Standard
Improving Fetal Head Contour Detection by Object Localisation with Deep Learning. / Al-Bander, Baidaa; Alzahrani, Theiab; Alzahrani, Saeed et al.
Medical Image Understanding and Analysis : 23rd Conference, MIUA 2019, Proceedings. ed. / Yalin Zheng; Bryan M. Williams; Ke Chen. Springer, 2020. p. 142-150 (Communications in Computer and Information Science; Vol. 1065 CCIS).
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Harvard
Al-Bander, B, Alzahrani, T, Alzahrani, S
, Williams, BM & Zheng, Y 2020,
Improving Fetal Head Contour Detection by Object Localisation with Deep Learning. in Y Zheng, BM Williams & K Chen (eds),
Medical Image Understanding and Analysis : 23rd Conference, MIUA 2019, Proceedings. Communications in Computer and Information Science, vol. 1065 CCIS, Springer, pp. 142-150, 23rd Conference on Medical Image Understanding and Analysis, MIUA 2019, Liverpool, United Kingdom,
24/07/19.
https://doi.org/10.1007/978-3-030-39343-4_12
APA
Al-Bander, B., Alzahrani, T., Alzahrani, S.
, Williams, B. M., & Zheng, Y. (2020).
Improving Fetal Head Contour Detection by Object Localisation with Deep Learning. In Y. Zheng, B. M. Williams, & K. Chen (Eds.),
Medical Image Understanding and Analysis : 23rd Conference, MIUA 2019, Proceedings (pp. 142-150). (Communications in Computer and Information Science; Vol. 1065 CCIS). Springer.
https://doi.org/10.1007/978-3-030-39343-4_12
Vancouver
Al-Bander B, Alzahrani T, Alzahrani S
, Williams BM, Zheng Y.
Improving Fetal Head Contour Detection by Object Localisation with Deep Learning. In Zheng Y, Williams BM, Chen K, editors, Medical Image Understanding and Analysis : 23rd Conference, MIUA 2019, Proceedings. Springer. 2020. p. 142-150. (Communications in Computer and Information Science). doi: 10.1007/978-3-030-39343-4_12
Author
Bibtex
@inproceedings{96b3d36655df4d08b7880cc6ab480add,
title = "Improving Fetal Head Contour Detection by Object Localisation with Deep Learning",
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.",
keywords = "CNN, Deep learning, FCN, Fetal ultrasound, Object detection and segmentation",
author = "Baidaa Al-Bander and Theiab Alzahrani and Saeed Alzahrani and Williams, {Bryan M.} and Yalin Zheng",
year = "2020",
month = jan,
day = "24",
doi = "10.1007/978-3-030-39343-4_12",
language = "English",
isbn = "9783030393427",
series = "Communications in Computer and Information Science",
publisher = "Springer",
pages = "142--150",
editor = "Yalin Zheng and Williams, {Bryan M.} and Ke Chen",
booktitle = "Medical Image Understanding and Analysis",
note = "23rd Conference on Medical Image Understanding and Analysis, MIUA 2019 ; Conference date: 24-07-2019 Through 26-07-2019",
}
RIS
TY - GEN
T1 - Improving Fetal Head Contour Detection by Object Localisation with Deep Learning
AU - Al-Bander, Baidaa
AU - Alzahrani, Theiab
AU - Alzahrani, Saeed
AU - Williams, Bryan M.
AU - Zheng, Yalin
PY - 2020/1/24
Y1 - 2020/1/24
N2 - 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.
AB - 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.
KW - CNN
KW - Deep learning
KW - FCN
KW - Fetal ultrasound
KW - Object detection and segmentation
U2 - 10.1007/978-3-030-39343-4_12
DO - 10.1007/978-3-030-39343-4_12
M3 - Conference contribution/Paper
AN - SCOPUS:85079086472
SN - 9783030393427
T3 - Communications in Computer and Information Science
SP - 142
EP - 150
BT - Medical Image Understanding and Analysis
A2 - Zheng, Yalin
A2 - Williams, Bryan M.
A2 - Chen, Ke
PB - Springer
T2 - 23rd Conference on Medical Image Understanding and Analysis, MIUA 2019
Y2 - 24 July 2019 through 26 July 2019
ER -