connolly | The Perk Lab

Name

Laura Connolly

Short Biography

Laura is a Ph.D student working under the supervision of Dr. Gabor Fichtinger, Dr. Parvin Mousavi, and Dr. Russell H. Taylor. She has been a member of the Perk Lab and Medical informatics (Med-i) lab at Queen’s University since May of 2018. Laura’s thesis work is focused on the development of robotic tools to assist in breast conserving surgery.

Affiliation

Institution: Queen's University

Department: Electrical Engineering

Designation: Graduate student (PhD)

Lab membership period: Since May 2018

Publications

2022
Fooladgar, F., Jamzad A., Connolly L., Santilli A., Kaufmann M., Ren K., et al. (2022). Uncertainty estimation for margin detection in cancer surgery using mass spectrometry. International Journal of Computer Assisted Radiology and Surgery. DOI Google Scholar BibTeX
Connolly, L., Degeut A., Leonard S., Tokuda J., Ungi T., Krieger A., et al. (2022). Bridging 3D Slicer and ROS2 for Image-Guided Robotic Interventions. Sensors. 22, DOI Google Scholar BibTeX
Connolly, L., Jamzad A., Nikniazi A., Poushimin R., Nunzi J. Michel, Rudan J., et al. (2022). Feasibility of combined optical and acoustic imaging for surgical cavity scanning. SPIE Medical Imaging 2022: Image-Guided Procedures, Robotic Interventions, and Modeling. 12034(120341H), DOI Google Scholar BibTeX
Connolly, L., Jamzad A., Nikniazi A., Poushimin R., Lasso A., Sunderland K. R., et al. (2022). An open-source testbed for developing image-guided robotic tumor-bed inspection. Imaging Network of Ontario (ImNO) Symposium. Google Scholar BibTeX Connolly2022b.pdf (838.81 KB)

2021
Connolly, L., Sunderland K. R., Lasso A., Degeut A., Ungi T., Rudan J., et al. (2021). A platform for robot-assisted Intraoperative imaging in breast conserving surgery. Imaging Network of Ontario Symposium . Google Scholar BibTeX Connolly2021a.pdf (773.23 KB)
Connolly, L., Degeut A., Sunderland K. R., Lasso A., Ungi T., Rudan J., et al. (2021). An open-source platform for cooperative semi-autonomous robotic surgery. IEEE International Conference on Autonomous Systems. DOI Google Scholar BibTeX
Connolly, L., Jamzad A., Kaufmann M., Farquharson C. E., Ren K., Rudan J., et al. (2021). Combined Mass Spectrometry and Histopathology Imaging for Perioperative Tissue Assessment in Cancer Surgery. Journal of Imaging. 7, DOI Google Scholar BibTeX

2020
Connolly, L., Jamzad A., Kaufmann M., Rubino R., Sedghi A., Ungi T., et al. (2020). Classification of tumor signatures from electrosurgical vapors using mass spectrometry and machine learning: a feasibility study. Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions and Modeling. 11315, DOI Google Scholar BibTeX Connolly2020a.pdf (823.25 KB)

2019
Connolly, L., Ungi T., Lasso A., Vaughan T., Asselin M., Mousavi P., et al. (2019). Mechanically-Controlled Spectroscopic Imaging for Tissue Classification. SPIE Medical Imaging 2019: Image-Guided Procedures, Robotic Interventions, and Modeling. 10951, DOI Google Scholar BibTeX Connolly2019a.pdf (877.7 KB)

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