Journal
JCI INSIGHT
Volume 3, Issue 11, Pages -Publisher
AMER SOC CLINICAL INVESTIGATION INC
DOI: 10.1172/jci.insight.120178
Keywords
-
Categories
Funding
- Eunice Kennedy Shriver National Institute of Child Health and Human Development Human Placenta Project of the National Institutes of Health [UO1-HD087209]
- Leslie Stevens' Fund, Sydney
Ask authors/readers for more resources
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 groundtruth). Our results demonstrated good similarity to the ground-truth and almost identical clinical results for the prediction of SGA.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available