Standard Plane Localisation in 3D Fetal Ultrasound Using Network with Geometric and Image Loss

Yuanwei Li, Juan J. Cerrolaza, Matthew Sinclair, Benjamin Hou, Amir Alansary, Bishesh Khanal, Jacqueline Matthew, Bernhard Kainz, Daniel Rueckert

Abstract: Standard scan plane detection in 3D fetal brain ultrasound (US) is a crucial step in the assessment of fetal brain development. We propose an automatic method for the detection of standard planes in 3D volumes by utilising a convolutional neural network (CNN) to learn the relationship between a 2D plane image and the transformation parameters required to move that plane towards the corresponding standard plane. In addition, we explore the effect of using two different training loss functions which exploit the geometric information and the image data of the extracted plane respectively. When evaluated on 72 subjects, our method achieves a plane detection error of 3.45 mm and 12.4 degrees.

Keywords: Convolutional neural network, Plane detection, Fetal ultrasound, Spatial transformer network

Author Affiliation: Imperial College London, Kings College London