MLAU-Net: Deep supervised attention and hybrid loss strategies for enhanced segmentation of low-resolution kidney ultrasound Article Swipe
Rashid Khan
,
Asim Zaman
,
Chao Chen
,
Chuda Xiao
,
Wen Zhong
,
Yang Liu
,
Haseeb Hassan
,
Liyilei Su
,
Weiguo Xie
,
Yan Kang
,
Bingding Huang
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1177/20552076241291306
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1177/20552076241291306
Objective The precise segmentation of kidneys from a 2D ultrasound (US) image is crucial for diagnosing and monitoring kidney diseases. However, achieving detailed segmentation is difficult due to US images’ low signal-to-noise ratio and low-contrast object boundaries. Methods This paper presents an approach called deep supervised attention with multi-loss functions (MLAU-Net) for US segmentation. The MLAU-Net model combines the benefits of attention mechanisms and deep supervision to improve segmentation accuracy. The attention mechanism allows the model to selectively focus on relevant regions of the kidney and ignore irrelevant background information, while the deep supervision captures the high-dimensional structure of the kidney in US images. Results We conducted experiments on two datasets to evaluate the MLAU-Net model's performance. The Wuerzburg Dynamic Kidney Ultrasound (WD-KUS) dataset with annotation contained kidney US images from 176 patients split into training and testing sets totaling 44,880. The Open Kidney Dataset’s second dataset has over 500 B-mode abdominal US images. The proposed approach achieved the highest dice, accuracy, specificity, Hausdorff distance (HD95), recall, and Average Symmetric Surface Distance (ASSD) scores of 90.2%, 98.26%, 98.93%, 8.90 mm, 91.78%, and 2.87 mm, respectively, upon testing and comparison with state-of-the-art U-Net series segmentation frameworks, which demonstrates the potential clinical value of our work. Conclusion The proposed MLAU-Net model has the potential to be applied to other medical image segmentation tasks that face similar challenges of low signal-to-noise ratios and low-contrast object boundaries.
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- Language
- en
- Landing Page
- https://doi.org/10.1177/20552076241291306
- OA Status
- gold
- Cited By
- 4
- References
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- Related Works
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- OpenAlex ID
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https://openalex.org/W4404487053Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1177/20552076241291306Digital Object Identifier
- Title
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MLAU-Net: Deep supervised attention and hybrid loss strategies for enhanced segmentation of low-resolution kidney ultrasoundWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-01-01Full publication date if available
- Authors
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Rashid Khan, Asim Zaman, Chao Chen, Chuda Xiao, Wen Zhong, Yang Liu, Haseeb Hassan, Liyilei Su, Weiguo Xie, Yan Kang, Bingding HuangList of authors in order
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https://doi.org/10.1177/20552076241291306Publisher landing page
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
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https://doi.org/10.1177/20552076241291306Direct OA link when available
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Segmentation, Artificial intelligence, Computer science, Pattern recognition (psychology), Hausdorff distance, Noise (video), Deep learning, Computer vision, Image (mathematics)Top concepts (fields/topics) attached by OpenAlex
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4Total citation count in OpenAlex
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2025: 4Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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