Improvement of Ultrasonic Image by a Combination of Correlation-based Image Processing and Virtual Sources Applied to Non-destructive Evaluation

Souza, Rayanne; Freitas, Miguel; Kubrusly, Alan C.; Weid, J. P. von der

doi:10.1109/iwssip48289.2020.9145248

Cited by 1 publication

(1 citation statement)

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“…In the clinical diagnosis of fetal development during pregnancy, ultrasound images help doctors better understand the situation, and provide sufficient basis for the judgment whether the fetal development meets the standard or is abnormal [1][2][3][4][5][6]. In order to make an in-depth analysis of the target in the uterus, it is necessary to segment the target from the ultrasound image [7][8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Fetal Development Ultrasound Image Segmentation and Registration

Zhang¹,

Cao²

2023

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Image segmentation and registration are the premise of ultrasonic image analysis. The key of computer-aided clinical diagnosis of fetal development is to improve the accuracy and speed of ultrasound image segmentation and registration, which is worth further discussion. As for the existing research results, problems still remain in the accuracy and effect of segmentation and registration. Therefore, this paper studied the fetal development ultrasound image segmentation and registration based on deep learning. In Chapter 2, the paper designed a convolution module, dividing the feature information generation process into two steps. The introduced self-tuning lightweight segmentation module and channel attention module were used to enhance the expression ability of features and improve the segmentation performance of the Convolutional Neural Network (CNN), respectively. In Chapter 3, this paper constructed a full-CNN model based on joint training to perform nonrigid registration of fetal development ultrasound images, which reduced the computational complexity of the model. The experimental results verified the effectiveness of the model.

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Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Fetal Development Ultrasound Image Segmentation and Registration

Zhang¹,

Cao²

2023

View full text Add to dashboard Cite

show abstract

Improvement of Ultrasonic Image by a Combination of Correlation-based Image Processing and Virtual Sources Applied to Non-destructive Evaluation

Cited by 1 publication

References 14 publications

Deep Learning-Based Fetal Development Ultrasound Image Segmentation and Registration

Deep Learning-Based Fetal Development Ultrasound Image Segmentation and Registration

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