Differentiating Operator Skill During Routine Fetal Ultrasound Scanning Using Probe Motion Tracking

Yipei Wang; Richard Droste; Jianbo Jiao; Harshita Sharma; Lior Drukker; Aris T. Papageorghiou; J. Alison Noble

doi:10.1007/978-3-030-60334-2_18

Differentiating Operator Skill During Routine Fetal Ultrasound Scanning Using Probe Motion Tracking

Yipei Wang^*, Richard Droste, Jianbo Jiao, Harshita Sharma, Lior Drukker, Aris T. Papageorghiou, J. Alison Noble

^*Corresponding author for this work

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

8 Citations (Scopus)

Abstract

In this paper, we consider differentiating operator skill during fetal ultrasound scanning using probe motion tracking. We present a novel convolutional neural network-based deep learning framework to model ultrasound probe motion in order to classify operator skill levels, that is invariant to operators’ personal scanning styles. In this study, probe motion data during routine second-trimester fetal ultrasound scanning was acquired by operators of known experience levels (2 newly-qualified operators and 10 expert operators). The results demonstrate that the proposed model can successfully learn underlying probe motion features that distinguish operator skill levels during routine fetal ultrasound with 95% accuracy.

Original language	English
Title of host publication	Medical Ultrasound, and Preterm, Perinatal and Paediatric Image Analysis - 1st International Workshop, ASMUS 2020, and 5th International Workshop, PIPPI 2020, Held in Conjunction with MICCAI 2020, Proceedings
Editors	Yipeng Hu, Roxane Licandro, J. Alison Noble, Jana Hutter, Andrew Melbourne, Stephen Aylward, Esra Abaci Turk, Jordina Torrents Barrena, Jordina Torrents Barrena
Publisher	Springer
Pages	180-188
Number of pages	9
ISBN (Print)	9783030603335
DOIs	https://doi.org/10.1007/978-3-030-60334-2_18
Publication status	Published - 2020
Event	1st International Workshop on Advances in Simplifying Medical UltraSound, ASMUS 2020, and the 5th International Workshop on Perinatal, Preterm and Paediatric Image Analysis, PIPPI 2020, held in conjunction with the 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020 - Lima, Peru Duration: 4 Oct 2020 → 8 Oct 2020

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12437 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	1st International Workshop on Advances in Simplifying Medical UltraSound, ASMUS 2020, and the 5th International Workshop on Perinatal, Preterm and Paediatric Image Analysis, PIPPI 2020, held in conjunction with the 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020
Country/Territory	Peru
City	Lima
Period	4/10/20 → 8/10/20

Bibliographical note

Funding Information:
Acknowledgement. We acknowledge the ERC (ERC-ADG-2015 694581, project PULSE), EPSRC (EP/M013774/1, Project Seebibyte), and the NIHR Oxford Biomedical Research Centre.

Publisher Copyright:
© 2020, Springer Nature Switzerland AG.

Keywords

Fetal ultrasound
Operator skill
Probe motion

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-030-60334-2_18

Cite this

Wang, Y., Droste, R., Jiao, J., Sharma, H., Drukker, L., Papageorghiou, A. T., & Noble, J. A. (2020). Differentiating Operator Skill During Routine Fetal Ultrasound Scanning Using Probe Motion Tracking. In Y. Hu, R. Licandro, J. A. Noble, J. Hutter, A. Melbourne, S. Aylward, E. Abaci Turk, J. Torrents Barrena, & J. Torrents Barrena (Eds.), Medical Ultrasound, and Preterm, Perinatal and Paediatric Image Analysis - 1st International Workshop, ASMUS 2020, and 5th International Workshop, PIPPI 2020, Held in Conjunction with MICCAI 2020, Proceedings (pp. 180-188). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12437 LNCS). Springer. https://doi.org/10.1007/978-3-030-60334-2_18

Wang, Yipei ; Droste, Richard ; Jiao, Jianbo et al. / Differentiating Operator Skill During Routine Fetal Ultrasound Scanning Using Probe Motion Tracking. Medical Ultrasound, and Preterm, Perinatal and Paediatric Image Analysis - 1st International Workshop, ASMUS 2020, and 5th International Workshop, PIPPI 2020, Held in Conjunction with MICCAI 2020, Proceedings. editor / Yipeng Hu ; Roxane Licandro ; J. Alison Noble ; Jana Hutter ; Andrew Melbourne ; Stephen Aylward ; Esra Abaci Turk ; Jordina Torrents Barrena ; Jordina Torrents Barrena. Springer, 2020. pp. 180-188 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{77bf18834654476bb2644998f5743dfe,

title = "Differentiating Operator Skill During Routine Fetal Ultrasound Scanning Using Probe Motion Tracking",

abstract = "In this paper, we consider differentiating operator skill during fetal ultrasound scanning using probe motion tracking. We present a novel convolutional neural network-based deep learning framework to model ultrasound probe motion in order to classify operator skill levels, that is invariant to operators{\textquoteright} personal scanning styles. In this study, probe motion data during routine second-trimester fetal ultrasound scanning was acquired by operators of known experience levels (2 newly-qualified operators and 10 expert operators). The results demonstrate that the proposed model can successfully learn underlying probe motion features that distinguish operator skill levels during routine fetal ultrasound with 95% accuracy.",

keywords = "Fetal ultrasound, Operator skill, Probe motion",

author = "Yipei Wang and Richard Droste and Jianbo Jiao and Harshita Sharma and Lior Drukker and Papageorghiou, {Aris T.} and Noble, {J. Alison}",

note = "Funding Information: Acknowledgement. We acknowledge the ERC (ERC-ADG-2015 694581, project PULSE), EPSRC (EP/M013774/1, Project Seebibyte), and the NIHR Oxford Biomedical Research Centre. Publisher Copyright: {\textcopyright} 2020, Springer Nature Switzerland AG.; 1st International Workshop on Advances in Simplifying Medical UltraSound, ASMUS 2020, and the 5th International Workshop on Perinatal, Preterm and Paediatric Image Analysis, PIPPI 2020, held in conjunction with the 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020 ; Conference date: 04-10-2020 Through 08-10-2020",

year = "2020",

doi = "10.1007/978-3-030-60334-2_18",

language = "English",

isbn = "9783030603335",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer",

pages = "180--188",

editor = "Yipeng Hu and Roxane Licandro and Noble, {J. Alison} and Jana Hutter and Andrew Melbourne and Stephen Aylward and {Abaci Turk}, Esra and {Torrents Barrena}, Jordina and {Torrents Barrena}, Jordina",

booktitle = "Medical Ultrasound, and Preterm, Perinatal and Paediatric Image Analysis - 1st International Workshop, ASMUS 2020, and 5th International Workshop, PIPPI 2020, Held in Conjunction with MICCAI 2020, Proceedings",

}

Wang, Y, Droste, R, Jiao, J, Sharma, H, Drukker, L, Papageorghiou, AT & Noble, JA 2020, Differentiating Operator Skill During Routine Fetal Ultrasound Scanning Using Probe Motion Tracking. in Y Hu, R Licandro, JA Noble, J Hutter, A Melbourne, S Aylward, E Abaci Turk, J Torrents Barrena & J Torrents Barrena (eds), Medical Ultrasound, and Preterm, Perinatal and Paediatric Image Analysis - 1st International Workshop, ASMUS 2020, and 5th International Workshop, PIPPI 2020, Held in Conjunction with MICCAI 2020, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12437 LNCS, Springer, pp. 180-188, 1st International Workshop on Advances in Simplifying Medical UltraSound, ASMUS 2020, and the 5th International Workshop on Perinatal, Preterm and Paediatric Image Analysis, PIPPI 2020, held in conjunction with the 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020, Lima, Peru, 4/10/20. https://doi.org/10.1007/978-3-030-60334-2_18

Differentiating Operator Skill During Routine Fetal Ultrasound Scanning Using Probe Motion Tracking. / Wang, Yipei; Droste, Richard; Jiao, Jianbo et al.
Medical Ultrasound, and Preterm, Perinatal and Paediatric Image Analysis - 1st International Workshop, ASMUS 2020, and 5th International Workshop, PIPPI 2020, Held in Conjunction with MICCAI 2020, Proceedings. ed. / Yipeng Hu; Roxane Licandro; J. Alison Noble; Jana Hutter; Andrew Melbourne; Stephen Aylward; Esra Abaci Turk; Jordina Torrents Barrena; Jordina Torrents Barrena. Springer, 2020. p. 180-188 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12437 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Differentiating Operator Skill During Routine Fetal Ultrasound Scanning Using Probe Motion Tracking

AU - Wang, Yipei

AU - Droste, Richard

AU - Jiao, Jianbo

AU - Sharma, Harshita

AU - Drukker, Lior

AU - Papageorghiou, Aris T.

AU - Noble, J. Alison

N1 - Funding Information: Acknowledgement. We acknowledge the ERC (ERC-ADG-2015 694581, project PULSE), EPSRC (EP/M013774/1, Project Seebibyte), and the NIHR Oxford Biomedical Research Centre. Publisher Copyright: © 2020, Springer Nature Switzerland AG.

PY - 2020

Y1 - 2020

N2 - In this paper, we consider differentiating operator skill during fetal ultrasound scanning using probe motion tracking. We present a novel convolutional neural network-based deep learning framework to model ultrasound probe motion in order to classify operator skill levels, that is invariant to operators’ personal scanning styles. In this study, probe motion data during routine second-trimester fetal ultrasound scanning was acquired by operators of known experience levels (2 newly-qualified operators and 10 expert operators). The results demonstrate that the proposed model can successfully learn underlying probe motion features that distinguish operator skill levels during routine fetal ultrasound with 95% accuracy.

AB - In this paper, we consider differentiating operator skill during fetal ultrasound scanning using probe motion tracking. We present a novel convolutional neural network-based deep learning framework to model ultrasound probe motion in order to classify operator skill levels, that is invariant to operators’ personal scanning styles. In this study, probe motion data during routine second-trimester fetal ultrasound scanning was acquired by operators of known experience levels (2 newly-qualified operators and 10 expert operators). The results demonstrate that the proposed model can successfully learn underlying probe motion features that distinguish operator skill levels during routine fetal ultrasound with 95% accuracy.

KW - Fetal ultrasound

KW - Operator skill

KW - Probe motion

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U2 - 10.1007/978-3-030-60334-2_18

DO - 10.1007/978-3-030-60334-2_18

M3 - Conference contribution

AN - SCOPUS:85092721386

SN - 9783030603335

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 180

EP - 188

BT - Medical Ultrasound, and Preterm, Perinatal and Paediatric Image Analysis - 1st International Workshop, ASMUS 2020, and 5th International Workshop, PIPPI 2020, Held in Conjunction with MICCAI 2020, Proceedings

A2 - Hu, Yipeng

A2 - Licandro, Roxane

A2 - Noble, J. Alison

A2 - Hutter, Jana

A2 - Melbourne, Andrew

A2 - Aylward, Stephen

A2 - Abaci Turk, Esra

A2 - Torrents Barrena, Jordina

PB - Springer

T2 - 1st International Workshop on Advances in Simplifying Medical UltraSound, ASMUS 2020, and the 5th International Workshop on Perinatal, Preterm and Paediatric Image Analysis, PIPPI 2020, held in conjunction with the 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020

Y2 - 4 October 2020 through 8 October 2020

ER -

Wang Y, Droste R, Jiao J, Sharma H, Drukker L, Papageorghiou AT et al. Differentiating Operator Skill During Routine Fetal Ultrasound Scanning Using Probe Motion Tracking. In Hu Y, Licandro R, Noble JA, Hutter J, Melbourne A, Aylward S, Abaci Turk E, Torrents Barrena J, Torrents Barrena J, editors, Medical Ultrasound, and Preterm, Perinatal and Paediatric Image Analysis - 1st International Workshop, ASMUS 2020, and 5th International Workshop, PIPPI 2020, Held in Conjunction with MICCAI 2020, Proceedings. Springer. 2020. p. 180-188. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-60334-2_18

Differentiating Operator Skill During Routine Fetal Ultrasound Scanning Using Probe Motion Tracking

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this