A deep neural network based correction scheme for improved air-tissue boundary prediction in real-time magnetic resonance imaging video

Mannem, Renuka; Ghosh, Prasanta Kumar

doi:10.1016/j.csl.2020.101160

Cited by 3 publications

(3 citation statements)

References 17 publications

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“…Mannem and Ghosh proposed a correction scheme for improved air tissue boundary segmentation based on deep neural networks by using a normal grid-based method to generate the input and output targets required for training. e results show that this method is more accurate in regularizing distances [8]. Sagheer and Kotb envisioned a neural network-based model for oil production forecasting.…”

Section: Introductionmentioning

confidence: 94%

Application of Neural Network and Virtual Reality Technology in Digital Video Effects

Jiang

2022

International Transactions on Electrical Energy Systems

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With the continuous innovation of technology, digital technology is more and more widely used in the production of TV shows and movies and plays an important role. The effective application of digital video effects technology can enhance the visual effects of the overall TV and film works and improve the quality of the works. After the use of digital special effects in film and television production, people see more exquisite film and television pictures, but today’s technology is not enough to meet people’s growing demand for film and television pictures. This paper applied neural network and virtual reality technology to the production process of digital video effects and conducted a comparative experiment with the digital video effects produced by using the current technology. The special effects were evaluated in four aspects: image fidelity, sound distortion, picture fluency, and production efficiency. The experimental results show that after applying neural network and virtual reality technologies, the image fidelity of digital video effects is increased by 6.74%, the sound distortion is reduced by 7.69%, the picture fluency is increased by 6.89%, and the production efficiency is increased by 7.91%. Applying the neural network and virtual reality technology can improve the quality and efficiency of digital video special effects production.

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Section: Introductionmentioning

confidence: 94%

Application of Neural Network and Virtual Reality Technology in Digital Video Effects

Jiang

2022

International Transactions on Electrical Energy Systems

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“…Previous methodologies have implemented greater automation by leveraging deep learning techniques. However, these rely on training data sourced from a relatively narrow sample of behaviours, imaging centres, and pulse sequences (e.g., Asadiabadi & Erzin, 2020 ; Bresch & Narayanan, 2009 ; Eslami et al, 2020 ; Labrunie et al, 2018 ; van Leeuwen et al, 2019 ; Mannem & Ghosh, 2021 ; Pandey & Sabbir Arif, 2021 ; Ruthven et al, 2021 ; Silva & Teixeira, 2015 ; Somandepalli et al, 2017 ; Takemoto et al, 2019 ; Valliappan et al, 2019 ) . While our pipeline is more labour-intensive, we have demonstrated that it generalises beyond the dataset for which it was designed (see Belyk et al, 2022 ).…”

Section: Discussionmentioning

confidence: 99%

“…A promising avenue for development has come from the application of machine learning, and deep learning algorithms in particular (Goodfellow et al, 2016 ). This is a field of statistics that continues to develop rapidly, and several implementations have been proposed for applications to rtMRI (e.g., Asadiabadi & Erzin, 2020 ; Bresch & Narayanan, 2009 ; Eslami et al, 2020 ; Labrunie et al, 2018 ; van Leeuwen et al, 2019 ; Mannem & Ghosh, 2021 ; Pandey & Sabbir Arif, 2021 ; Ruthven et al, 2021 ; Silva & Teixeira, 2015 ; Somandepalli et al, 2017 ; Takemoto et al, 2019 ; Valliappan et al, 2019 ). Deep-learning-based approaches can yield machine-generated traces of the vocal tract which are sufficiently accurate to be useful for scientific measurements.…”

Section: Introductionmentioning

confidence: 99%

An open-source toolbox for measuring vocal tract shape from real-time magnetic resonance images

2023

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Real-time magnetic resonance imaging (rtMRI) is a technique that provides high-contrast videographic data of human anatomy in motion. Applied to the vocal tract, it is a powerful method for capturing the dynamics of speech and other vocal behaviours by imaging structures internal to the mouth and throat. These images provide a means of studying the physiological basis for speech, singing, expressions of emotion, and swallowing that are otherwise not accessible for external observation. However, taking quantitative measurements from these images is notoriously difficult. We introduce a signal processing pipeline that produces outlines of the vocal tract from the lips to the larynx as a quantification of the dynamic morphology of the vocal tract. Our approach performs simple tissue classification, but constrained to a researcher-specified region of interest. This combination facilitates feature extraction while retaining the domain-specific expertise of a human analyst. We demonstrate that this pipeline generalises well across datasets covering behaviours such as speech, vocal size exaggeration, laughter, and whistling, as well as producing reliable outcomes across analysts, particularly among users with domain-specific expertise. With this article, we make this pipeline available for immediate use by the research community, and further suggest that it may contribute to the continued development of fully automated methods based on deep learning algorithms.

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Improving CNN-based semantic segmentation on structurally similar data using contrastive graph convolutional networks

Chen,

Tang,

2024

Pattern Recognition

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A deep neural network based correction scheme for improved air-tissue boundary prediction in real-time magnetic resonance imaging video

Cited by 3 publications

References 17 publications

Application of Neural Network and Virtual Reality Technology in Digital Video Effects

Application of Neural Network and Virtual Reality Technology in Digital Video Effects

An open-source toolbox for measuring vocal tract shape from real-time magnetic resonance images

Improving CNN-based semantic segmentation on structurally similar data using contrastive graph convolutional networks

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