Paper
15 March 2019 Offset regression networks for view plane estimation in 3D fetal ultrasound
Author Affiliations +
Abstract
Ultrasound (US) is the modality of choice for fetal screening, which includes the assessment of a variety of standardized growth measurements, like the abdominal circumference (AC). Screening guidelines define criteria on the scan plane, in which the measurement is taken. As US is increasingly becoming a 3D modality, approaches for automated determination of the optimal scan plane in a volumetric dataset would greatly improve the workflow. In this work, a novel framework for deep hyperplane learning is proposed and applied for view plane estimation in fetal US examinations. The approach is tightly integrated in the clinical workflow and consists of two main steps. First, the bounding box around the structure of interest is determined in the central slice (MPR). Second, offsets from the structure in the bounding box to the optimal view plane are estimated. By linear regression through the estimated offsets, the view plane coordinates can then be determined. The presented approach is successfully applied on clinical screening data for AC plane estimation and a high accuracy is obtained, outperforming or comparable to recent publications on the same application.
© (2019) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Alexander Schmidt-Richberg, Nicole Schadewaldt, Tobias Klinder, Matthias Lenga, Robert Trahms, Earl Canfield, David Roundhill, and Cristian Lorenz "Offset regression networks for view plane estimation in 3D fetal ultrasound", Proc. SPIE 10949, Medical Imaging 2019: Image Processing, 109493K (15 March 2019); https://doi.org/10.1117/12.2512697
Lens.org Logo
CITATIONS
Cited by 8 scholarly publications.
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Fetus

Ultrasonography

Abdomen

Network architectures

3D image processing

Neural networks

Back to Top