Bibliographic Details
| Title: |
MLAU-Net: Deep supervised attention and hybrid loss strategies for enhanced segmentation of low-resolution kidney ultrasound |
| Authors: |
Rashid Khan, Asim Zaman, Chao Chen, Chuda Xiao, Wen Zhong, Yang Liu, Haseeb Hassan, Liyilei Su, Weiguo Xie, Yan Kang, Bingding Huang |
| Source: |
Digital Health, Vol 10 (2024) |
| Publisher Information: |
SAGE Publishing, 2024. |
| Publication Year: |
2024 |
| Collection: |
LCC:Computer applications to medicine. Medical informatics |
| Subject Terms: |
Computer applications to medicine. Medical informatics, R858-859.7 |
| More Details: |
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. |
| Document Type: |
article |
| File Description: |
electronic resource |
| Language: |
English |
| ISSN: |
2055-2076
20552076 |
| Relation: |
https://doaj.org/toc/2055-2076 |
| DOI: |
10.1177/20552076241291306 |
| Access URL: |
https://doaj.org/article/d1d7c2034a114ad3b3c7b09c06796bfe |
| Accession Number: |
edsdoj.1d7c2034a114ad3b3c7b09c06796bfe |
| Database: |
Directory of Open Access Journals |
