Prof. Jie Ying Wu Profile

Prof. Jie Ying Wu

Assistant Professor

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Publications (3)

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Proceedings Article | 29 March 2024 Poster + Paper

Eye tracking for tele-robotic surgery: a comparative evaluation of head-worn solutions

Regine Büter, Roger Soberanis-Mukul, Paola Ruiz Puentes, Ahmed Ghazi, Jie Ying Wu, Mathias Unberath

Proceedings Volume 12928, 129281Y (2024) https://doi.org/10.1117/12.3006476

KEYWORDS: Eye tracking, Eye, Glasses, Calibration, Surgery, Error analysis

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Purpose: Metrics derived from eye-gaze-tracking and pupillometry show promise for cognitive load assessment, potentially enhancing training and patient safety through user-specific feedback in tele-robotic surgery. However, current eye-tracking solutions’ effectiveness in tele-robotic surgery is uncertain compared to everyday situations due to close-range interactions causing extreme pupil angles and occlusions. To assess the effectiveness of modern eye-gaze-tracking solutions in tele-robotic surgery, we compare the Tobii Pro 3 Glasses and Pupil Labs Core, evaluating their pupil diameter and gaze stability when integrated with the da Vinci Research Kit (dVRK). Methods: The study protocol includes a nine-point gaze calibration followed by pick-and-place task using the dVRK and is repeated three times. After a final calibration, users view a 3x3 grid of AprilTags, focusing on each marker for 10 seconds, to evaluate gaze stability across dVRK-screen positions with the L2-norm. Different gaze calibrations assess calibration’s temporal deterioration due to head movements. Pupil diameter stability is evaluated using the FFT from the pupil diameter during the pick-and-place tasks. Users perform this routine with both head-worn eye-tracking systems. Results: Data collected from ten users indicate comparable pupil diameter stability. FFTs of pupil diameters show similar amplitudes in high-frequency components. Tobii Glasses show more temporal gaze stability compared to Pupil Labs, though both eye trackers yield a similar 4cm error in gaze estimation without an outdated calibration. Conclusion: Both eye trackers demonstrate similar stability of the pupil diameter and gaze, when the calibration is not outdated, indicating comparable eye-tracking and pupillometry performance in tele-robotic surgery settings.

SPIE Journal Paper | 25 January 2016

Quantitative analysis of ultrasound images for computer-aided diagnosis

Jie Ying Wu, Adam Tuomi, Michael Beland, Joseph Konrad, David Glidden, David Grand, Derek Merck

JMI, Vol. 3, Issue 01, 014501, (January 2016) https://doi.org/10.1117/12.10.1117/1.JMI.3.1.014501

KEYWORDS: Liver, Computer aided diagnosis and therapy, Ultrasonography, Skull, Quantitative analysis, Biopsy, Image filtering, Control systems, Diagnostics, Magnetic resonance imaging

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We propose an adaptable framework for analyzing ultrasound (US) images quantitatively to provide computer-aided diagnosis using machine learning. Our preliminary clinical targets are hepatic steatosis, adenomyosis, and craniosynostosis. For steatosis and adenomyosis, we collected US studies from 288 and 88 patients, respectively, as well as their biopsy or magnetic resonanceconfirmed diagnosis. Radiologists identified a region of interest (ROI) on each image. We filtered the US images for various texture responses and use the pixel intensity distribution within each ROI as feature parameterizations. Our craniosynostosis dataset consisted of 22 CT-confirmed cases and 22 age-matched controls. One physician manually measured the vectors from the center of the skull to the outer cortex at every 10 deg for each image and we used the principal directions as shape features for parameterization. These parameters and the known diagnosis were used to train classifiers. Testing with cross-validation, we obtained 72.74% accuracy and 0.71 area under receiver operating characteristics curve for steatosis (p<0.0001), 77.27% and 0.77 for adenomyosis (p<0.0001), and 88.63% and 0.89 for craniosynostosis (p=0.0006). Our framework is able to detect a variety of diseases with high accuracy. We hope to include it as a routinely available support system in the clinic.

Proceedings Article | 17 March 2015 Paper

Quantitative ultrasound texture analysis for clinical decision making support

Jie Ying Wu, Michael Beland, Joseph Konrad, Adam Tuomi, David Glidden, David Grand, Derek Merck

Proceedings Volume 9419, 94190W (2015) https://doi.org/10.1117/12.2081949

KEYWORDS: Liver, Ultrasonography, Image filtering, Biopsy, Machine learning, Image classification, Diagnostics, Principal component analysis, Control systems, Magnetic resonance imaging

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We propose a general ultrasound (US) texture-analysis and machine-learning framework for detecting the presence of disease that is suitable for clinical application across clinicians, disease types, devices, and operators. Its stages are image selection, image filtering, ROI selection, feature parameterization, and classification. Each stage is modular and can be replaced with alternate methods. Thus, this framework is adaptable to a wide range of tasks.

Our two preliminary clinical targets are hepatic steatosis and adenomyosis diagnosis. For steatosis, we collected US images from 288 patients and their pathology-determined values of steatosis (%) from biopsies. Two radiologists independently reviewed all images and identified the region of interest (ROI) most representative of the hepatic echotexture for each patient.

To parameterize the images into comparable quantities, we filter the US images at multiple scales for various texture responses. For each response, we collect a histogram of pixel features within the ROI, and parameterize it as a Gaussian function using its mean, standard deviation, kurtosis, and skew to create a 36-feature vector. Our algorithm uses a support vector machine (SVM) for classification. Using a threshold of 10%, we achieved 72.81% overall accuracy, 76.18% sensitivity, and 65.96% specificity in identifying steatosis with leave-ten-out cross-validation (p<0.0001).

Extending this framework to adenomyosis, we identified 38 patients with MR-confirmed findings of adenomyosis and previous US studies and 50 controls. A single rater picked the best US-image and ROI for each case. Using the same processing pipeline, we obtained 76.14% accuracy, 86.00% sensitivity, and 63.16% specificity with leave-one-out cross-validation (p<0.0001).

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