Fully automated, real-time 3D-ultrasound segmentation to estimate first trimester placental volume using deep learning - Looney, P; Stevenson, G; Nicolaides, K; Plasencia, W; Molloholli, M; Natsis, S; Collins, S (American Society for Clinical Investigation, 2018)
Objectives: 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. Methods: The placenta was segmented from 2393 first trimester 3D-US volumes using a semi-automated technique. This was quality controlled by three operators to produce the ‘ground-truth’ dataset. A fully convolutional neural network (OxNNet) was trained using this ‘ground-truth’ dataset to automatically segment the placenta. Findings: OxNNet delivered state of the art automatic segmentation (median Dice similarity coefficient of 0.84). The effect of training set size on the performance of OxNNet demonstrated the need for large datasets (n=1200, median DSC (inter-quartile range) 0.81 (0.15)). The clinical utility of placental volume was tested by looking at prediction of small-for-gestational-age (SGA) babies at term. The receiver-operating characteristics curves demonstrated almost identical results (OxNNet 0.65 (95% CI; 0.61-0.69) and ‘ground-truth’ 0.65 (95% CI; 0.61-0.69)). Conclusions: Our results demonstrated good similarity to the ‘ground-truth’ and almost identical clinical results for the prediction of SGA. Our open source software, OxNNet, and trained models are available on request.