BAA-Net: attention-based CNN for automatic placental segmentation of MR images

Abstract

The placenta is an important organ for the material exchange between the fetus and the mother. The abnormal placenta may lead to fetal intrauterine growth restriction, invasive placenta and other related diseases, thus endangering the health of both the mother and fetus. Accurate segmentation of placental tissue in fetal magnetic resonance images could help diagnose placental abnormalities. However, the manual segmentation of placenta is very time-consuming, and the semi automatic placental segmentation methods still require the operator’s interaction. In this paper, we proposed a fully automatic placental segmentation method, in which BiO-Net is used as the backbone network and is further improved by embedding Atrous Spatial Pyramid Pooling (ASPP) and attention mechanism, termed as BAA-Net. To retain more details of boundary information, the ASPP module was introduced to the encoder for capturing high-resolution feature maps to improve the placental segmentation performance. Because different feature channels from the encoder and decoder have different effects on the segmentation task, to make better use of the most useful features, four channel attention modules were introduced into the decoder to highlight the most relevant feature channels. To evaluate the performance of the proposed BAA-Net, MR images of 20 pregnant women were used for the experiments. The Dice and average symmetric surface distance (ASSD) obtained by our BAA-Net are 0.8674 and 2.8880 mm, respectively. The experimental results show that the proposed BAA-Net is effective for the automatic placental segmentation, comparing with the existing methods. Accurate placental segmentation is conducive to the diagnosis of placental abnormalities, which has important clinical significance.