Automatic Segmentation of 3D Ultrasound Spine Curvature Using Convolutional Neural Network

Sunetra Banerjee; Sai Ho Ling; Juan Lyu; Steven Su; Yong Ping Zheng

doi:10.1109/EMBC44109.2020.9175673

Automatic Segmentation of 3D Ultrasound Spine Curvature Using Convolutional Neural Network

Sunetra Banerjee
, Sai Ho Ling
, Juan Lyu
, Steven Su
, Yong Ping Zheng

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

12 Citations (Scopus)

Abstract

Scoliosis is a 3D spinal deformation where the spine takes a lateral curvature, which generates an angle in a coronal plane. For periodic detection of scoliosis, safe and economic imaging modality is needed as continuous exposure to radiative imaging may cause cancer. 3D ultrasound imaging is a cost-effective and radiation-free imaging modality which gives volume projection image. Identification of mid-spine line using manual, semi-automatic and automatic methods have been published. Still, there are some difficulties like variations in human measurement, slow processing of data associated with them. In this paper, we propose an unsupervised ground truth generation and automatic spine curvature segmentation using U- Net. This approach of the application of Convolutional Neural Network on ultrasound spine image, to perform automatic detection of scoliosis, is a novel one.

Original language	English
Title of host publication	42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society
Subtitle of host publication	Enabling Innovative Technologies for Global Healthcare, EMBC 2020
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	2039-2042
Number of pages	4
ISBN (Electronic)	9781728119908
DOIs	https://doi.org/10.1109/EMBC44109.2020.9175673
Publication status	Published - Jul 2020
Event	42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society, EMBC 2020 - Montreal, Canada Duration: 20 Jul 2020 → 24 Jul 2020

Publication series

Name	Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
Volume	2020-July
ISSN (Print)	1557-170X

Conference

Conference	42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society, EMBC 2020
Country/Territory	Canada
City	Montreal
Period	20/07/20 → 24/07/20

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Convolutional Neural Network
Scoliosis
Segmentation
Ultrasound imaging

ASJC Scopus subject areas

Signal Processing
Biomedical Engineering
Computer Vision and Pattern Recognition
Health Informatics

More information

10.1109/EMBC44109.2020.9175673

Cite this

Banerjee, S., Ling, S. H., Lyu, J., Su, S., & Zheng, Y. P. (2020). Automatic Segmentation of 3D Ultrasound Spine Curvature Using Convolutional Neural Network. In 42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society: Enabling Innovative Technologies for Global Healthcare, EMBC 2020 (pp. 2039-2042). Article 9175673 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS; Vol. 2020-July). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/EMBC44109.2020.9175673

Banerjee, Sunetra ; Ling, Sai Ho ; Lyu, Juan et al. / Automatic Segmentation of 3D Ultrasound Spine Curvature Using Convolutional Neural Network. 42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society: Enabling Innovative Technologies for Global Healthcare, EMBC 2020. Institute of Electrical and Electronics Engineers Inc., 2020. pp. 2039-2042 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS).

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title = "Automatic Segmentation of 3D Ultrasound Spine Curvature Using Convolutional Neural Network",

abstract = "Scoliosis is a 3D spinal deformation where the spine takes a lateral curvature, which generates an angle in a coronal plane. For periodic detection of scoliosis, safe and economic imaging modality is needed as continuous exposure to radiative imaging may cause cancer. 3D ultrasound imaging is a cost-effective and radiation-free imaging modality which gives volume projection image. Identification of mid-spine line using manual, semi-automatic and automatic methods have been published. Still, there are some difficulties like variations in human measurement, slow processing of data associated with them. In this paper, we propose an unsupervised ground truth generation and automatic spine curvature segmentation using U- Net. This approach of the application of Convolutional Neural Network on ultrasound spine image, to perform automatic detection of scoliosis, is a novel one.",

keywords = "Convolutional Neural Network, Scoliosis, Segmentation, Ultrasound imaging",

author = "Sunetra Banerjee and Ling, \{Sai Ho\} and Juan Lyu and Steven Su and Zheng, \{Yong Ping\}",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society, EMBC 2020 ; Conference date: 20-07-2020 Through 24-07-2020",

year = "2020",

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doi = "10.1109/EMBC44109.2020.9175673",

language = "English",

series = "Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS",

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Banerjee, S, Ling, SH, Lyu, J, Su, S & Zheng, YP 2020, Automatic Segmentation of 3D Ultrasound Spine Curvature Using Convolutional Neural Network. in 42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society: Enabling Innovative Technologies for Global Healthcare, EMBC 2020., 9175673, Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, vol. 2020-July, Institute of Electrical and Electronics Engineers Inc., pp. 2039-2042, 42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society, EMBC 2020, Montreal, Canada, 20/07/20. https://doi.org/10.1109/EMBC44109.2020.9175673

Automatic Segmentation of 3D Ultrasound Spine Curvature Using Convolutional Neural Network. / Banerjee, Sunetra; Ling, Sai Ho; Lyu, Juan et al.
42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society: Enabling Innovative Technologies for Global Healthcare, EMBC 2020. Institute of Electrical and Electronics Engineers Inc., 2020. p. 2039-2042 9175673 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS; Vol. 2020-July).

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

TY - GEN

T1 - Automatic Segmentation of 3D Ultrasound Spine Curvature Using Convolutional Neural Network

AU - Banerjee, Sunetra

AU - Ling, Sai Ho

AU - Lyu, Juan

AU - Su, Steven

AU - Zheng, Yong Ping

PY - 2020/7

Y1 - 2020/7

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AB - Scoliosis is a 3D spinal deformation where the spine takes a lateral curvature, which generates an angle in a coronal plane. For periodic detection of scoliosis, safe and economic imaging modality is needed as continuous exposure to radiative imaging may cause cancer. 3D ultrasound imaging is a cost-effective and radiation-free imaging modality which gives volume projection image. Identification of mid-spine line using manual, semi-automatic and automatic methods have been published. Still, there are some difficulties like variations in human measurement, slow processing of data associated with them. In this paper, we propose an unsupervised ground truth generation and automatic spine curvature segmentation using U- Net. This approach of the application of Convolutional Neural Network on ultrasound spine image, to perform automatic detection of scoliosis, is a novel one.

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KW - Segmentation

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M3 - Conference article published in proceeding or book

AN - SCOPUS:85091045584

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SP - 2039

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PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society, EMBC 2020

Y2 - 20 July 2020 through 24 July 2020

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Banerjee S, Ling SH, Lyu J, Su S, Zheng YP. Automatic Segmentation of 3D Ultrasound Spine Curvature Using Convolutional Neural Network. In 42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society: Enabling Innovative Technologies for Global Healthcare, EMBC 2020. Institute of Electrical and Electronics Engineers Inc. 2020. p. 2039-2042. 9175673. (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS). doi: 10.1109/EMBC44109.2020.9175673

Automatic Segmentation of 3D Ultrasound Spine Curvature Using Convolutional Neural Network

Abstract

Publication series

Conference

UN SDGs

Keywords

ASJC Scopus subject areas

More information

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