Abstract
Non-contrast CT is often preferred in clinical screening while segmentation of such CT data is more challenging due to the low contrast in tissue boundaries and scarce supervised training data than contrast-enhanced CT (CTce) segmentation. To alleviate manual labelling work of radiologists, we generate training samples for 3D U-Net segmentation network by transforming the existing CTce liver segmentation dataset to the non-contrast CT styled volumes with CycleGAN. We validated the performance of CycleGAN in both unsupervised and hybrid supervised training strategy. The results show that using CycleGAN in unsupervised segmentation can achieve higher mean Dice coefficients than fully supervised manner in liver segmentation. The hybrid training of generated samples and the target task samples can improve the generalization ability of segmentation.
C. Song and B. He—Contributed equally to this work.
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References
Huang, C., et al.: Fully automatic liver segmentation using a probability atlas registration. In: O’Conner, L. (ed.) Proceedings of ICECC, pp. 126–129. IEEE Computer Society Press (2012)
Xu, L.: H-DenseUNet: hybrid densely connected UNet for liver and tumor segmentation from CT volumes. IEEE Trans. Med. Imaging 37(12), 2663–2674 (2018)
Seo, H., Huang, C., Bassenne, M., Xiao, R., Xing, L.: Modified U-Net (mU-Net) with incorporation of object-dependent high level features for improved liver and liver-tumor segmentation in CT images. IEEE Trans. Med. Imaging 39(5), 1316–1325 (2020)
Heimman, T., van Ginneken, B., Styner, M.A., et al.: Comparison and evaluation of methods for liver segmentation from CT datasets. IEEE Trans. Med. Imaging 28(8), 1251–1265 (2009)
Bilic, P., Christ, P.R., Vorontsov, E., et al.: The liver tumor segmentation benchmark (LiTS). arXiv reprint. arXiv: 1901.040506 (2019)
Insideradiology Homepage. https://www.insideradiology.com.au/iodine-containing-contrast-medium-hp/
Tomoshige, S., Oost, E., Shimizu, A., et al.: A conditional statistical shape model with integrated error estimation of the conditions; application to liver segmentation in non-contrast CT images. Med. Image Anal. 18(1), 130–143 (2014)
Goksel, O., Foncubierta-Rodríguez, A., Jimenez-del-Toro, O., et al.: Overview of the VISCERAL challenge at ISBI 2015. In: Goksel, O. (ed.) Proceedings of the VISCERAL Anatomy Grand Challenge at the 2015 IEEE International Symposium on Biomedical Imaging (ISBI), New York, NY, 16 April 2015
Zhu, J., Park. T., Isola, P., Efros, A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of the IEEE International Conference on Computer Vision 2017, Venice, pp. 2242–2251 (2017)
Jiang, J., et al.: Tumor-aware, adversarial domain adaptation from CT to MRI for lung cancer segmentation. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11071, pp. 777–785. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00934-2_86
Liu, L., Muelly, M., Deng, J., Pfister, T., Li, L.: Generative modeling for small-data object detection. In: Proceedings of the IEEE International Conference on Computer Vision, 2019, Seoul, Korea (South), pp. 6072–6080 (2019)
Chen, C., Dou, Q., Chen, H., et al.: Unsupervised bidirectional cross-modality adaptation via deeply synergistic image and feature alignment for medical image segmentation. IEEE Trans. Med. Imaging 39(7), 2494–2505 (2020)
Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Isensee, F., Petersen, J., Klein, A., et al.: nnU-Net: self-adapting framework for u-net-based medical image segmentation. arXiv preprint arXiv:1809.10486 (2018)
Acknowledgments
This work was partially supported by the National Key Research and Development Program (2019YFC0118100 and 2017YFC0110903), the Guangdong Key Area Research and Development Program (2020B010165004), the Shenzhen Key Basic Science Program (JCYJ20170413162213765 and JCYJ20180507182437217), the Shenzhen Key Laboratory Program (ZDSYS201707271637577), the NSFC-Shenzhen Union Program (U1613221).
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Song, C., He, B., Chen, H., Jia, S., Chen, X., Jia, F. (2020). Non-contrast CT Liver Segmentation Using CycleGAN Data Augmentation from Contrast Enhanced CT. In: Cardoso, J., et al. Interpretable and Annotation-Efficient Learning for Medical Image Computing. IMIMIC MIL3ID LABELS 2020 2020 2020. Lecture Notes in Computer Science(), vol 12446. Springer, Cham. https://doi.org/10.1007/978-3-030-61166-8_13
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DOI: https://doi.org/10.1007/978-3-030-61166-8_13
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