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Improving Fetal Head Contour Detection by Object Localisation with Deep Learning

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Publication date24/01/2020
Host publicationMedical Image Understanding and Analysis : 23rd Conference, MIUA 2019, Proceedings
EditorsYalin Zheng, Bryan M. Williams, Ke Chen
PublisherSpringer
Pages142-150
Number of pages9
ISBN (Print)9783030393427
<mark>Original language</mark>English
Event23rd Conference on Medical Image Understanding and Analysis, MIUA 2019 - Liverpool, United Kingdom
Duration: 24/07/201926/07/2019

Conference

Conference23rd Conference on Medical Image Understanding and Analysis, MIUA 2019
Country/TerritoryUnited Kingdom
CityLiverpool
Period24/07/1926/07/19

Publication series

NameCommunications in Computer and Information Science
Volume1065 CCIS
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937

Conference

Conference23rd Conference on Medical Image Understanding and Analysis, MIUA 2019
Country/TerritoryUnited Kingdom
CityLiverpool
Period24/07/1926/07/19

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.