Abstract
Due to diversity among tumor lesions and less difference between surroundings, to extract the discriminative features of a medical image is still a challenging job. In order to improve the ability in the representation of these complex objects, the type of approach has been proposed with the encoder-decoder architecture models for biomedical segmentation. However, most of them fuse coarse-grained and fine-grained features directly which will cause a semantic gap. In order to bridge the semantic gap and fuse features better, we propose a style consistency loss to constrain semantic similarity when combing the encoder and decoder features. The comparison experiments are done between our proposed U-Net with style consistency loss constraint in with the state-of-art segmentation deep networks including FCN, original U-Net and U-Net with residual block. Experimental results on LiTS-2017 show that our method achieves a liver dice gain of 1.7% and a tumor dice gain of 3.11% points over U-Net.
The first student Yunfeng Liu is Master Degree Candidate.
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Acknowledgement
This work is supported by National Natural Science Foundation of China (No. 61871276), Beijing Natural Science Foundation (No. 7184199), Capital’s Funds for Health Improvement and Research (No. 2018-2-2023), Research Foundation of Beijing Friendship Hospital, Capital Medical University (No. yyqdkt2017-25) and WBE Liver Fibrosis Foundation (No. CFHPC2019006).
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Liu, Y., Jia, X., Yang, Z., Yang, D. (2019). Style Consistency Constrained Fusion Feature Learning for Liver Tumor Segmentation. In: Lin, Z., et al. Pattern Recognition and Computer Vision. PRCV 2019. Lecture Notes in Computer Science(), vol 11859. Springer, Cham. https://doi.org/10.1007/978-3-030-31726-3_33
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DOI: https://doi.org/10.1007/978-3-030-31726-3_33
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