Abstract
We present a new, fully automatic algorithm for liver tumors segmentation in follow-up CT studies. The inputs are a baseline CT scan and a delineation of the tumors in it and a follow-up scan; the outputs are the tumors delineations in the follow-up CT scan. The algorithm starts by defining a region of interest using a deformable registration of the baseline scan and tumors delineations to the follow-up CT scan and automatic liver segmentation. Then, it constructs a voxel classifier by training a Convolutional Neural Network (CNN). Finally, it segments the tumor in the follow-up study with the learned classifier. The main novelty of our method is the combination of follow-up based detection with CNN-based segmentation. Our experimental results on 67 tumors from 21 patients with ground-truth segmentations approved by a radiologist yield a success rate of 95.4 % and an average overlap error of 16.3 % (std = 10.3).
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Vivanti, R., Ephrat, A., Joskowicz, L., Lev-Cohain, N., Karaaslan, O.A., Sosna, J. (2015). Automatic Liver Tumor Segmentation in Follow-Up CT Scans: Preliminary Method and Results. In: Wu, G., Coupé, P., Zhan, Y., Munsell, B., Rueckert, D. (eds) Patch-Based Techniques in Medical Imaging. Patch-MI 2015. Lecture Notes in Computer Science(), vol 9467. Springer, Cham. https://doi.org/10.1007/978-3-319-28194-0_7
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