Improving Fetal Head Contour Detection by Object Localisation with Deep Learning



Al-Bander, Baidaa, Alzahrani, Theiab, Alzahrani, Saeed, Williams, Bryan M ORCID: 0000-0001-5930-287X and Zheng, Yalin ORCID: 0000-0002-7873-0922
(2020) Improving Fetal Head Contour Detection by Object Localisation with Deep Learning. .

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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.

Item Type: Conference or Workshop Item (Unspecified)
Uncontrolled Keywords: Fetal ultrasound, Object detection and segmentation, Deep learning, CNN, FCN
Depositing User: Symplectic Admin
Date Deposited: 08 Jun 2020 08:27
Last Modified: 18 Jan 2023 23:50
DOI: 10.1007/978-3-030-39343-4_12
Related URLs:
URI: https://livrepository.liverpool.ac.uk/id/eprint/3089638