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Improving Fetal Head Contour Detection by Object Localisation with Deep Learning

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Improving Fetal Head Contour Detection by Object Localisation with Deep Learning. / Al-Bander, Baidaa; Alzahrani, Theiab; Alzahrani, Saeed; Williams, Bryan M.; Zheng, Yalin.

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/ISSNConference contribution/Paperpeer-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). https://doi.org/10.1007/978-3-030-39343-4_12

Author

Al-Bander, Baidaa ; Alzahrani, Theiab ; Alzahrani, Saeed ; Williams, Bryan M. ; Zheng, Yalin. / Improving Fetal Head Contour Detection by Object Localisation with Deep Learning. Medical Image Understanding and Analysis : 23rd Conference, MIUA 2019, Proceedings. editor / Yalin Zheng ; Bryan M. Williams ; Ke Chen. Springer, 2020. pp. 142-150 (Communications in Computer and Information Science).

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 -