DCO-Net: Deformable convolution-based O-shape network for fully automated placenta segmentation

Abstract

The placenta is an important organ that connects the fetus with the mother during pregnancy. Many pregnancies fail because of the placental abnormality. Nowadays, placental segmentation techniques based on magnetic resonance (MR) images are increasingly used to assist in the diagnosis of placental abnormalities. However, manual segmentation of the placenta is unreliable and time-consuming. In comparison to manually segmentation methods, the CNN-based methods perform better. However, due to the limitations of the fixed geometric structures in the traditional convolution, the features of the edge are not completely obtained. That is the challenge in placenta segmentation. By incorporating offsets into the regular grid sampling locations of standard convolution, deformable convolution allows for the utilization of spatial information to its full potential. Therefore, in this paper, we proposed a fully automatic placental segmentation method based on deformable convolution, termed as Deformable Convolution O-shape Network (DCO-Net). Specifically, we adopt the U-shaped architecture and employed a bi-directional O-shaped route by adding backward skip connections. The bi-directional O-shaped route recurrently reused the building blocks without introducing any extra parameters. To evaluate the performance of the proposed DCO-Net, a total of 871 MR images from 20 pregnant women were used for the experiments. The dice and average symmetric surface distance (ASSD) obtained by DCO-Net were 0.8488 and 3.0976, respectively. The experimental results demonstrate that the proposed DCO-Net is more efficient for automatic placental segmentation compared with the other competitive approaches.