Ghost-Light-3dnet: Efficient Network For Heart Segmentation

Accurate 3D whole heart segmentation provides more details of the morphological and pathological information that could help doctors with more effective patient-specific treatments. 3D CNN network has been recognized as an important role in accurate volumetric segmentation. Typically, 3D CNN network has a large number of parameters as well as the floating point operations (FLOPs), which leads to heavy and complex computation. In this paper, we introduce an efficient 3D network (Ghost-Light-3DNet) for heart segmentation. Our solution is characterized by two key components: First, inspired by GhostNet in 2D, we extend the Ghost module to 3D which can generate more feature maps from cheap operations. Second, a sequential separable conv with residual module is applied as a light plug-and-play component to further reduce network parameters and FLOPs. For evaluation, the proposed method is validated on the MM-WHS heart segmentation Challenge 2017 datasets. Compared to state-of-the-art solution using 3D UNet-like architecture, our Ghost-Light-3DNet achieves comparable segmentation accuracy with the 2. 18x fewer parameters and 4. 48x less FLOPs, respectively.