Automatic Segmentation of Left Ventricle in Echocardiography Based on YOLOv3 Model to Achieve Constraint and Positioning

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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Assessment of automatic segmentation accuracy with various point cloud density

Geodesy and Cartography ◽

10.22389/0016-7126-2020-961-7-47-55 ◽

2020 ◽

Vol 961 (7) ◽

pp. 47-55

Author(s):

A.G. Yunusov ◽

A.J. Jdeed ◽

N.S. Begliarov ◽

M.A. Elshewy

Keyword(s):

Point Cloud ◽

Laser Scanning ◽

Reference Data ◽

Automatic Segmentation ◽

Geometric Accuracy ◽

Segmentation Method ◽

Segmentation Methods ◽

Segmentation Accuracy ◽

Number Of Segments ◽

Cloud Density

Laser scanning is considered as one of the most useful and fast technologies for modelling. On the other hand, the size of scan results can vary from hundreds to several million points. As a result, the large volume of the obtained clouds leads to complication at processing the results and increases the time costs. One way to reduce the volume of a point cloud is segmentation, which reduces the amount of data from several million points to a limited number of segments. In this article, we evaluated effect on the performance, the accuracy of various segmentation methods and the geometric accuracy of the obtained models at density changes taking into account the processing time. The results of our experiment were compared with reference data in a form of comparative analysis. As a conclusion, some recommendations for choosing the best segmentation method were proposed.

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Dynamic 3-dimensional echocardiographic reconstruction of the left ventricle using color doppler myocardial tissue imaging technique. In vivo experimental and clinical study

Journal of the American College of Cardiology ◽

10.1016/s0735-1097(96)80268-3 ◽

1996 ◽

Vol 27 (2) ◽

pp. 21 ◽

Cited By ~ 1

Author(s):

José Azevedo ◽

Miguel Garcia-Fernandez ◽

Pablo Puerta ◽

Mar Moreno ◽

Daniel SanRoman ◽

...

Keyword(s):

Clinical Study ◽

Left Ventricle ◽

Imaging Technique ◽

Color Doppler ◽

Tissue Imaging ◽

Myocardial Tissue ◽

3 Dimensional

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Automatic speech segmentation using throat-acoustic correlation coefficients

Open Engineering ◽

10.1515/eng-2016-0039 ◽

2016 ◽

Vol 6 (1) ◽

Cited By ~ 2

Author(s):

Rustam Rafikovich Mussabayev ◽

Maksat N. Kalimoldayev ◽

Yedilkhan N. Amirgaliyev ◽

Timur R. Mussabayev

Keyword(s):

Large Volume ◽

Automatic Segmentation ◽

Correlation Coefficients ◽

High Accuracy ◽

Speech Segmentation ◽

Preliminary Training ◽

Effi Ciency ◽

Segmentation Accuracy ◽

New Type ◽

The Given

Abstract This work considers one of the approaches to the solution of the task of discrete speech signal automatic segmentation. The aim of this work is to construct such an algorithm which should meet the following requirements: segmentation of a signal into acoustically homogeneous segments, high accuracy and segmentation speed, unambiguity and reproducibility of segmentation results, lack of necessity of preliminary training with the use of a special set consisting of manually segmented signals. Development of the algorithm which corresponds to the given requirements was conditioned by the necessity of formation of automatically segmented speech databases that have a large volume. One of the new approaches to the solution of this task is viewed in this article. For this purpose we use the new type of informative features named TAC-coefficients (Throat-Acoustic Correlation coefficients) which provide sufficient segmentation accuracy and effi- ciency.

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Automatic segmentation of the left ventricle in cardiac CT angiography using convolutional neural networks

2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI) ◽

10.1109/isbi.2016.7493206 ◽

2016 ◽

Cited By ~ 17

Author(s):

Majd Zreik ◽

Tim Leiner ◽

Bob D. de Vos ◽

Robbert W. van Hamersvelt ◽

Max A. Viergever ◽

...

Keyword(s):

Neural Networks ◽

Left Ventricle ◽

Ct Angiography ◽

Convolutional Neural Networks ◽

Cardiac Ct ◽

Automatic Segmentation ◽

Cardiac Ct Angiography

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Segmentation of Multi-Tumor from PET/CT Images

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.l1030.10812s219 ◽

2019 ◽

Vol 8 (12S2) ◽

pp. 170-176

Keyword(s):

Automatic Segmentation ◽

Mean Shift ◽

Ground Truth ◽

Histogram Equalization ◽

Ct Images ◽

Svm Classifier ◽

Segmentation Evaluation ◽

Multiple Tumor ◽

Mean Shift Clustering ◽

Pet Ct

The aim of the project is to develop a methodology for automatic segmentation of multiple tumor from PET/CT images. Image pre-processing methods such as Contrast Limited Adaptive Histogram Equalization (CLAHE), image sharpening and Wiener filtering were performed to remove the artifacts due to contrast variations and noise. The image was segmented using K-means, Threshold segmentation, watershed segmentation, FCM clustering Segmentation, Mean shift Clustering Segmentation, Graph Cut Segmentation. Evaluation was made for the segmentation against the Ground Truth. Various Features was selected and extracted. Classification was made using SVM classifier and KNN classifier to classify the tumor as benign or malignant. The proposed method was carried out using PET/CT images of lung cancer patients and implemented using MATLAB.

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A joint multi-scale convolutional network for fully automatic segmentation of the left ventricle

2017 IEEE International Conference on Image Processing (ICIP) ◽

10.1109/icip.2017.8296855 ◽

2017 ◽

Author(s):

Qianqian Tong ◽

Zhiyong Yuan ◽

Xiangyun Liao ◽

Mianlun Zheng ◽

Weixu Zhu ◽

...

Keyword(s):

Left Ventricle ◽

Automatic Segmentation ◽

Convolutional Network ◽

Multi Scale ◽

Fully Automatic

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Automatic Segmentation and Functional Assessment of the Left Ventricle using U-net Fully Convolutional Network

2019 IEEE International Conference on Imaging Systems and Techniques (IST) ◽

10.1109/ist48021.2019.9010123 ◽

2019 ◽

Author(s):

Hisham Abdeltawab ◽

Fahmi Khalifa ◽

Fatma Taher ◽

Garth Beache ◽

Tamer Mohamed ◽

...

Keyword(s):

Left Ventricle ◽

Functional Assessment ◽

Automatic Segmentation ◽

Convolutional Network ◽

Fully Convolutional Network

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Optimizing the automatic segmentation of the left ventricle in magnetic resonance images

Medical Physics ◽

10.1118/1.1842912 ◽

2005 ◽

Vol 32 (2) ◽

pp. 369-375 ◽

Cited By ~ 16

Author(s):

E. Angelié ◽

P. J. H. de Koning ◽

M. G. Danilouchkine ◽

H. C. van Assen ◽

G. Koning ◽

...

Keyword(s):

Magnetic Resonance ◽

Left Ventricle ◽

Automatic Segmentation ◽

Magnetic Resonance Images

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Investigating the Relevance of Graph Cut Parameter on Interactive and Automatic Cell Segmentation

Computational and Mathematical Methods in Medicine ◽

10.1155/2018/7396910 ◽

2018 ◽

Vol 2018 ◽

pp. 1-10 ◽

Cited By ~ 1

Author(s):

Kazeem Oyeyemi Oyebode ◽

Shengzhi Du ◽

Barend Jacobus van Wyk ◽

Karim Djouani

Keyword(s):

Statistical Analysis ◽

Significant Role ◽

Energy Function ◽

Automatic Segmentation ◽

Graph Cut ◽

Cell Segmentation ◽

Medical Field ◽

Segmentation Strategy ◽

Segmentation Accuracy ◽

Graph Cut Segmentation

Graph cut segmentation provides a platform to analyze images through a global segmentation strategy, and as a result of this, it has gained a wider acceptability in many interactive and automatic segmentation fields of application, such as the medical field. The graph cut energy function has a parameter that is tuned to ensure that the output is neither oversegmented (shrink bias) nor undersegmented. Models have been proposed in literature towards the improvement of graph cut segmentation, in the context of interactive and automatic cell segmentation. Along this line of research, the graph cut parameter has been leveraged, while in some instances, it has been ignored. Therefore, in this work, the relevance of graph cut parameter on both interactive and automatic cell segmentation is investigated. Statistical analysis, based on F1 score, of three publicly available datasets of cells, suggests that the graph cut parameter plays a significant role in improving the segmentation accuracy of the interactive graph cut than the automatic graph cut.

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