Abstract
Segmentation of the liver tumor is critical for preoperative planning, surgical protocol guidance, and post-operative treatment. Because of using contrast agents (CA), current liver tumor imaging still suffers from high-risk, time-consumption and expensive issues. In this study, a new Radiomics-guided generative adversarial network (Radiomics-guided GAN) is proposed as a safe, short time-consumption and inexpensive clinical tool to segment liver tumor without CA. The innovative Radiomics-guided adversarial mechanism learns the mapping relationship between the contrast images and the non-contrast images, which leads to completing the segmentation. Radiomics-guided GAN contains a segmentor and a discriminator module: the discriminator innovatively uses the Radiomics-feature from the contrast images as prior knowledge to guide the segmentor’s adversarial learning; the segmentor innovatively uses dense connection and skip connection to receive and share the guidance information, extracting the representing feature – Implicit Contract Radiomics (ICR) feature – in the non-contrast images. Our method yielded a pixel segmentation accuracy of 95.85%, and a Dice coefficient of 92.17 ± 0.79%, from 200 clinical subjects. The results illustrate that our method achieves the segmentation of liver tumor without CA and become the most potential useful tool for clinicians.
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Acknowledgements
This work is partly supported by National Natural Science Foundation of China (Grant number 61872261), the open funding project of State Key Laboratory of Virtual Reality Technology and Systems, Beihang University (Grant No. 2018-VRLAB2018B07), Research Project Supported by Shanxi Scholarship Council of China (201801D121139).
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Xiao, X. et al. (2019). Radiomics-guided GAN for Segmentation of Liver Tumor Without Contrast Agents. In: Shen, D., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science(), vol 11765. Springer, Cham. https://doi.org/10.1007/978-3-030-32245-8_27
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DOI: https://doi.org/10.1007/978-3-030-32245-8_27
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