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 tra

SUBMITTER: Looney P 

PROVIDER: S-EPMC6124436 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature