Final published version
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Publication date | 24/01/2020 |
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Host publication | Medical Image Understanding and Analysis : 23rd Conference, MIUA 2019, Proceedings |
Editors | Yalin Zheng, Bryan M. Williams, Ke Chen |
Publisher | Springer |
Pages | 142-150 |
Number of pages | 9 |
ISBN (print) | 9783030393427 |
<mark>Original language</mark> | English |
Event | 23rd Conference on Medical Image Understanding and Analysis, MIUA 2019 - Liverpool, United Kingdom Duration: 24/07/2019 → 26/07/2019 |
Conference | 23rd Conference on Medical Image Understanding and Analysis, MIUA 2019 |
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Country/Territory | United Kingdom |
City | Liverpool |
Period | 24/07/19 → 26/07/19 |
Name | Communications in Computer and Information Science |
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Volume | 1065 CCIS |
ISSN (Print) | 1865-0929 |
ISSN (electronic) | 1865-0937 |
Conference | 23rd Conference on Medical Image Understanding and Analysis, MIUA 2019 |
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Country/Territory | United Kingdom |
City | Liverpool |
Period | 24/07/19 → 26/07/19 |
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.