The segmentation of unlabeled medical images is troublesome due to the high cost of annotation, and unsupervised domain adaptation is one solution to this. In this paper, an improved unsupervised domain adaptation method was proposed. The proposed method considered both global alignment and category-wise alignment. First, we aligned the appearance of two domains by image transformation. Second, we aligned the output maps of two domains in a global way. Then, we decomposed the semantic prediction map by category, aligning the prediction maps in a category-wise manner. Finally, we evaluated the proposed method on the 2017 Multi-Modality Whole Heart Segmentation Challenge dataset, and obtained 82.1 on the dice similarity coefficient and 4.6 on the average symmetric surface distance, demonstrating the effectiveness of the combination of global alignment and category-wise alignment.
Ronneberger, O., Fischer, P. and Brox, T. (2015) U-net: Convolutional Networks for Biomedical Image Segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, Cham, 234-241. https://doi.org/10.1007/978-3-319-24574-4_28
Isensee, F., Jäger, P.F., Kohl, S.A., Petersen, J. and Maier-Hein, K.H. (2019) Automated Design of Deep Learning Methods for Biomedical Image Segmentation.
Milletari, F., Navab, N. and Ahmadi, S.A. (2016) V-net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation. 2016 Fourth International Conference on 3D Vision IEEE, Stanford, 25-28 October 2016, 565-571. https://doi.org/10.1109/3DV.2016.79
Pan, S.J. and Yang, Q. (2009) A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22, 1345-1359. https://doi.org/10.1109/TKDE.2009.191
Zhang, Y., Miao, S., Mansi, T. and Liao, R. (2018) Task Driven Generative Modeling for Unsupervised Domain Adaptation: Application to X-Ray Image Segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, Cham, 599-607. https://doi.org/10.1007/978-3-030-00934-2_67
Zhao, S., Li, B., Yue, X., Gu, Y., Xu, P., Hu, R., et al. (2019) Multi-Source Domain Adaptation for Semantic Segmentation. 33rd Conference on Neural Information Processing Systems, Vancouver, 8-14 December 2019, 7287-7300.
Yang, Y. and Soatto, S. (2020) FDA: Fourier Domain Adaptation for Semantic Segmentation. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, 13-19 June 2020, 4085-4095. https://doi.org/10.1109/CVPR42600.2020.00414
Russo, P., Carlucci, F.M., Tommasi, T. and Caputo, B. (2018) From Source to Target and Back: Symmetric Bi-Directional Adaptive GAN. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, 18-23 June 2018, 8099-8108. https://doi.org/10.1109/CVPR.2018.00845
Chen, C., Dou, Q., Chen, H. and Heng, P.A. (2018) Semantic-Aware Generative Adversarial Nets for Unsupervised Domain Adaptation in Chest X-Ray Segmentation. In: International Workshop on Machine Learning in Medical Imaging, Springer, Cham, 143-151. https://doi.org/10.1007/978-3-030-00919-9_17
Ganin, Y., Ustinova, E., Ajakan, H., Germain, P., Larochelle, H., Laviolette, F., et al. (2016) Domain-Adversarial Training of Neural Networks. The Journal of Machine Learning Research, 17, 2096-2030.
Kamnitsas, K., Baumgartner, C., Ledig, C., Newcombe, V., Simpson, J., Kane, A., et al. (2017) Unsupervised Domain Adaptation in Brain Lesion Segmentation with Adversarial Networks. In: International Conference on Information Processing in Medical Imaging, Springer, Cham, 597-609. https://doi.org/10.1007/978-3-319-59050-9_47
Dou, Q., Ouyang, C., Chen, C., Chen, H. and Heng, P.A. (2018) Unsupervised Cross-Modality Domain Adaptation of Convents for Biomedical Image Segmentations with Adversarial Loss. Proceedings of the 27th International Joint Conference on Artificial Intelligence, Stockholm, 13-19 July 2018, 691-697. https://doi.org/10.24963/ijcai.2018/96
Tzeng, E., Hoffman, J., Saenko, K. and Darrell, T. (2017) Adversarial Discriminative Domain Adaptation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, 21-26 July 2017, 7167-7176. https://doi.org/10.1109/CVPR.2017.316
Tsai, Y.H., Hung, W.C., Schulter, S., Sohn, K., Yang, M.H. and Chandraker, M. (2018) Learning to Adapt Structured Output Space for Semantic Segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, 18-23 June 2018, 7472-7481. https://doi.org/10.1109/CVPR.2018.00780
Wang, S., Yu, L., Yang, X., Fu, C.W. and Heng, P.A. (2019) Patch-Based Output Space Adversarial Learning for Joint Optic Disc and Cup Segmentation. IEEE Transactions on Medical Imaging, 38, 2485-2495. https://doi.org/10.1109/TMI.2019.2899910
Wang, S., Yu, L., Li, K., Yang, X., Fu, C.W. and Heng, P.A. (2019) Boundary and Entropy-Driven Adversarial Learning for Fundus Image Segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, Cham, 102-110. https://doi.org/10.1007/978-3-030-32239-7_12
Hoffman, J., Tzeng, E., Park, T., Zhu, J.Y., Isola, P., Saenko, K., et al. (2018) Cycada: Cycle-Consistent Adversarial Domain Adaptation. International Conference on Machine Learning, Stockholm, 10-15 July 2018, 1989-1998.
Zhang, Y., Qiu, Z., Yao, T., Liu, D. and Mei, T. (2018) Fully Convolutional Adaptation Networks for Semantic Segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, 18-23 June 2018, 6810-6818. https://doi.org/10.1109/CVPR.2018.00712
Chen, C., Dou, Q., Chen, H., Qin, J. and Heng, P.A. (2020) Unsupervised Bidirectional Cross-Modality Adaptation via Deeply Synergistic Image and Feature Alignment for Medical Image Segmentation. IEEE Transactions on Medical Imaging, 39, 2494-2505. https://doi.org/10.1109/TMI.2020.2972701
Zhang, Y. and Wang, Z. (2020) Joint Adversarial Learning for Domain Adaptation in Semantic Segmentation. Proceedings of the AAAI Conference on Artificial Intelligence, 34, 6877-6884. https://doi.org/10.1609/aaai.v34i04.6169
Yang, J., Xu, R., Li, R., Qi, X., Shen, X., Li, G. and Lin, L. (2020) An Adversarial Perturbation-Oriented Domain Adaptation Approach for Semantic Segmentation. Proceedings of the AAAI Conference on Artificial Intelligence, 34, 12613-12620. https://doi.org/10.1609/aaai.v34i07.6952
Yang, X., Dou, H., Li, R., Wang, X., Bian, C., Li, S., et al. (2018) Generalizing Deep Models for Ultrasound Image Segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, Cham, 497-505. https://doi.org/10.1007/978-3-030-00937-3_57
Dong, J., Cong, Y., Sun, G., Zhong, B. and Xu, X. (2020) What Can Be Transferred: Unsupervised Domain Adaptation for Endoscopic Lesions Segmentation. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, 13-19 June 2020, 4023-4032. https://doi.org/10.1109/CVPR42600.2020.00408
Chen, C., Dou, Q., Chen, H., Qin, J. and Heng, P.A. (2019) Synergistic Image and Feature Adaptation: Towards Cross-Modality Domain Adaptation for Medical Image Segmentation. Proceedings of the AAAI Conference on Artificial Intelligence, 33, 865-872. https://doi.org/10.1609/aaai.v33i01.3301865
Chen, Y.H., Chen, W.Y., Chen, Y.T., Tsai, B.C., Frank Wang, Y.C. and Sun, M. (2017) No More Discrimination: Cross City Adaptation of Road Scene Segmenters. Proceedings of the IEEE International Conference on Computer Vision, Venice, 22-29 October 2017, 1992-2001. https://doi.org/10.1109/ICCV.2017.220
Menta, M., Romero, A. and van de Weijer, J. (2020) Learning to Adapt Class-Specific Features across Domains for Semantic Segmentation.
Zhang, Q., Zhang, J., Liu, W. and Tao, D. (2019) Category Anchor-Guided Unsupervised Domain Adaptation for Semantic Segmentation. 33rd Conference on Neural Information Processing Systems, Vancouver, 8-14 December 2019, 435-445.
Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., et al. (2014) Generative Adversarial Networks. Proceedings of the 27th International Conference on Neural Information Processing Systems, Volume 2, 2672-2680.
Zhu, J.Y., Park, T., Isola, P. and Efros, A.A. (2017) Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks. Proceedings of the IEEE International Conference on Computer Vision, Rio de Janeiro, 14-20 October 2007, 2223-2232. https://doi.org/10.1109/ICCV.2017.244
Zhuang, X. and Shen, J. (2016) Multi-Scale Patch and Multi-Modality Atlases for Whole Heart Segmentation of MRI. Medical Image Analysis, 31, 77-87. https://doi.org/10.1016/j.media.2016.02.006