Improving Fetal Head Contour Detection by Object Localisation with Deep Learning

Al-Bander, Baidaa and Alzahrani, Theiab and Alzahrani, Saeed and Williams, Bryan M. and Zheng, Yalin (2020) Improving Fetal Head Contour Detection by Object Localisation with Deep Learning. In: Medical Image Understanding and Analysis : 23rd Conference, MIUA 2019, Proceedings. Communications in Computer and Information Science . Springer, GBR, pp. 142-150. ISBN 9783030393427

Abstract

Ultrasound-based fetal head biometrics measurement is a key indicator in monitoring the conditions of fetuses. Since manual measurement of relevant anatomical structures of fetal head is time-consuming and subject to inter-observer variability, there has been strong interest in finding automated, robust, accurate and reliable method. In this paper, we propose a deep learning-based method to segment fetal head from ultrasound images. The proposed method formulates the detection of fetal head boundary as a combined object localisation and segmentation problem based on deep learning model. Incorporating an object localisation in a framework developed for segmentation purpose aims to improve the segmentation accuracy achieved by fully convolutional network. Finally, ellipse is fitted on the contour of the segmented fetal head using least-squares ellipse fitting method. The proposed model is trained on 999 2-dimensional ultrasound images and tested on 335 images achieving Dice coefficient of$$97.73 \pm 1.32$$. The experimental results demonstrate that the proposed deep learning method is promising in automatic fetal head detection and segmentation.