@article {Connolly2022c,
	title = {Bridging 3D Slicer and ROS2 for Image-Guided Robotic Interventions},
	journal = {Sensors},
	volume = {22},
	year = {2022},
	month = {07/2022},
	doi = {https://doi.org/10.3390/s22145336},
	author = {Laura Connolly and Anton Degeut and Simon Leonard and Junichi Tokuda and Tamas Ungi and Axel Krieger and Peter Kazanzides and Parvin Mousavi and Gabor Fichtinger and Russell H. Taylor}
}
@conference {Connolly2022,
	title = {Feasibility of combined optical and acoustic imaging for surgical cavity scanning},
	booktitle = {SPIE Medical Imaging 2022: Image-Guided Procedures, Robotic Interventions, and Modeling},
	volume = {12034},
	year = {2022},
	month = {04/2022},
	address = {San Diego (online)},
	abstract = {
PURPOSE: Over 30\% of breast conserving surgery patients must undergo repeat surgery to address incomplete tumor resection. We hypothesize that the addition of a robotic cavity scanning system can improve the success rates of these procedures by performing additional, intraoperative imaging to detect left-over cancer cells. In this study, we assess the feasibility of a combined optical and acoustic imaging approach for this cavity scanning system. METHODS: Dual-layer tissue phantoms are imaged with both throughput broadband spectroscopy and an endocavity ultrasound probe. The absorbance and transmittance of the incident light from the broadband source is used to characterize each tissue sample optically. Additionally, a temporally enhanced ultrasound approach is used to distinguish the heterogeneity of the tissue sample by classifying individual pixels in the ultrasound image with a support vector machine. The goal of this combined approach is to use optical characterization to classify the tissue surface, and acoustic characterization to classify the sample heterogeneity. RESULTS: Both optical and acoustic characterization demonstrated promising preliminary results. The class of each tissue sample is distinctly separable based on the transmittance and absorption of the broadband light. Additionally, an SVM trained on the temporally enhance ultrasound signals for each tissue type, showed 82\% linear separability of labelled temporally enhanced ultrasound sequences in our test set. CONCLUSIONS: By combining broadband and ultrasound imaging, we demonstrate a potential non-destructive imaging approach for this robotic cavity scanning system. With this approach, our system can detect both surface level tissue characteristics and depth information. Applying this to breast conserving surgery can help inform the surgeon about the tissue composition of the resection cavity after initial tumor resection.},
	doi = {https://doi.org/10.1117/12.2611964},
	author = {Laura Connolly and Amoon Jamzad and Arash Nikniazi and Rana Poushimin and Jean Michel Nunzi and John Rudan and Gabor Fichtinger and Parvin Mousavi}
}
@conference {connolly2022b,
	title = {An open-source testbed for developing image-guided robotic tumor-bed inspection},
	booktitle = {Imaging Network of Ontario (ImNO) Symposium},
	year = {2022},
	author = {Laura Connolly and Amoon Jamzad and Arash Nikniazi and Rana Poushimin and Andras Lasso and Kyle R. Sunderland and Tamas Ungi and Jean Michel Nunzi and John Rudan and Gabor Fichtinger and Parvin Mousavi}
}
@article {Fooladgar2022,
	title = {Uncertainty estimation for margin detection in cancer surgery using mass spectrometry},
	journal = {International Journal of Computer Assisted Radiology and Surgery},
	year = {2022},
	month = {09/2022},
	type = {Journal},
	doi = {https://doi.org/10.1007/s11548-022-02764-3},
	author = {Fahimeh Fooladgar and Amoon Jamzad and Laura Connolly and Alice Santilli and Martin Kaufmann and Kevin Ren and Purang Abolmaesumi and John Rudan and Doug McKay and Gabor Fichtinger and Parvin Mousavi}
}
@article {Connolly2021c,
	title = {Combined Mass Spectrometry and Histopathology Imaging for Perioperative Tissue Assessment in Cancer Surgery},
	journal = {Journal of Imaging},
	volume = {7},
	number = {203},
	year = {2021},
	month = {10/2021},
	type = {Article},
	abstract = {<p><span style="color:rgb(34, 34, 34); font-family:arial; font-size:12px">Mass spectrometry is an effective imaging tool for evaluating biological tissue to detect cancer. With the assistance of deep learning, this technology can be used as a perioperative tissue assessment tool that will facilitate informed surgical decisions. To achieve such a system requires the development of a database of mass spectrometry signals and their corresponding pathology labels. Assigning correct labels, in turn, necessitates precise spatial registration of histopathology and mass spectrometry data. This is a challenging task due to the domain differences and noisy nature of images. In this study, we create a registration framework for mass spectrometry and pathology images as a contribution to the development of perioperative tissue assessment. In doing so, we explore two opportunities in deep learning for medical image registration, namely, unsupervised, multi-modal deformable image registration and evaluation of the registration. We test this system on prostate needle biopsy cores that were imaged with desorption electrospray ionization mass spectrometry (DESI) and show that we can successfully register DESI and histology images to achieve accurate alignment and, consequently, labelling for future training. This automation is expected to improve the efficiency and development of a deep learning architecture that will benefit the use of mass spectrometry imaging for cancer diagnosis.</span></p>
},
	doi = {https://doi.org/10.3390/jimaging7100203},
	author = {Laura Connolly and Amoon Jamzad and Martin Kaufmann and Catriona E. Farquharson and Kevin Ren and John Rudan and Gabor Fichtinger and Parvin Mousavi}
}
@conference {Connolly2021,
	title = {An open-source platform for cooperative semi-autonomous robotic surgery},
	booktitle = {IEEE International Conference on Autonomous Systems},
	year = {2021},
	month = {10/2021},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Montreal, Quebec},
	doi = {https://doi.org/10.1109/ICAS49788.2021.9551149},
	author = {Laura Connolly and Anton Degeut and Kyle R. Sunderland and Andras Lasso and Tamas Ungi and John Rudan and Russell H. Taylor and Parvin Mousavi and Gabor Fichtinger}
}
@article {Connolly2021b,
	title = {A platform for robot-assisted Intraoperative imaging in breast conserving surgery},
	year = {2021},
	month = {2021},
	publisher = {Imaging Network of Ontario Symposium},
	address = {Online},
	author = {Laura Connolly and Kyle R. Sunderland and Andras Lasso and Anton Degeut and Tamas Ungi and John Rudan and Russell H. Taylor and Parvin Mousavi and Gabor Fichtinger}
}
@conference {Connolly2020a,
	title = {Classification of tumor signatures from electrosurgical vapors using mass spectrometry and machine learning: a feasibility study},
	booktitle = {Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions and Modeling},
	volume = {11315},
	year = {2020},
	month = {2020},
	publisher = {SPIE},
	organization = {SPIE},
	address = {Houston, Texas, United States},
	doi = {https://doi.org/10.1117/12.2549343},
	author = {Laura Connolly and Amoon Jamzad and Martin Kaufmann and Rachel Rubino and Alireza Sedghi and Tamas Ungi and Mark Asselin and Scott Yam and John Rudan and Christopher Nicol and Gabor Fichtinger and Parvin Mousavi}
}
@conference {Connolly2019a,
	title = {Mechanically-Controlled Spectroscopic Imaging for Tissue Classification},
	booktitle = {SPIE Medical Imaging 2019:  Image-Guided Procedures, Robotic Interventions, and Modeling},
	volume = {10951},
	year = {2019},
	month = {03/2019},
	address = {San Diego, California},
	doi = {https://doi.org/10.1117/12.2512481},
	author = {Laura Connolly and Tamas Ungi and Andras Lasso and Thomas Vaughan and Mark Asselin and Parvin Mousavi and Scott Yam and Gabor Fichtinger}
}
