Visualising patterns in clinicians' eye movements while interpreting fetal ultrasound imaging videos is challenging. Across and within videos, there are differences in size and position of Areas-of-Interest (AOIs) due to fetal position, movement and …
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 …
Deep networks have been shown to achieve impressive accuracy for some medical image analysis tasks where large datasets and annotations are available. However, tasks involving learning over new sets of classes arriving over extended time is a …
Ultrasound is the primary modality for obstetric imaging and is highly sonographer dependent. Long training period, insufficient recruitment and poor retention of sonographers are among the global challenges in the expansion of ultrasound use. For …
Introduction Pupillometry, the measurement of eye pupil diameter, is a well-established and objective modality correlated with cognitive workload. In this paper, we analyse the pupillary response of ultrasound imaging operators to assess their …
This paper presents a novel multi-modal learning approach for automated skill characterization of obstetric ultrasound operators using heterogeneous spatio-temporal sensory cues, namely, scan video, eye-tracking data, and pupillometric data, acquired …
BibTex @article{doi:10.1002/uog.22266, author = {Sharma, H. and Drukker, L. and Droste, R. and Chatelain, P. and Papageorghiou, A.T. and Noble, J.A.}, title = {OC10.02: Task-evoked pupillary response as an index of cognitive workload of sonologists undertaking fetal ultrasound}, journal = {Ultrasound in Obstetrics \& Gynecology}, volume = {56}, number = {S1}, pages = {28-28}, doi = {10.1002/uog.22266}, url = {https://obgyn.onlinelibrary.wiley.com/doi/abs/10.1002/uog.22266}, eprint = {https://obgyn.onlinelibrary.wiley.com/doi/pdf/10.1002/uog.22266}, year = {2020} }
We present a novel automated approach for detection of standardized abdominal circumference (AC) planes in fetal ultrasound built in a convolutional neural network (CNN) framework, called SonoEyeNet, that utilizes eye movement data of a sonographer …