurgitation (TR). From the echo images, texture features are extracted, and classification is performed using Random Forest (RF) classifier. Extraction of keyframe is performed from the video file using two approaches: a reference frame keyframe extraction technique and a redundant frame removal technique. To check the robustness of the model, we have considered both segmented and nonsegmented frames. Segmentation is carried out after keyframe extraction using the Level Set (LS) with Fuzzy C-means (FCM) approach. Performances are evaluated in terms of accuracy, precision, recall, and F1-score and compared for both reference frame and redundant frame extraction techniques. K-fold cross-validation is used to examine the performance of the model. The performance result shows that our proposed approach outperforms other state-of-art machine learning approaches in terms of accuracy, precision, recall, and F1-score.

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Cui, Xiaoxiao, Pengfei Zhang, Yujun Li, Zhi Liu, Xiaoyan Xiao, Yang Zhang, Longkun Sun, Lizhen Cui, Guang Yang, and Shuo Li. "MCAL: An Anatomical Knowledge Learning Model for Myocardial Segmentation in 2-D Echocardiography." IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control 69, no. 4 (April 2022): 1277–87. http://dx.doi.org/10.1109/tuffc.2022.3151647.

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Leclerc, Sarah, Erik Smistad, Joao Pedrosa, Andreas Ostvik, Frederic Cervenansky, Florian Espinosa, Torvald Espeland, et al. "Deep Learning for Segmentation Using an Open Large-Scale Dataset in 2D Echocardiography." IEEE Transactions on Medical Imaging 38, no. 9 (September 2019): 2198–210. http://dx.doi.org/10.1109/tmi.2019.2900516.

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Wu, H. S., D. Wang, L. Shi, and C. M. Yu. "Automatic segmentation of left ventricle in 3D echocardiography using a level set approach." International Journal of Cardiology 164, no. 2 (April 2013): S12—S13. http://dx.doi.org/10.1016/s0167-5273(13)70558-8.

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