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Heart segmentation plays an important role in accurate diagnosis and treatment of cardiovascular disease. More recently, deep convolutional neural networks (CNN) are predominant to many medical image analysis applications including 3D heart segmentation. For example, 3D UNet with U-shape encoder-decoder architecture performs well in volumetric segmentation. However, standard convolution, the building block of 3D CNN network usually contains a large number of parameters. In this paper, our objective is to investigate a light convolution module to build an efficient network. Inspired by 2D Tied Block Convolution (TBC), we introduce a Tied Block 3D Convolution (TBC-3D) operator which reuses a small number of convolution filters across each channel group. To this end, TBC-3D requires fewer parameters and is able to obtain more feature maps with high performance. Furthermore, we combine TBC-3D with the Ghost-3D module to construct Ghost Tied Block (GTB). Specifically, Ghost module employs standard convolution (OP1) with few filters to obtain intrinsic feature maps, and then generates more features by cheap linear operation like depth-wise convolution (OP2). TBC-3D is applied in both OP1 and OP2 in the Ghost-3D module. Compared to state-of-the-art solutions using 3D UNet-like architecture, our model with GTB achieves competitive performance on the MM-WHS whole heart segmentation Challenge 2017 datasets with 2.31x less parameters and 1.93x fewer FLOPs.
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