Al-Bander B, Alzahrani T, Alzahrani S, Williams BM, Zheng Y. Improving fetal head contour detection by object localisation with deep learning. InMedical Image Understanding and Analysis: 23rd Conference, MIUA 2019, Liverpool, UK, July 24–26, 2019, Proceedings 2020 Jan 24 (pp. 142-150). Cham: Springer International Publishing.
https://link.springer.com/chapter/10.1007/978-3-030-39343-4_12
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±1.32. The experimental results demonstrate that the proposed deep learning method is promising in automatic fetal head detection and segmentation.
