Abstract
In this work we explore a fully convolutional network (FCN) for the task of liver segmentation and liver metastases detection in computed tomography (CT) examinations. FCN has proven to be a very powerful tool for semantic segmentation. We explore the FCN performance on a relatively small dataset and compare it to patch based CNN and sparsity based classification schemes. Our data contains CT examinations from 20 patients with overall 68 lesions and 43 livers marked in one slice and 20 different patients with a full 3D liver segmentation. We ran 3-fold cross-validation and results indicate superiority of the FCN over all other methods tested. Using our fully automatic algorithm we achieved true positive rate of 0.86 and 0.6 false positive per case which are very promising and clinically relevant results.
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Acknowledgment
Part of this work was funded by the INTEL Collaborative Research Institute for Computational Intelligence (ICRI-CI).
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Ben-Cohen, A., Diamant, I., Klang, E., Amitai, M., Greenspan, H. (2016). Fully Convolutional Network for Liver Segmentation and Lesions Detection. In: Carneiro, G., et al. Deep Learning and Data Labeling for Medical Applications. DLMIA LABELS 2016 2016. Lecture Notes in Computer Science(), vol 10008. Springer, Cham. https://doi.org/10.1007/978-3-319-46976-8_9
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DOI: https://doi.org/10.1007/978-3-319-46976-8_9
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