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

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 oper...

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Bibliographic Details
Published inMedical Ultrasound, and Preterm, Perinatal and Paediatric Image Analysis Vol. 12437; pp. 180 - 188
Main Authors Wang, Yipei, Droste, Richard, Jiao, Jianbo, Sharma, Harshita, Drukker, Lior, Papageorghiou, Aris T., Noble, J. Alison
Format Book Chapter Journal Article
LanguageEnglish
Published Switzerland Springer International Publishing AG 01.01.2020
Springer International Publishing
SeriesLecture Notes in Computer Science
Subjects
Fetal ultrasound
Operator skill
Probe motion
Probe motion
Fetal ultrasound
Operator skill
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Summary: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.
ISBN:9783030603335
3030603334
ISSN:0302-9743
1611-3349
DOI:10.1007/978-3-030-60334-2_18