research-article

Ultrasound Image Segmentation of Breast Tumors Based on Swin-transformerv2

Authors:

Feng Yue Baoping,

Yao Rui HanAuthors Info & Claims

ICIT '22: Proceedings of the 2022 10th International Conference on Information Technology: IoT and Smart City

Pages 106 - 111

https://doi.org/10.1145/3582197.3582214

Published: 30 March 2023 Publication History

Abstract

Breast cancer is one of the most common cancers with a high mortality rate. Early detection is essential to reduce the risk of mortality and morbidity for breast cancer. The diagnosis of breast cancer relies heavily on the segmentation accuracy of the breast tumor region. Convolutional neural networks, a form of deep learning, have now been applied extensively to breast tumor segmentation, but they still cannot focus on global information and accurately locate features. In this paper, the swin-transformerv2-UNet(S2UNet) method for segmenting breast ultrasound images based on swin-transformerv2 is proposed. S2UNet adopts the structure of upsampling and downsampling as a whole. First, the breast ultrasound image is divided into patches, and these patches are input into the downsampling structure for feature extraction. Then, the skip connection structure is introduced to fuse the features extracted from swin-transfermerv2 in the downsampling structure with the features of the corresponding modules in the upsampling structure. Next, these features are labeled and spliced through the upsampling structure to obtain the final breast tumor image. The S2UNet method was evaluated on the breast ultrasound images dataset (BUSI) with five-fold cross-validation. The results demonstrate that compared to UNet and other advanced segmentation models (Attention-UNet, TransUNet, Swin-UNet), the S2UNet with fewer parameters shows better segmentation performance with dice similarity coefficient (DSC) of 73.26% and intersection over union (IoU) of 58.30%.

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https://doi.org/10.1049/ipr2.13189
Lee JPark JKim YKim K(2024)Deep-learning-based pelvic automatic segmentation in pelvic fracturesScientific Reports10.1038/s41598-024-63093-w14:1Online publication date: 28-May-2024
https://doi.org/10.1038/s41598-024-63093-w
Zhang WWu HQin J(2024)Domesticating SAM for Breast Ultrasound Image Segmentation via Spatial-Frequency Fusion and Uncertainty CorrectionComputer Vision – ECCV 202410.1007/978-3-031-73337-6_2(20-37)Online publication date: 31-Oct-2024
https://doi.org/10.1007/978-3-031-73337-6_2
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Index Terms

Ultrasound Image Segmentation of Breast Tumors Based on Swin-transformerv2
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Image segmentation

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ICIT '22: Proceedings of the 2022 10th International Conference on Information Technology: IoT and Smart City

December 2022

385 pages

ISBN:9781450397438

DOI:10.1145/3582197

Copyright © 2022 ACM.

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Publication History

Published: 30 March 2023

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ICIT 2022

ICIT 2022: IoT and Smart City

December 23 - 25, 2022

Shanghai, China

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Cited By

Xie XTang X(2024)A novel density‐based representation for point cloud and its ability to facilitate classificationIET Image Processing10.1049/ipr2.1318918:12(3496-3506)Online publication date: 12-Aug-2024
https://doi.org/10.1049/ipr2.13189
Lee JPark JKim YKim K(2024)Deep-learning-based pelvic automatic segmentation in pelvic fracturesScientific Reports10.1038/s41598-024-63093-w14:1Online publication date: 28-May-2024
https://doi.org/10.1038/s41598-024-63093-w
Zhang WWu HQin J(2024)Domesticating SAM for Breast Ultrasound Image Segmentation via Spatial-Frequency Fusion and Uncertainty CorrectionComputer Vision – ECCV 202410.1007/978-3-031-73337-6_2(20-37)Online publication date: 31-Oct-2024
https://doi.org/10.1007/978-3-031-73337-6_2
KATAR OYILDIRIM Ö(2023)Breast Cancer Segmentation from Ultrasound Images Using ResNext-based U-Net ModelBitlis Eren Üniversitesi Fen Bilimleri Dergisi10.17798/bitlisfen.133131012:3(871-886)Online publication date: 28-Sep-2023
https://doi.org/10.17798/bitlisfen.1331310
Chen ZHe BLiang ZTang HFu YZhang Y(2023)Multimodal U-Net Breast Cancer Tumor Algorithm Based on Radio Frequency (RF) Data and Ultrasound Images2023 8th International Conference on Image, Vision and Computing (ICIVC)10.1109/ICIVC58118.2023.10269830(455-459)Online publication date: 27-Jul-2023
https://doi.org/10.1109/ICIVC58118.2023.10269830

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