Asymmetric U-shaped network with hybrid attention mechanism for kidney ultrasound images segmentation

Chen, Gongping; Zhao, Yu; Dai, Yu; Zhang, Jianxun; Yin, Xin; Cui, Liang; Jiang, Qian

doi:10.1016/j.eswa.2022.118847

Cited by 12 publications

(3 citation statements)

References 54 publications

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“…However, in many cases, the abovementioned methods struggle to handle cervical cell images with irregular shapes and sizes. (Chen et al 2023) The fundamental concept of active contour models allows the contour shape to deform based on the minimization of an energy function, gradually fitting the edge of the target object. The energy function is primarily determined by the internal energy and external energy, which describe contour features and image features, respectively.…”

Section: Segmentation Methodsmentioning

confidence: 99%

An interactive nuclei segmentation framework with Voronoi diagrams and weighted convex difference for cervical cancer pathology images

Yang,

Lei,

Huang

et al. 2024

Phys. Med. Biol.

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Objective.Nuclei segmentation is crucial for pathologists to accurately classify and grade cancer. However, this process faces significant challenges, such as the complex background structures in pathological images, the high-density distribution of nuclei, and cell adhesion. Approach.In this paper, we present an interactive nuclei segmentation framework that increases the precision of nuclei segmentation. Our framework incorporates expert monitoring to gather as much prior information as possible and accurately segment complex nucleus images through limited pathologist interaction, where only a small portion of the nucleus locations in each image are labeled. The initial contour is determined by the Voronoi diagram generated from the labeled points, which is then input into an optimized weighted convex difference model to regularize partition boundaries in an image. Specifically, we provide theoretical proof of the mathematical model, stating that the objective function monotonically decreases. Furthermore, we explore a postprocessing stage that incorporates histograms, which are simple and easy to handle and prevent arbitrariness and subjectivity in individual choices. Main results. To evaluate our approach, we conduct experiments on both a cervical cancer dataset and a nasopharyngeal cancer dataset. The experimental results demonstrate that our approach achieves competitive performance compared to other methods. Significance. The Voronoi diagram in the paper serves as prior information for the active contour, providing positional information for individual cells. Moreover, the active contour model achieves precise segmentation results while offering mathematical interpretability.

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Section: Segmentation Methodsmentioning

confidence: 99%

An interactive nuclei segmentation framework with Voronoi diagrams and weighted convex difference for cervical cancer pathology images

Yang,

Lei,

Huang

et al. 2024

Phys. Med. Biol.

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show abstract

“…The channel and spatial attention in the hybrid attention are fully utilized to improve the feature extraction capability of the network and also combined with the residual connectivity of the classification network to further improve the diagnostic accuracy of the model. The ISANET based on CNN and hybrid attention was proposed by Xu et al [67] for the classification of lung cancer. Pathological regions are attended to by models with embedded channels and spatial attention, resulting in the superior performance of the model in classifying lung cancer.…”

Section: Hybrid Attention In Medical Image Classification Taskmentioning

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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“…Over the years, researchers have developed several networks based on UNet architecture, each with unique enhancements and improvements. Some of these networks include Sharp UNet [ 41 ], Attention UNet [ 42 ], UNet++ [ 43 ], UNet3+ [ 44 ], CSM-Net [ 45 ], Asymmetric UNet [ 46 ], Kernel UNet [ 47 ], and Swin_UNet [ 26 ]. The CSM-Net, Kernel UNet, and Asymmetric UNet are models designed for ultrasound segmentation, with their architecture specifically emphasizing the integration of attention mechanisms.…”

Section: Introductionmentioning

confidence: 99%

Enhancing breast ultrasound segmentation through fine-tuning and optimization techniques: Sharp attention UNet

Khaledyan,

Marini,

M. Baran

et al. 2023

PLoS ONE

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Segmentation of breast ultrasound images is a crucial and challenging task in computer-aided diagnosis systems. Accurately segmenting masses in benign and malignant cases and identifying regions with no mass is a primary objective in breast ultrasound image segmentation. Deep learning (DL) has emerged as a powerful tool in medical image segmentation, revolutionizing how medical professionals analyze and interpret complex imaging data. The UNet architecture is a highly regarded and widely used DL model in medical image segmentation. Its distinctive architectural design and exceptional performance have made it popular among researchers. With the increase in data and model complexity, optimization and fine-tuning models play a vital and more challenging role than before. This paper presents a comparative study evaluating the effect of image preprocessing and different optimization techniques and the importance of fine-tuning different UNet segmentation models for breast ultrasound images. Optimization and fine-tuning techniques have been applied to enhance the performance of UNet, Sharp UNet, and Attention UNet. Building upon this progress, we designed a novel approach by combining Sharp UNet and Attention UNet, known as Sharp Attention UNet. Our analysis yielded the following quantitative evaluation metrics for the Sharp Attention UNet: the Dice coefficient, specificity, sensitivity, and F1 score values obtained were 0.93, 0.99, 0.94, and 0.94, respectively. In addition, McNemar’s statistical test was applied to assess significant differences between the approaches. Across a number of measures, our proposed model outperformed all other models, resulting in improved breast lesion segmentation.

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Asymmetric U-shaped network with hybrid attention mechanism for kidney ultrasound images segmentation

Cited by 12 publications

References 54 publications

An interactive nuclei segmentation framework with Voronoi diagrams and weighted convex difference for cervical cancer pathology images

An interactive nuclei segmentation framework with Voronoi diagrams and weighted convex difference for cervical cancer pathology images

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

Enhancing breast ultrasound segmentation through fine-tuning and optimization techniques: Sharp attention UNet

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