Abstract
This paper presents a novel framework devoted to the detection of HCC (Hepato-Cellular Carcinoma) within hepatic DCE-MRI (Dynamic Contrast-Enhanced MRI) sequences, by a deep learning approach. In clinical routine, radiologists usually consider different phases during contrast injection (before injection; arterial phase; portal phase for instance) for HCC diagnosis. By employing a U-Net architecture, we are able to identify such tumors with a very high accuracy (98.5% of classification rate at best) for a small cohort of patients, which should be confirmed in future works by considering larger groups. We also show in this paper the influence of patch size for this machine learning process, and the positive impact of employing all phases available in DCE-MRI sequences, compared to use only one.
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Acknowledgement
This work was co-financed by the Lodz University of Technology, Faculty of Electrical, Electronic, Computer and Control Engineering as a statutory activity (Project no. 501/12-24-1-5428).
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Fabijańska, A. et al. (2018). U-CatcHCC: An Accurate HCC Detector in Hepatic DCE-MRI Sequences Based on an U-Net Framework. In: Chmielewski, L., Kozera, R., Orłowski, A., Wojciechowski, K., Bruckstein, A., Petkov, N. (eds) Computer Vision and Graphics. ICCVG 2018. Lecture Notes in Computer Science(), vol 11114. Springer, Cham. https://doi.org/10.1007/978-3-030-00692-1_28
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