  Fully convolutional neural networks have made promising progress in joint
liver and liver tumor segmentation. Instead of following the debates over 2D
versus 3D networks (for example, pursuing the balance between large-scale 2D
pretraining and 3D context), in this paper, we novelly identify the wide
variation in the ratio between intra- and inter-slice resolutions as a crucial
obstacle to the performance. To tackle the mismatch between the intra- and
inter-slice information, we propose a slice-aware 2.5D network that emphasizes
extracting discriminative features utilizing not only in-plane semantics but
also out-of-plane coherence for each separate slice. Specifically, we present a
slice-wise multi-input multi-output architecture to instantiate such a design
paradigm, which contains a Multi-Branch Decoder (MD) with a Slice-centric
Attention Block (SAB) for learning slice-specific features and a Densely
Connected Dice (DCD) loss to regularize the inter-slice predictions to be
coherent and continuous. Based on the aforementioned innovations, we achieve
state-of-the-art results on the MICCAI 2017 Liver Tumor Segmentation (LiTS)
dataset. Besides, we also test our model on the ISBI 2019 Segmentation of
THoracic Organs at Risk (SegTHOR) dataset, and the result proves the robustness
and generalizability of the proposed method in other segmentation tasks.
