Abstract
Tumor segmentation is a challenging, but substantial task in diagnosis, treatment planning and monitoring. This paper presents a self-learning technique to segment lesions on clinical 3D MRI images. The method is self-learning and iterative: instead of creating a model from manually segmented tumors it learns a given individual tumor in an iterative way without user interaction in the learning cycles. Based on a manually defined region of interest the presented iterative approach first learns the tumor features from the initial region using Random Forest classifier, then in each subsequent cycle it updates the previously learned model automatically. The method was evaluated on liver DCE-MRI images using manually defined tumor segmentation as reference. The algorithm was tested for various types of liver tumors. The presented results showed good correlation with the reference using absolute volume difference and DICE similarity measurements which gave 7.8 % and 88 % average results respectively.
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Acknowledgements
We would like to thank to Department of Radiology, University of Szeged providing the input images, especially for Dr. O. Urbán 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., Ruskó, L., Nagy, A. (2016). A Self-learning Tumor Segmentation Method on DCE-MRI Images. In: Campilho, A., Karray, F. (eds) Image Analysis and Recognition. ICIAR 2016. Lecture Notes in Computer Science(), vol 9730. Springer, Cham. https://doi.org/10.1007/978-3-319-41501-7_66
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DOI: https://doi.org/10.1007/978-3-319-41501-7_66
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