matic 3D ultrasound segmentation of the first trimester placenta using deep learning

© 2017 IEEE. Placental volume measured with 3D ultrasound in the first trimester has been shown to be correlated to adverse pregnancy outcomes. This could potentially be used as a screening test to predict the 'at risk' pregnancy. However, manual segmentation whilst previously shown to be accurate and repeatable is very time consuming and semi-automated methods still require operator input. To generate a screening tool, fully automated placental segmentation is required. In this work, a deep convolutional neural network (cNN), DeepMedic, was trained using the output of the semi-automated Random Walker method as ground truth. 300 3D ultrasound scans of first trimester placentas were used to train, validate and test the cNN. Compared against the semi-automated segmentation, resultant median (1 st Quartile, 3 rd Quartile) Dice Similarity Coefficient was 0.73 (0.66, 0.76). The median (1 st Quartile, 3 rd Quartile) Hausdorff distance was 27 mm (18 mm, 36 mm). We present the first attempt at using a deep cNN for segmentation of 3D ultrasound of the placenta. This work shows that feasible results compared to ground truth were obtained that could form the basis of a fully automatic segmentation method.