  At present, lesion segmentation is still performed manually (or
semi-automatically) by medical experts. To facilitate this process, we
contribute a fully-automatic lesion segmentation pipeline. This work proposes a
method as a part of the LiTS (Liver Tumor Segmentation Challenge) competition
for ISBI 17 and MICCAI 17 comparing methods for automatics egmentation of liver
lesions in CT scans. By utilizing cascaded, densely connected 2D U-Nets and a
Tversky-coefficient based loss function, our framework achieves very good shape
extractions with high detection sensitivity, with competitive scores at time of
publication. In addition, adjusting hyperparameters in our Tversky-loss allows
to tune the network towards higher sensitivity or robustness.
