지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2023.9
- 수록면
- 282 - 293 (12page)
- DOI
- 10.7315/CDE.2023.282
이용수
초록· 키워드
오류제보하기
Volumetric medical image segmentation is critical in diagnosing diseases and planning subsequent treatment. The convolutional neural network (CNN)-based U-Net was proposed for conducting accurate and robust medical image segmentation since the skip connection of U-Net and deep feature representation significantly improved its performance. However, since CNN-based models mainly focus on local and low-level features, they cannot extract global and high-level features effectively. Meanwhile, the Vision Transformer developed in natural language processing is proposed to improve image classification performance by splitting an input image into patches and conducting linear embeddings of the patches, which can extract global features. However, the Vision Transformer has difficulty in handling detailed and low-level features. This study proposes SwinResNet which can effectively conduct volumetric medical image segmentation by fusing the Swin Transformer and CNN models. The combination can take advantage of both models and complement each other. Swin Transformer and ResNet are used as encoders, and the receptive field blocks and aggregation modules are applied to the multi-level features extracted from both encoders. Comprehensive evaluation shows that the proposed approach outperforms well-known previous studies.
목차
ABSTRACT
1. 서론
2. 관련연구
3. 제안하는 방법
4. 평가
5. 결론
References
참고문헌 (37)
참고문헌 신청
Yang, S.-W., Choi, Y. and Koo, S., 2011, CUDAbased Fast DRR Generation for Analysis of Medical Images, Transactions of the Society of CAD/CAM Engineers, 16(4), pp.285-291.
Xun, J., Zhou, M., Wu, Z. and Ali, S., 2015, Robust Surface Segmentation and Edge Feature Lines Extraction from Fractured Fragments of Relics, Journal of Computational Design and Engineering, 2(2), pp.79-87, doi: 10.1016/j.jcde.2014.12.002
Min, S.K. and Park, S.C., 2016, Registration of Dental Range Images from a Intraoral Scanner, Korean Journal of Computational Design and Engineering, 21(3), pp.296-305, doi: 10.7315/cde.2016.296
Ronneberger, O., Fischer, P. and Brox, T., 2015, U-Net: Convolutional Networks for Biomedical Image Segmentation, Proceedings of International Conference on Medical Image Computing and Computer-Assisted Intervention, pp.234-241, doi: 10.1007/978-3-319-245
Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q. and Wang, M., 2021, Swin-UNet: UNet-like Pure Transformer for Medical Image Segmentation, arXiv preprint arXiv:2105.05537, doi: 10.1007/978-3-031-25066-8_9
Xun, J., Zhou, M., Wu, Z. and Ali, S., 2015, Robust Surface Segmentation and Edge Feature Lines Extraction from Fractured Fragments of Relics, Journal of Computational Design and Engineering, 2(2), pp.79-87, doi: 10.1016/j.jcde.2014.12.002
Min, S.K. and Park, S.C., 2016, Registration of Dental Range Images from a Intraoral Scanner, Korean Journal of Computational Design and Engineering, 21(3), pp.296-305, doi: 10.7315/cde.2016.296
Ronneberger, O., Fischer, P. and Brox, T., 2015, U-Net: Convolutional Networks for Biomedical Image Segmentation, Proceedings of International Conference on Medical Image Computing and Computer-Assisted Intervention, pp.234-241, doi: 10.1007/978-3-319-245
Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q. and Wang, M., 2021, Swin-UNet: UNet-like Pure Transformer for Medical Image Segmentation, arXiv preprint arXiv:2105.05537, doi: 10.1007/978-3-031-25066-8_9
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