Liver tumor segmentation using 2.5D UV-Net with multi-scale convolution.

Liver tumor segmentation networks are generally based on U-shaped encoder-decoder network with 2D or 3D structure. However, 2D networks lose the inter-layer information of continuous slices and 3D networks might introduce unacceptable parameters for GPU memory. As a result, 2.5D networks were proposed to balance the memory consumption and 3D context. Different from the canonical 2.5D design, which utilizes a 2D network combined with RNN, we propose a new 2.5D design called UV-Net to encode the inter-layer information in the context of 3D convolution, and reconstruct the high-resolution results with 2D deconvolution. At the same time, the multi-scale convolution structure enables multi-scale feature extraction without extra computational cost, which effectively mines structured information, reduces information redundancy, strengthens independent features, and makes feature dimension sparse, to enhance network capacity and efficiency. Combined with the proposed preprocessing method of removing mean energy, UV-Net significantly outperforms the existing methods in liver tumor segmentation and especially improves the segmentation accuracy of small objects on the LiTS2017 dataset.