The V-Net based 3D fully convolutional neural networks have been widely used in liver volumetric data segmentation. However, due to the large number of parameters of these networks, 3D FCNs suffer from high computational cost and GPU memory usage. To address these issues, we design a lightweight V-Net (LV-Net) for liver segmentation in this paper. The proposed network makes two contributions. The first is that we design an inverted residual bottleneck block (IRB block) and a 3D average pooling block and apply them to the proposed LV-Net. Compared with vanilla convolution, depth-wise convolution and point-wise convolution employed by the IRB block can not only reduce the number of parameters significantly, but also extract features sufficiently well by decoupling cross-channel corrections and spatial correlations. The second is that the LV-Net employs 3D deep supervision to improve the final loss function in training phase, which makes
the proposed LV-Net acquire a more powerful discrimination capability between liver areas and non-liver areas. The proposed
LV-Net is evaluated on public LiTS [1] dataset, and experiments demonstrate that the proposed LV-Net is superior to popular 2D and 3D networks in terms of segmentation performance, parameter quantity and computational cost.