Abstract
Dynamic contrast enhanced MRI images play a crucial role in liver tumor characterization during daily clinical practice. However, this task can be very time-consuming due to very different and various tumor types. In this paper we present an automatic liver tumor characterization method which consists of two main parts: registration of the MRI images and a supervised learning-based classification using the Random Forest method. Our dataset contained 10 benign and 30 malignant liver tumor cases. Manual tumor contours were determined by a well-trained physician. Although we used a relatively small train and test set, presented results can be considered promising. Our preliminary results showed that colorectal carcinoma metastasis (CRC) can be separated from other tumor types with an average accuracy of 96% (±8%). Furthermore, other mixed tumor types were successfully classified as non-CRC cases with high accuracy.
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Acknowledgements
We would like to thank to Department of Radiology, University of Szeged providing the input images and for creating the reference tumor contours.
This work was supported by Analitic Healthcare Quality User Information Program of the National Research, Development and Innovation Fund, Hungarian Government, Grant VKSZ_12-1-2013-0012.
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Urbán, S., Tanács, A. (2018). Automatic Liver Tumor Characterization Using LAVA DCE-MRI Images. In: Tavares, J., Natal Jorge, R. (eds) VipIMAGE 2017. ECCOMAS 2017. Lecture Notes in Computational Vision and Biomechanics, vol 27. Springer, Cham. https://doi.org/10.1007/978-3-319-68195-5_43
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DOI: https://doi.org/10.1007/978-3-319-68195-5_43
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