Category Archives: Graduation

Congratulations Dr. Tanskanen!

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Congratulations to Dr. Adrian Tanskanen who successfully defended his PhD thesis!

Adrian’s work ‘Leveraging Multipath Effects in Multimodal Optical Coherence Tomography for Cancer Detection‘ described methods to characterize, mitigate and leverage multipath artifacts inherent to OCT systems using double-clad fiber.

Select Publications:

Congratulations Mehar & Allan!

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Attenuation coefficient of normal (left) and pathologic (right) oral tissue.

A segmented airway and example cross-sections (segmentations in green).

Congratulations to Mehar and Allan who successfully defended their undergraduate honours theses this fall!

Mehar’s work ‘Methods for the Estimation of Depth-resolved Attenuation in OCT‘ compared different approaches for calculating the attenuation coefficient from OCT data. This allows us to characterize optical properties of tissue as they change with respect to depth.

Allan developed an automated tool to segment the tissue surface in OCT of the small airways of the lung. This tool allow us to quantify image features in the small airways without requiring manual segmentation. You can read his thesis, ‘Lumen Segmentation in Endobronchial Optical Coherence Tomography with Deep Learning‘ online.

Congratulations Chloe!

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Chloe successfully defended her Master’s thesis ‘Segmentation of Oral Optical Coherence Tomography with Deep Learning‘ with no revisions, the highest level of pass at Simon Fraser University’s School of Engineering Science.

Chloe developed a four-part neural network pipeline to aid OCT interpretation by providing en face maps of epithelial depth. This allows for rapid identification of the most pathologic region in endoscopic OCT of the oral cavity, which may help identify the best site for biopsy. Chloe’s pipeline demonstrates as-good-as or better agreement than manual assessment between two raters, suggesting strong performance. Further work includes validating this tool on data acquired with a different OCT system to test generalizability.