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SonoEyeNet: Standardized Fetal Ultrasound Plane Detection Informed by Eye Tracking.

Y Cai1, H Sharma1, P Chatelain1

  • 1Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, UK.

Proceedings. IEEE International Symposium on Biomedical Imaging
|April 12, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces SonoEyeNet, an AI tool using sonographer eye movements to improve automated fetal abdominal circumference (AC) plane detection in ultrasounds. This approach significantly enhances detection accuracy compared to methods without eye-tracking data.

Keywords:
eye trackingfetal ultrasoundinformation fusionstandardized plane detectiontransfer learning

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Fetal Ultrasound

Background:

  • Accurate measurement of fetal abdominal circumference (AC) is crucial for assessing fetal growth and well-being.
  • Automated detection of standardized AC planes in ultrasound images can improve efficiency and reduce inter-observer variability.
  • Current automated methods may lack the nuanced understanding of anatomical landmarks that experienced sonographers possess.

Purpose of the Study:

  • To develop and evaluate SonoEyeNet, a novel automated approach for detecting standardized abdominal circumference (AC) planes in fetal ultrasound.
  • To investigate the utility of incorporating sonographer eye movement data into a convolutional neural network (CNN) framework for improved interpretation.
  • To quantify the performance enhancement in AC plane detection accuracy using human visual fixation information.

Main Methods:

  • Development of SonoEyeNet, a CNN-based framework integrating fetal ultrasound images with visual heatmaps derived from sonographer eye movement data.
  • Collection of eye movement data from experienced sonographers identifying AC planes in ultrasound video clips.
  • Investigation of various methods for processing visual heatmaps and fusing them with image feature maps within the CNN.

Main Results:

  • SonoEyeNet achieved significantly improved AC plane detection performance when utilizing sonographer eye fixation information.
  • Precision, recall, and F1-score reached 96.5%, 99.0%, and 97.8% respectively with eye-tracking assistance.
  • Performance without eye fixation information was considerably lower, with precision, recall, and F1-score at 73.6%, 74.1%, and 73.8%.

Conclusions:

  • Incorporating human visual fixation data into CNN models substantially enhances the accuracy of automated fetal AC plane detection.
  • SonoEyeNet represents a promising advancement in AI-assisted medical imaging, leveraging expert human visual behavior for improved diagnostic tools.
  • The findings suggest that integrating eye-tracking data can bridge the gap between automated systems and expert human interpretation in medical ultrasound.