Abstract
Purpose
In recent years, breast cancer has become the greatest threat to women. There are many studies dedicated to the precise segmentation of breast tumors, which is indispensable in computer-aided diagnosis. Deep neural networks have achieved accurate segmentation of images. However, convolutional layers are biased to extract local features and tend to lose global and location information as the network deepens, which leads to a decrease in breast tumors segmentation accuracy. For this reason, we propose a hybrid attention-guided network (HAG-Net). We believe that this method will improve the detection rate and segmentation of tumors in breast ultrasound images.
Methods
The method is equipped with multi-scale guidance block (MSG) for guiding the extraction of low-resolution location information. Short multi-head self-attention (S-MHSA) and convolutional block attention module are used to capture global features and long-range dependencies. Finally, the segmentation results are obtained by fusing multi-scale contextual information.
Results
We compare with 7 state-of-the-art methods on two publicly available datasets through five random fivefold cross-validations. The highest dice coefficient, Jaccard Index and detect rate (\(82.6 \pm 1.9\)%, \(74.2\pm 2.1\)%, \(92.1\pm 2.2\)% and \(77.8\pm 3.1\)%, \(66.8\pm 3.2\)%, \(91.9\pm 5.0\)%, separately) obtained on two publicly available datasets(BUSI and OASUBD), prove the superiority of our method.
Conclusion
HAG-Net can better utilize multi-resolution features to localize the breast tumors. Demonstrating excellent generalizability and applicability for breast tumors segmentation compare to other state-of-the-art methods.
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Funding
This research was funded by the Guangdong Basic and Applied Basic Research Foundation (2023A1515012833), the Science and Technology Program of Guangzhou (202201010544), Guangdong Provincial Key Laboratory of Traditional Chinese Medicine Informatization (2021B1212040007) and National Key Research and Development Project (2019YFC0120100, and 2019YFC0121907).
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YL and XJ contributed to the design of the entire work. MZ: Investigation, Conceptualization, Writing, Review & editing. DZ and RQ: Writing, Funding acquisition, Writing, Review & editing. ZO and JB: Writing, Review & editing, investigation.
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Lu, Y., Jiang, X., Zhou, M. et al. A hybrid attentional guidance network for tumors segmentation of breast ultrasound images. Int J CARS 18, 1489–1500 (2023). https://doi.org/10.1007/s11548-023-02849-7
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DOI: https://doi.org/10.1007/s11548-023-02849-7