Fully Convolutional Network for Liver Segmentation
and Lesions Detection



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.


Ben-Cohen, A., Diamant, I., Klang, E., Amitai, M., & Greenspan, H. (2016). Fully Convolutional Network for Liver Segmentation and Lesions Detection.
In International Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis (pp. 77-85). Springer International Publishing.

Ben-Cohen, A., Klang, E., Amitai, M., & Greenspan, H. (2016). Sparsity-based liver metastases detection using learned dictionaries.
In Biomedical Imaging (ISBI), 2016 IEEE 13th International Symposium on (pp. 1195-1198). IEEE.

horizontal rule

For problems or questions regarding this web page contact hayit@eng.tau.ac.il.
Last updated: 12/11/16.