scholarly journals A Combined Fully Convolutional Networks and Deformable Model for Automatic Left Ventricle Segmentation Based on 3D Echocardiography

2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Suyu Dong ◽  
Gongning Luo ◽  
Kuanquan Wang ◽  
Shaodong Cao ◽  
Qince Li ◽  
...  
Keyword(s):  
Left Ventricle ◽  
Feature Fusion ◽  
Three Dimensional ◽  
Geometrical Model ◽  
Deformable Model ◽  
Lv Function ◽  
Practical Applications ◽  
Convolutional Networks ◽  
Fully Convolutional Networks ◽  
Three Dimensional Echocardiography

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.

scholarly journals Deep RetinaNet for Dynamic Left Ventricle Detection in Multiview Echocardiography Classification

2020 ◽  
Vol 2020 ◽  
pp. 1-6 ◽  
Author(s):  
Meijun Yang ◽  
Xiaoyan Xiao ◽  
Zhi Liu ◽  
Longkun Sun ◽  
Wei Guo ◽  
...  
Keyword(s):  
Left Ventricle ◽  
Three Dimensional ◽  
Detection Algorithm ◽  
Convolutional Networks ◽  
Fully Convolutional Networks ◽  
Dimensional Reconstruction ◽  
Feature Pyramid ◽  
The Mean ◽  
Accuracy Rates

Background. Currently, echocardiography has become an essential technology for the diagnosis of cardiovascular diseases. Accurate classification of apical two-chamber (A2C), apical three-chamber (A3C), and apical four-chamber (A4C) views and the precise detection of the left ventricle can significantly reduce the workload of clinicians and improve the reproducibility of left ventricle segmentation. In addition, left ventricle detection is significant for the three-dimensional reconstruction of the heart chambers. Method. RetinaNet is a one-stage object detection algorithm that can achieve high accuracy and efficiency at the same time. RetinaNet is mainly composed of the residual network (ResNet), the feature pyramid network (FPN), and two fully convolutional networks (FCNs); one FCN is for the classification task, and the other is for the border regression task. Results. In this paper, we use the classification subnetwork to classify A2C, A3C, and A4C images and use the regression subnetworks to detect the left ventricle simultaneously. We display not only the position of the left ventricle on the test image but also the view category on the image, which will facilitate the diagnosis. We used the mean intersection-over-union (mIOU) as an index to measure the performance of left ventricle detection and the accuracy as an index to measure the effect of the classification of the three different views. Our study shows that both classification and detection effects are noteworthy. The classification accuracy rates of A2C, A3C, and A4C are 1.000, 0.935, and 0.989, respectively. The mIOU values of A2C, A3C, and A4C are 0.858, 0.794, and 0.838, respectively.

scholarly journals Building Multi-Feature Fusion Refined Network for Building Extraction from High-Resolution Remote Sensing Images

2021 ◽  
Vol 13 (14) ◽  
pp. 2794
Author(s):  
Shuhao Ran ◽  
Xianjun Gao ◽  
Yuanwei Yang ◽  
Shaohua Li ◽  
Guangbin Zhang ◽  
...  
Keyword(s):  
Feature Fusion ◽  
Small Scale ◽  
Automatic Extraction ◽  
Learning Approaches ◽  
Building Extraction ◽  
Visual Interpretation ◽  
Learning Capacity ◽  
Convolutional Networks ◽  
Fully Convolutional Networks ◽  
Great Progress

Deep learning approaches have been widely used in building automatic extraction tasks and have made great progress in recent years. However, the missing detection and wrong detection causing by spectrum confusion is still a great challenge. The existing fully convolutional networks (FCNs) cannot effectively distinguish whether the feature differences are from one building or the building and its adjacent non-building objects. In order to overcome the limitations, a building multi-feature fusion refined network (BMFR-Net) was presented in this paper to extract buildings accurately and completely. BMFR-Net is based on an encoding and decoding structure, mainly consisting of two parts: the continuous atrous convolution pyramid (CACP) module and the multiscale output fusion constraint (MOFC) structure. The CACP module is positioned at the end of the contracting path and it effectively minimizes the loss of effective information in multiscale feature extraction and fusion by using parallel continuous small-scale atrous convolution. To improve the ability to aggregate semantic information from the context, the MOFC structure performs predictive output at each stage of the expanding path and integrates the results into the network. Furthermore, the multilevel joint weighted loss function effectively updates parameters well away from the output layer, enhancing the learning capacity of the network for low-level abstract features. The experimental results demonstrate that the proposed BMFR-Net outperforms the other five state-of-the-art approaches in both visual interpretation and quantitative evaluation.

Comparing of two dimensional and three dimensional fully convolutional networks for radiotherapy dose prediction in left-sided breast cancer

2021 ◽  
Vol 104 (3) ◽  
pp. 003685042110381
Author(s):  
Xue Bai ◽  
Ze Liu ◽  
Jie Zhang ◽  
Shengye Wang ◽  
Qing Hou ◽  
...  
Keyword(s):  
Three Dimensional ◽  
Region Of Interest ◽  
Dice Similarity Coefficient ◽  
Two Dimensional ◽  
Dimensional Model ◽  
Dose Distributions ◽  
Convolutional Networks ◽  
Dose Prediction ◽  
Fully Convolutional Networks ◽  
Percentage Difference

Fully convolutional networks were developed for predicting optimal dose distributions for patients with left-sided breast cancer and compared the prediction accuracy between two-dimensional and three-dimensional networks. Sixty cases treated with volumetric modulated arc radiotherapy were analyzed. Among them, 50 cases were randomly chosen to conform the training set, and the remaining 10 were to construct the test set. Two U-Net fully convolutional networks predicted the dose distributions, with two-dimensional and three-dimensional convolution kernels, respectively. Computed tomography images, delineated regions of interest, or their combination were considered as input data. The accuracy of predicted results was evaluated against the clinical dose. Most types of input data retrieved a similar dose to the ground truth for organs at risk ( p > 0.05). Overall, the two-dimensional model had higher performance than the three-dimensional model ( p < 0.05). Moreover, the two-dimensional region of interest input provided the best prediction results regarding the planning target volume mean percentage difference (2.40 ± 0.18%), heart mean percentage difference (4.28 ± 2.02%), and the gamma index at 80% of the prescription dose are with tolerances of 3 mm and 3% (0.85 ± 0.03), whereas the two-dimensional combined input provided the best prediction regarding ipsilateral lung mean percentage difference (4.16 ± 1.48%), lung mean percentage difference (2.41 ± 0.95%), spinal cord mean percentage difference (0.67 ± 0.40%), and 80% Dice similarity coefficient (0.94 ± 0.01). Statistically, the two-dimensional combined inputs achieved higher prediction accuracy regarding 80% Dice similarity coefficient than the two-dimensional region of interest input (0.94 ± 0.01 vs 0.92 ± 0.01, p < 0.05). The two-dimensional data model retrieves higher performance than its three-dimensional counterpart for dose prediction, especially when using region of interest and combined inputs.

scholarly journals Transfer Change Rules from Recurrent Fully Convolutional Networks for Hyperspectral Unmanned Aerial Vehicle Images without Ground Truth Data

2020 ◽  
Vol 12 (7) ◽  
pp. 1099 ◽  
Author(s):  
Ahram Song ◽  
Yongil Kim
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Three Dimensional ◽  
Ground Truth ◽  
Training Data ◽  
Fine Tuning ◽  
Target Domain ◽  
Ground Truth Data ◽  
Convolutional Networks ◽  
Fully Convolutional Networks ◽  
Aerial Vehicle

Change detection (CD) networks based on supervised learning have been used in diverse CD tasks. However, such supervised CD networks require a large amount of data and only use information from current images. In addition, it is time consuming to manually acquire the ground truth data for newly obtained images. Here, we proposed a novel method for CD in case of a lack of training data in an area near by another one with the available ground truth data. The proposed method automatically entails generating training data and fine-tuning the CD network. To detect changes in target images without ground truth data, the difference images were generated using spectral similarity measure, and the training data were selected via fuzzy c-means clustering. Recurrent fully convolutional networks with multiscale three-dimensional filters were used to extract objects of various sizes from unmanned aerial vehicle (UAV) images. The CD network was pre-trained on labeled source domain data; then, the network was fine-tuned on target images using generated training data. Two further CD networks were trained with a combined weighted loss function. The training data in the target domain were iteratively updated using he prediction map of the CD network. Experiments on two hyperspectral UAV datasets confirmed that the proposed method is capable of transferring change rules and improving CD results based on training data extracted in an unsupervised way.

SPECKLE TRACKING : A NEW PROGRESS IN ECHOCARDIOGRPAHY

2011 ◽  
pp. 48-53
Author(s):  
Anh Vu Nguyen
Keyword(s):  
Speckle Tracking ◽  
Speckle Tracking Echocardiography ◽  
Three Dimensional ◽  
Left Ventricular ◽  
Lv Function ◽  
Intraobserver Variability ◽  
Global Function ◽  
Recent Developments ◽  
Dimensional Echocardiography ◽  
Three Dimensional Echocardiography

The author summarizes the recent developments in speckle-tracking echocardiography (STE), a relatively new technique that can be used in conjunction with two-dimensional or three-dimensional echocardiography for resolving the multidirectional components of left ventricular (LV) deformation. STE quantify accurately the regional and global function of the left ventricle. STE holds promise to reduce interobserver and intraobserver variability in assessing regional LV function. Following a brief overview of the approach, the authors pool the initial observations from clinical studies on the development, validation, merits, and limitations of STE.

Comparison of biplane manual, automated volumetric and MRI methods for assessment of left ventricle volumes and function in a series of three left ventricle non-compaction patients

2021 ◽  
Author(s):  
Katarzyna Justyna Pigon ◽  
Edyta Radzik ◽  
Krystyna Krzemień-Wolska ◽  
Brygida Przywara-Chowaniec ◽  
Ewa Nowalany-Kozielska ◽  
...  
Keyword(s):  
Left Ventricle ◽  
Speckle Tracking ◽  
Three Dimensional ◽  
Golden Rule ◽  
Ventricular Myocardium ◽  
Two Dimensional ◽  
Prognostic Information ◽  
Dimensional Echocardiography ◽  
Three Dimensional Echocardiography ◽  
And Function

Abstract Background Non-compaction of the ventricular myocardium (LVNC) is a rare genetically determined heart defect. The disease is most frequently detected during accidental echocardiography. There is no golden rule examination for LVNC diagnosis. In our study, we aimed to compare the measurements of the left ventricle volumes and function obtained with the two-dimensional, and three-dimensional echo based on the results from MR imaging in a series of three left ventricle non-compaction cardiomyopathy patients. Case presentation Patients’ age was 43–53 years. Two patients presented with congestive heart failure, third patient presented with ventricular arrhythmia. The proportion of thickness of non-compacted to compacted layers ranged from 2.3 to 3.6. Three-dimensional echo has revealed substantially higher end-diastolic and end-systolic volumes in comparison to two-dimensional echo, yet not as high as assessed in magnetic resonance imaging. Speckle tracking revealed reduced global longitudinal, radial, and circumferential strain, even in the patients with normal left ventricle function. Conclusion Real-time three-dimensional echocardiography with automated left ventricle analysis offers more information on the assessment of left ventricle in patients with left ventricle non-compaction in comparison to two-dimensional echocardiography. Speckle tracking analysis may add prognostic information in this specific group of patients.

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