Using Combined CNNs for ROI Segmentation in Early Investigation of Pregnancy

Artificial intelligence applications are recently showing promising results in advancing prenatal ultrasound examination. Regarding deep learning contribution for early pregnancy evaluation in ultrasound images, research is still in its infancy. There are some ultrasound elements whose measurements offer essential information on early pregnancy. Proper determination of the gestational age relates mainly to the dimension of the gestational sac and the length of the embryo. In this regard, semantic segmentation can lead to improved imagistic diagnosis by automatized measurement of the mentioned structures. The paper presents a precise pregnancy detection system in ultrasound images based on artificial intelligence by proposing various combined convolutional neural networks focused on semantic segmentation tasks of some regions of interest like an embryo and gestational sac and selecting the most accurate ones in terms of multiple performance metrics. The proposed convolutional neural networks are DeepLabV3+networks based on multiple state-of-the-art convolutional neural networks, used as encoders, such as ResNet-50, ResNet-18, InceptionResNet-V2, Xception, and MobileNet-V2.