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International Journal of Computer Assisted Radiology and Surgery

Published in:

28-02-2023 | Original Article

A hybrid attentional guidance network for tumors segmentation of breast ultrasound images

Authors: Yaosheng Lu, Xiaosong Jiang, Mengqiang Zhou, Dengjiang Zhi, Ruiyu Qiu, Zhanhong Ou, Jieyun Bai

Published in: International Journal of Computer Assisted Radiology and Surgery | Issue 8/2023

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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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Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F (2021) Global cancer statistics 2020: Globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 71(3):209–249. https://doi.org/10.3322/caac.21660CrossRefPubMed

Xian M, Zhang Y, Cheng HD (2015) Fully automatic segmentation of breast ultrasound images based on breast characteristics in space and frequency domains. Pattern Recogn 48(2):485–497. https://doi.org/10.1016/j.patcog.2014.07.026CrossRef

Moon WK, Lo C-M, Chen R-T, Shen Y-W, Chang JM, Huang C-S, Chen J-H, Hsu W-W, Chang R-F (2014) Tumor detection in automated breast ultrasound images using quantitative tissue clustering. Med Phys 41(4):042901. https://doi.org/10.1118/1.4869264CrossRefPubMed

Shareef B, Vakanski A, Xian M, Freer PE (2020) Estan: enhanced small tumor-aware network for breast ultrasound image segmentation. Healthcare. https://doi.org/10.48550/arXiv.2009.12894

Byra M, Jarosik P, Szubert A, Galperin M, Ojeda-Fournier H, Olson L, O’Boyle M, Comstock C, Andre M (2020) Breast mass segmentation in ultrasound with selective kernel u-net convolutional neural network. Biomed Signal Process Control 61:102027. https://doi.org/10.1016/j.bspc.2020.102027CrossRefPubMedPubMedCentral

Yang K, Suzuki A, Ye J, Nosato H, Izumori A, Sakanashi H (2022) Ctg-net: cross-task guided network for breast ultrasound diagnosis. PLoS ONE 17(8):1–25. https://doi.org/10.1371/journal.pone.0271106CrossRef

Podda AS, Balia R, Barra S, Carta S, Fenu G, Piano L (2022) Fully-automated deep learning pipeline for segmentation and classification of breast ultrasound images. J Comput Sci 63:101816. https://doi.org/10.1016/j.jocs.2022.101816CrossRef

Xue C, Zhu L, Fu H, Hu X, Li X, Zhang H, Heng P-A (2021) Global guidance network for breast lesion segmentation in ultrasound images. Med Image Anal 70:101989. https://doi.org/10.1016/j.media.2021.101989CrossRefPubMed

Yang K, Suzuki A, Ye J, Nosato H, Izumori A, Sakanashi H (2021) Tumor detection from breast ultrasound images using mammary gland attentive U-Net. In: International forum on medical imaging in Asia 2021, vol. 11792, p 1179202. https://doi.org/10.1117/12.2590073

10.

Zou H, Gong X, Luo J, Li T (2021) A robust breast ultrasound segmentation method under noisy annotations. Comput Methods Prog Biomed 209:106327. https://doi.org/10.1016/j.cmpb.2021.106327CrossRef

11.

Punn NS, Agarwal S (2022) Rca-iunet: a residual cross-spatial attention-guided inception u-net model for tumor segmentation in breast ultrasound imaging. Mach Vis Appl 33(2):1–10. https://doi.org/10.1007/s00138-022-01280-3CrossRef

12.

Tang P, Yang X, Nan Y, Xiang S, Liang Q (2021) Feature pyramid nonlocal network with transform modal ensemble learning for breast tumor segmentation in ultrasound images. IEEE Trans Ultrason Ferroelectr Freq Control 68(12):3549–3559. https://doi.org/10.1109/TUFFC.2021.3098308CrossRefPubMed

13.

Wu Y, Zhang R, Zhu L, Wang W, Wang S, Xie H, Cheng G, Wang FL, He X, Zhang H (2021) Bgm-net: Boundary-guided multiscale network for breast lesion segmentation in ultrasound. Front Mol Biosci. https://doi.org/10.3389/fmolb.2021.698334

14.

Ilesanmi AE, Chaumrattanakul U, Makhanov SS (2021) A method for segmentation of tumors in breast ultrasound images using the variant enhanced deep learning. Biocybern Biomed Eng 41(2):802–818. https://doi.org/10.1016/j.bbe.2021.05.007CrossRef

15.

Xu M, Huang K, Chen Q, Qi X (2021) Mssa-net: Multi-scale self-attention network for breast ultrasound image segmentation. In: 2021 IEEE 18th international symposium on biomedical imaging (ISBI), pp 827–831 . https://doi.org/10.1109/ISBI48211.2021.9433899

16.

Zhang G, Zhao K, Hong Y, Qiu X, Zhang K, Wei B (2021) Sha-mtl: soft and hard attention multi-task learning for automated breast cancer ultrasound image segmentation and classification. Int J Comput Assist Radiol Surg 16(10):1719–1725. https://doi.org/10.1007/s11548-021-02445-7CrossRefPubMed

17.

Al-Dhabyani W, Gomaa M, Khaled H, Fahmy A (2020) Dataset of breast ultrasound images. Data Brief 28:104863. https://doi.org/10.1016/j.dib.2019.104863CrossRefPubMed

18.

Piotrzkowska-Wróblewska H, Dobruch-Sobczak K, Byra M, Nowicki A (2017) Open access database of raw ultrasonic signals acquired from malignant and benign breast lesions. Med Phys 44(11):6105–6109. https://doi.org/10.1002/mp.12538CrossRefPubMed

19.

Zeiler MD, Fergus R (2014) Visualizing and understanding convolutional networks. In: Computer vision—-ECCV 2014, Cham, pp 818–833. https://doi.org/10.1007/978-3-319-10590-1_53

20.

Srinivas A, Lin T-Y, Parmar N, Shlens J, Abbeel P, Vaswani A (2021) Bottleneck transformers for visual recognition. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (CVPR), pp 16519–16529 . https://doi.org/10.48550/arXiv.2101.11605

21.

Woo S, Park J, Lee J-Y, Kweon IS (2018) Cbam: Convolutional block attention module. In: Proceedings of the European conference on computer vision (ECCV) . https://doi.org/10.48550/arXiv.1807.06521

22.

Lin T-Y, Dollar P, Girshick R, He K, Hariharan B, Belongie S (2017) Feature pyramid networks for object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR). https://doi.org/10.48550/arXiv.1612.03144

23.

Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: Medical image computing and computer-assisted intervention—MICCAI , pp 234–241. https://doi.org/10.1007/978-3-319-24574-4_28

24.

Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR) . https://doi.org/10.48550/arXiv.1605.06211

25.

Oktay O, Schlemper J, Folgoc LL, Lee MCH, Heinrich MP, Misawa K, Mori K, McDonagh SG, Hammerla NY, Kainz B, Glocker B, Rueckert D (2018) Attention u-net: learning where to look for the pancreas. CoRR arXiv:1804.03999

26.

Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, Liang J (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support, pp 3–11. https://doi.org/10.1007/978-3-030-00889-5_1

27.

Chen L-C, Zhu Y, Papandreou G, Schroff F, Adam H (2018) Encoder-decoder with atrous separable convolution for semantic image segmentation. In: Proceedings of the European conference on computer vision (ECCV) . https://doi.org/10.48550/arXiv.1802.02611

Title: A hybrid attentional guidance network for tumors segmentation of breast ultrasound images
Authors: Yaosheng Lu
Xiaosong Jiang
Mengqiang Zhou
Dengjiang Zhi
Ruiyu Qiu
Zhanhong Ou
Jieyun Bai
Publication date: 28-02-2023
Publisher: Springer International Publishing
Published in: International Journal of Computer Assisted Radiology and Surgery / Issue 8/2023
Print ISSN: 1861-6410
Electronic ISSN: 1861-6429
DOI: https://doi.org/10.1007/s11548-023-02849-7

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Abstract

Purpose

Methods

Results

Conclusion

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