Combining deep learning and multi-atlas label fusion for automated placenta segmentation from 3DUS

Abstract

In recent years there is growing interest in studying the placenta in vivo. However, 3D ultrasound images (3DUS) are typically very noisy, and the placenta shape and position are highly variable. As such, placental segmentation efforts to date have focused on interactive methods that require considerable user input, or automated methods with relatively low performance and various limitations. We propose a novel algorithm using a combination of deep learning and multi-atlas joint label fusion (JLF) methods for automated segmentation of the placenta in 3DUS images. We extract 2D cross-sections of the ultrasound cone beam with a variety of orientations from the 3DUS images and train a convolutional neural network (CNN) on these slices. We use the prediction by the CNN to initialize the multi-atlas JLF algorithm. The posteriors obtained by the CNN and JLF models are combined to enhance the overall segmentation performance. The method is evaluated on a dataset of 47 patients in the first trimester. We perform 4-fold cross-validation and achieve a mean Dice coefficient of 86.3 ± 5.3 for the test folds. This is a substantial increase in accuracy compared to existing automated methods and is comparable to the performance of semi-automated methods currently considered the bronze standard in placenta segmentation.