VC-Net: Deep Volume-Composition Networks for Segmentation and Visualization of Highly Sparse and Noisy Image Data

Wang, Yifan; Yan, Guoli; Zhu, Haikuan; Buch, Sagar; Wang, Ying; Haacke, E. Mark; Hua, Jing; Zhong, Zichun

doi:10.1109/tvcg.2020.3030374

Cited by 32 publications

(25 citation statements)

References 39 publications

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“…Visualization technology plays an important role in providing doctors with fast and accurate medical images in a noninvasive manner, providing aids to diagnosis, surgical navigation, treatment guidance, and medical teaching. Curvilinear reconstruction, a technique that uses centerlines to visualize tubular structures, has become a must-have feature of medical workstations, with features such as length retention, allowing the entire vessel or even the entire vascular tree to be displayed on a single image [ 26 ]. The main ones currently used are projection CPR, extension CPR, and straightening CPR.…”

Section: Current Status Of Researchmentioning

confidence: 99%

Detection of 3D Arterial Centerline Extraction in Spiral CT Coronary Angiography

Cai

Wang

et al. 2021

Journal of Healthcare Engineering

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This paper presents an in-depth study and analysis of the 3D arterial centerline in spiral CT coronary angiography, and constructs its detection and extraction technique. The first time, the distance transform is used to complete the boundary search of the original figure; the second time, the distance transform is used to calculate the value of the distance transform of all voxels, and according to the value of the distance transform, unnecessary voxels are deleted, to complete the initial contraction of the vascular region and reduce the computational consumption in the next process; then, the nonwitnessed voxels are used to construct the maximum inner joint sphere model and find the skeletal voxels that can reflect the shape of the original figure. Finally, the skeletal lines were optimized on these initially extracted skeletal voxels using a dichotomous-like principle to obtain the final coronary artery centerline. Through the evaluation of the experimental results, the algorithm can extract the coronary centerline more accurately. In this paper, the segmentation method is evaluated on the test set data by two kinds of indexes: one is the index of segmentation result evaluation, including dice coefficient, accuracy, specificity, and sensitivity; the other is the index of clinical diagnosis result evaluation, which is to refine the segmentation result for vessel diameter detection. The results obtained in this paper were compared with the physicians’ labeling results. In terms of network performance, the Dice coefficient obtained in this paper was 0.89, the accuracy was 98.36%, the sensitivity was 93.36%, and the specificity was 98.76%, which reflected certain advantages in comparison with the advanced methods proposed by previous authors. In terms of clinical evaluation indexes, by performing skeleton line extraction and diameter calculation on the results obtained by the segmentation method proposed in this paper, the absolute error obtained after comparing with the diameter of the labeled image was 0.382 and the relative error was 0.112, which indicates that the segmentation method in this paper can recover the vessel contour more accurately. Then, the results of coronary artery centerline extraction with and without fine branch elimination were evaluated, which proved that the coronary artery centerline has higher accuracy after fine branch elimination. The algorithm is also used to extract the centerline of the complete coronary artery tree, and the results prove that the algorithm has better results for the centerline extraction of the complete coronary vascular tree.

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Section: Current Status Of Researchmentioning

confidence: 99%

Detection of 3D Arterial Centerline Extraction in Spiral CT Coronary Angiography

Cai

Wang

et al. 2021

Journal of Healthcare Engineering

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“…The authors also evaluated the behavior of understanding the advantages and disadvantages of the model more minutely. Wang Y. et al (2020) studied the deep volume synthesis network for segmentation and visualization of highly sparse and noisy image data. The authors constructed a multi-stream CNN framework to effectively learn three-dimensional volume and two-dimensional eigenvectors, respectively.…”

Section: Recent Related Workmentioning

confidence: 99%

Deep Learning Based Emotion Recognition and Visualization of Figural Representation

2022

Front. Psychol.

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This exploration aims to study the emotion recognition of speech and graphic visualization of expressions of learners under the intelligent learning environment of the Internet. After comparing the performance of several neural network algorithms related to deep learning, an improved convolution neural network-Bi-directional Long Short-Term Memory (CNN-BiLSTM) algorithm is proposed, and a simulation experiment is conducted to verify the performance of this algorithm. The experimental results indicate that the Accuracy of CNN-BiLSTM algorithm reported here reaches 98.75%, which is at least 3.15% higher than that of other algorithms. Besides, the Recall is at least 7.13% higher than that of other algorithms, and the recognition rate is not less than 90%. Evidently, the improved CNN-BiLSTM algorithm can achieve good recognition results, and provide significant experimental reference for research on learners’ emotion recognition and graphic visualization of expressions in an intelligent learning environment.

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“…For example, inference can be performed more efficiently than conventional methods once a neural network is trained [45]. Furthermore, DL solutions offer a performance boost, such as data interpolation quality [55], reduction rate [109], or segmentation accuracy [154], compared with non-DL solutions. SciVis data and tasks share significant similarities with those in computer vision (CV) and computer graphics (CG).…”

Section: Introductionmentioning

confidence: 99%

DL4SciVis: A State-of-the-Art Survey on Deep Learning for Scientific Visualization

Wang¹,

Han²

2022

Preprint

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Since 2016, we have witnessed the tremendous growth of artificial intelligence+visualization (AI+VIS) research. However, existing survey papers on AI+VIS focus on visual analytics and information visualization, not scientific visualization (SciVis). In this paper, we survey related deep learning (DL) works in SciVis, specifically in the direction of DL4SciVis: designing DL solutions for solving SciVis problems. To stay focused, we primarily consider works that handle scalar and vector field data but exclude mesh data. We classify and discuss these works along six dimensions: domain setting, research task, learning type, network architecture, loss function, and evaluation metric. The paper concludes with a discussion of the remaining gaps to fill along the discussed dimensions and the grand challenges we need to tackle as a community. This state-of-the-art survey guides SciVis researchers in gaining an overview of this emerging topic and points out future directions to grow this research.

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VC-Net: Deep Volume-Composition Networks for Segmentation and Visualization of Highly Sparse and Noisy Image Data

Cited by 32 publications

References 39 publications

Detection of 3D Arterial Centerline Extraction in Spiral CT Coronary Angiography

Detection of 3D Arterial Centerline Extraction in Spiral CT Coronary Angiography

Deep Learning Based Emotion Recognition and Visualization of Figural Representation

DL4SciVis: A State-of-the-Art Survey on Deep Learning for Scientific Visualization

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