Abstract
Dynamic contrast-enhanced magnetic resonance imaging provide not only the information on the morphological features of the lesions, but also the changes of the lesion’s blood perfusion. In this paper, we propose a tensor-based temporal data representation (TTD) model and a multi-channel fusion 3D convolutional neural network (MCF-3D CNN) to extract the temporal and spatial features of dynamic contrast enhanced-MR images (DCE-MR images). To evaluate the performance of the proposed methods, we established a DCE-MR image dataset for non-invasively assessing the differentiation of Hepatocellular carcinoma (HCC). The TTD model achieves the accuracy of 73.96% for non-invasive assessment of HCC differentiation via MCF-3D CNN. Meanwhile, the 3D CNN with TTD achieves accuracy, sensitivity and specificity of 95.17%, 96.33%, and 94.00%, respectively, in discriminating the HCC and cirrhosis. Compared with the normal data representation method, the proposed TTD method is more conducive for 3D CNN to extract temporal-spatial features of DCE-MR images.
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Acknowledgments
This work is supported in part by the grants from 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).
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Jia, X., Xiao, Y., Yang, D., Yang, Z., Wang, X., Liu, Y. (2018). Temporal-Spatial Feature Learning of Dynamic Contrast Enhanced-MR Images via 3D Convolutional Neural Networks. In: Wang, Y., Jiang, Z., Peng, Y. (eds) Image and Graphics Technologies and Applications. IGTA 2018. Communications in Computer and Information Science, vol 875. Springer, Singapore. https://doi.org/10.1007/978-981-13-1702-6_38
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DOI: https://doi.org/10.1007/978-981-13-1702-6_38
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