Fully automated, real-time 3D ultrasound segmentation to estimate first trimester placental volume using deep learning

Abstract

We present a new technique to fully automate the segmentation of an organ from 3D ultrasound (3D-US) volumes, using the placenta as the target organ. Image analysis tools to estimate organ volume do exist but are too time consuming and operator dependant. Fully automating the segmentation process would potentially allow the use of placental volume to screen for increased risk of pregnancy complications. The placenta was segmented from 2,393 first trimester 3D-US volumes using a semiautomated technique. This was quality controlled by three operators to produce the "ground-truth" data set. A fully convolutional neural network (OxNNet) was trained using this ground-truth data set to automatically segment the placenta. OxNNet delivered state-of-the-art automatic segmentation. The effect of training set size on the performance of OxNNet demonstrated the need for large data sets. The clinical utility of placental volume was tested by looking at predictions of small-for-gestational-age babies at term. The receiver-operating characteristics curves demonstrated almost identical results between OxNNet and the ground-truth). Our results demonstrated good similarity to the ground-truth and almost identical clinical results for the prediction of SGA.

Keywords: Diagnostic imaging; Obstetrics/gynecology; Reproductive Biology.

Conflict of interest statement

Conflict of interest: The authors have declared that no conflict of interest exists.

Figures

Figure 1. OxNNet Training.

Learning curves of mean squared error (MSE) for both training (red) and validation (blue) data sets for different numbers of cases in training. Box plot of Dice similarity coefficients (DSC) for OxNNet using different numbers of cases in training (100–1,200).

Figure 2. OxNNet Metrics.

Histograms showing the distribution of the Dice similarity coefficient (DSC), relative volume difference (RVD), and Hausdorff distance (actual and mean values) for the cross validated test sets of 2,393 cases. The median is shown by the red dashed line in each figure.

Figure 3. OxNNet compared to groundtruth.

Placental segmentations with 2D B-mode plane (left). RW segmentation (center; red). OxNNet prediction (right; blue). The values of the Dice similarity coefficient and Hausdorff distance metrics for this case were 0.838 and 12.6 mm, respectively.

Figure 4. Placental volume and gestational age.

Box plots showing the distribution of actual placental volumes (PlVol) for OxNNet, the logarithm of the multiples of the medians (MoMs) for OxNNet, actual placental volumes (PlVol) for random walker, and the logarithm of MoMs for random walker versus the gestational age (GA). The number of cases for each GA (y axis on the right) is plotted as a column chart in the background.

Figure 5. Sensitivity and specificity.

Receiver-operating characteristics (ROC) curves of placental volume calculated by both the fully automated fCNN (OxNNet) and the random walker (RW) technique to predict small for gestational age (SGA:

Figure 6. The architecture of the OxNNet fully convolutional neural network.