Thesis etd-05152018-105709 |
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Thesis type
Tesi di laurea magistrale
URN
etd-05152018-105709
Thesis title
Eye Tracking for Proton Clinic Environment - Development of a High Accuracy Eye Tracking Device for Uveal Melanoma Proton Therapy
Department
INGEGNERIA DELL'INFORMAZIONE
Course of study
INGEGNERIA ROBOTICA E DELL'AUTOMAZIONE
Supervisors
.
relatore Prof.ssa Pallottino, Lucia
Keywords
- computer vision
- eye tracking
- gaze estimation
- high accuracy
- proton clinic
- proton therapy
- regression
- uveal melanoma
- vision
Graduation session start date
19/07/2018
Availability
Withheld
Release date
19/07/2088
Abstract (Inglese)
Abstract (Italiano)
Uveal Melanoma is the most common intraocular tumor in humans, and one of the most promising treatments available is proton therapy. Proton clinics use specific devices for high energy proton beams forming and delivery, used to damage selectively the tumoral cells, saving the healthy part of the eye and maintaining as much vision as possible.
Accurate energy dose delivery is critical for this kind of treatment, and position and rotation of the eye must be measured with high accuracy, in order to determine the position and orientation of the tumor in real-time. This is achievable with eye tracking.
Aim of this thesis is the design and validation of a non-invasive eye tracker suitable for proton clinic environment, with accuracy of 0.5° in the worst case. For this purpose a feature-based video-oculography eye tracker with IR active illumination was studied, simulated and implemented. In addition, a custom pupil and glint detection algorithm has been developed, along with a Kalman filter. A mapping procedure and a regression strategy has also been developed and implemented.
Results of a single mapping on 5 volunteers show accuracy above the 0.5° bound, with great differences among the different tests. Results are below the required accuracy only if the mapping is repeated several times, and then averaged.
Accurate energy dose delivery is critical for this kind of treatment, and position and rotation of the eye must be measured with high accuracy, in order to determine the position and orientation of the tumor in real-time. This is achievable with eye tracking.
Aim of this thesis is the design and validation of a non-invasive eye tracker suitable for proton clinic environment, with accuracy of 0.5° in the worst case. For this purpose a feature-based video-oculography eye tracker with IR active illumination was studied, simulated and implemented. In addition, a custom pupil and glint detection algorithm has been developed, along with a Kalman filter. A mapping procedure and a regression strategy has also been developed and implemented.
Results of a single mapping on 5 volunteers show accuracy above the 0.5° bound, with great differences among the different tests. Results are below the required accuracy only if the mapping is repeated several times, and then averaged.
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