Cookies on this website
We use cookies to ensure that we give you the best experience on our website. If you click 'Continue' we'll assume that you are happy to receive all cookies and you won't see this message again. Click 'Find out more' for information on how to change your cookie settings.

© Springer International Publishing AG 2017. In this paper, voxel probability maps generated by a novel fovea fully convolutional network architecture (FovFCN) are used as additional feature images in the context of a segmentation approach based on deformable shape models. The method is applied to fetal 3D ultrasound image data aiming at a segmentation of the abdominal outline of the fetal torso. This is of interest, e.g., for measuring the fetal abdominal circumference, a standard biometric measure in prenatal screening. The method is trained on 126 3D ultrasound images and tested on 30 additional scans. The results show that the approach can successfully combine the advantages of FovFCNs and deformable shape models in the context of challenging image data, such as given by fetal ultrasound. With a mean error of 2.24 mm, the combination of model-based segmentation and neural networks outperforms the separate approaches.

Original publication

DOI

10.1007/978-3-319-67561-9_6

Type

Conference paper

Publication Date

01/01/2017

Volume

10554 LNCS

Pages

52 - 61