ArticleSimplifying medical ultrasound : second international workshop, ASMUS 2021 : held in conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021 : proceedings. ASMUS (Workshop) (2nd : 2021 : Online)2021
Towards Scale and Position Invariant Task Classification using Normalised Visual Scanpaths in Clinical Fetal Ultrasound.
Article in Simplifying medical ultrasound : second international workshop, ASMUS 2021 : held in conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021 : proceedings. ASMUS (Workshop) (2nd : 2021 : Online), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- The Use of Machine Learning in Eye Tracking Studies in Medical Imaging: A Review.IEEE journal of biomedical and health informatics · 2024Review
- Gaze-probe joint guidance with multi-task learning in obstetric ultrasound scanning.Medical image analysis · 2023Article
- Skill, or Style? Classification of Fetal Sonography Eye-Tracking Data.Proceedings of machine learning research · 2023Article
- Multimodal-GuideNet: Gaze-Probe Bidirectional Guidance in Obstetric Ultrasound Scanning.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2022Article
- Skill Characterisation of Sonographer Gaze Patterns during Second Trimester Clinical Fetal Ultrasounds using Time Curves.Proceedings. Eye Tracking Research & Applications Symposium · 2022Article
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Authors and funding
5 authors.
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Abstract
We present a method for classifying tasks in fetal ultrasound scans using the eye-tracking data of sonographers. The visual attention of a sonographer captured by eye-tracking data over time is defined by a scanpath. In routine fetal ultrasound, the captured standard imaging planes are visually inconsistent due to fetal position, movements, and sonographer scanning experience. To address this challenge, we propose a scale and position invariant task classification method using normalised visual scanpaths. We describe a normalisation method that uses bounding boxes to provide the gaze with a reference to the position and scale of the imaging plane and use the normalised scanpath sequences to train machine learning models for discriminating between ultrasound tasks. We compare the proposed method to existing work considering raw eyetracking data. The best performing model achieves the F1-score of 84% and outperforms existing models.
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Registered trials
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