LAEDNet: A Lightweight Attention Encoder–Decoder Network for ultrasound medical image segmentation

Zhou, Quan; Wang, Qianwen; Bao, Yunchao; Kong, Lingjun; Jin, Xin; Ou, Weihua

doi:10.1016/j.compeleceng.2022.107777

Cited by 41 publications

(9 citation statements)

References 22 publications

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“…Moreover, many research studies have found that EfficientNet is more effective and accurate than others. This has been observed in various fields such as medical (Zhou et al 2022 ; Sharma et al 2023 ) as well as engineering (Nguon et al 2022 ; Jin et al 2022 ).…”

Section: Resultsmentioning

confidence: 82%

Ultrasonic tomography imaging enhancement approach based on deep convolutional neural networks

Malikov

Cuenca

Kim

et al. 2023

J Vis

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The containment liner plate (CLP) is a thin layer of carbon steel material applied as a base for concrete structures protecting nuclear material. The structural health monitoring of the CLP is critical to ensure the safety of nuclear power plants. Hidden defects in the CLP can be identified utilizing ultrasonic tomographic imaging techniques such as the reconstruction algorithm for the probabilistic inspection of damage (RAPID) methodology. However, Lamb waves have a multimodal dispersion feature, which makes the selection of a single mode more difficult. Thus, sensitivity analysis was utilized since it allows for the determination of each mode's level of sensitivity as a function of frequency; the S0 mode was chosen after examining the sensitivity. Even though proper Lamb wave mode was selected, the tomographic image had blurred zones. Blurring reduces the precision of an ultrasonic image and makes it more difficult to distinguish the dimensions of the flaw. To enhance the tomographic image of the CLP, deep learning architecture such as U-Net was utilized for the segmentation of the experimental ultrasonic tomographic image, which includes an encoder and decoder part for better visualization of the tomographic image. Nevertheless, collecting enough ultrasonic images to train the U-Net model was not economically feasible, and only a small number of the CLP specimens can be tested. Thus, it was necessary to utilize transfer learning and get the values of the parameters from a pre-trained model with a much larger dataset as a starting point for a new task, rather than training a new model from scratch. Through these deep learning approaches, we were able to eliminate the blurred section of the ultrasonic tomography, leading to images with clear edges of defects and no blurred zones. Graphical abstract

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Section: Resultsmentioning

confidence: 82%

Ultrasonic tomography imaging enhancement approach based on deep convolutional neural networks

Malikov

Cuenca

Kim

et al. 2023

J Vis

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“…Zhou et al introduced the Lightweight Attention Encoder–Decoder Network (LAEDNet) [ 87 ], an innovative and efficient asymmetrical encoder–decoder network, for the segmentation of the Head Circumference Ultrasound Images Dataset (HCUS).…”

Section: Organsmentioning

confidence: 99%

Ultrasound Image Analysis with Vision Transformers—Review

Vafaeezadeh,

Behnam,

Gifani

2024

Diagnostics

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Ultrasound (US) has become a widely used imaging modality in clinical practice, characterized by its rapidly evolving technology, advantages, and unique challenges, such as a low imaging quality and high variability. There is a need to develop advanced automatic US image analysis methods to enhance its diagnostic accuracy and objectivity. Vision transformers, a recent innovation in machine learning, have demonstrated significant potential in various research fields, including general image analysis and computer vision, due to their capacity to process large datasets and learn complex patterns. Their suitability for automatic US image analysis tasks, such as classification, detection, and segmentation, has been recognized. This review provides an introduction to vision transformers and discusses their applications in specific US image analysis tasks, while also addressing the open challenges and potential future trends in their application in medical US image analysis. Vision transformers have shown promise in enhancing the accuracy and efficiency of ultrasound image analysis and are expected to play an increasingly important role in the diagnosis and treatment of medical conditions using ultrasound imaging as technology progresses.

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“…(2) The execution process of the deep learning model requires a large number of computing resources, such as advanced GPU devices [122]. Since radiologists can't always use expensive high-performance computing equipment during the diagnosis process, the lightweight models are a potential research direction.…”

Section: Key Challenges and Potential Solutionsmentioning

confidence: 99%

Deep Learning Attention Mechanism in Medical Image Analysis: Basics and Beyonds

Li²,

Yan³

et al. 2023

IJNDI

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Survey/review study Deep Learning Attention Mechanism in Medical Image Analysis: Basics and Beyonds Xiang Li 1, Minglei Li 1, Pengfei Yan 1, Guanyi Li 1, Yuchen Jiang 1, Hao Luo 1,*, and Shen Yin 2 1 Department of Control Science and Engineering, Harbin Institute of Technology, Harbin 150001, China 2 Department of Mechanical and Industrial Engineering, Faculty of Engineering, Norwegian University of Science and Technology, Trondheim 7034, Norway * Correspondence: hao.luo@hit.edu.cn Received: 16 October 2022 Accepted: 25 November 2022 Published: Abstract: With the improvement of hardware computing power and the development of deep learning algorithms, a revolution of "artificial intelligence (AI) + medical image" is taking place. Benefiting from diversified modern medical measurement equipment, a large number of medical images will be produced in the clinical process. These images improve the diagnostic accuracy of doctors, but also increase the labor burden of doctors. Deep learning technology is expected to realize an auxiliary diagnosis and improve diagnostic efficiency. At present, the method of deep learning technology combined with attention mechanism is a research hotspot and has achieved state-of-the-art results in many medical image tasks. This paper reviews the deep learning attention methods in medical image analysis. A comprehensive literature survey is first conducted to analyze the keywords and literature. Then, we introduce the development and technical characteristics of the attention mechanism. For its application in medical image analysis, we summarize the related methods in medical image classification, segmentation, detection, and enhancement. The remaining challenges, potential solutions, and future research directions are also discussed.

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LAEDNet: A Lightweight Attention Encoder–Decoder Network for ultrasound medical image segmentation

Cited by 41 publications

References 22 publications

Ultrasonic tomography imaging enhancement approach based on deep convolutional neural networks

Ultrasonic tomography imaging enhancement approach based on deep convolutional neural networks

Ultrasound Image Analysis with Vision Transformers—Review

Deep Learning Attention Mechanism in Medical Image Analysis: Basics and Beyonds

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