  A fully automatic technique for segmenting the liver and localizing its
unhealthy tissues is a convenient tool in order to diagnose hepatic diseases
and assess the response to the according treatments. In this work we propose a
method to segment the liver and its lesions from Computed Tomography (CT) scans
using Convolutional Neural Networks (CNNs), that have proven good results in a
variety of computer vision tasks, including medical imaging. The network that
segments the lesions consists of a cascaded architecture, which first focuses
on the region of the liver in order to segment the lesions on it. Moreover, we
train a detector to localize the lesions, and mask the results of the
segmentation network with the positive detections. The segmentation
architecture is based on DRIU, a Fully Convolutional Network (FCN) with side
outputs that work on feature maps of different resolutions, to finally benefit
from the multi-scale information learned by different stages of the network.
The main contribution of this work is the use of a detector to localize the
lesions, which we show to be beneficial to remove false positives triggered by
the segmentation network. Source code and models are available at
https://imatge-upc.github.io/liverseg-2017-nipsws/ .
