Abstract
Automated segmentation of liver tumors in
contrast-enhanced abdominal computed tomography (CT) scans is essential in assisting medical
professionals to evaluate tumor development and
make fast therapeutic schedule. Although deep
convolutional neural networks (DCNNs) have contributed many breakthroughs in image segmentation, this task remains challenging, since 2D DCNNs are incapable of exploring the inter-slice information and 3D DCNNs are too complex to be
trained with the available small dataset. In this paper, we propose the light-weight hybrid convolutional network (LW-HCN) to segment the liver and
its tumors in CT volumes. Instead of combining a
2D and a 3D networks for coarse-to-fine segmentation, LW-HCN has a encoder-decoder structure,
in which 2D convolutions used at the bottom of the
encoder decreases the complexity and 3D convolutions used in other layers explore both spatial and
temporal information. To further reduce the complexity, we design the depthwise and spatiotemporal separate (DSTS) factorization for 3D convolutions, which not only reduces parameters dramatically but also improves the performance. We evaluated the proposed LW-HCN model against several recent methods on the LiTS and 3D-IRCADb
datasets and achieved, respectively, the Dice per
case of 73.0% and 94.1% for tumor segmentation,
setting a new state of the art