Publications

@article{Farahmand2021,

title = {Channel Quality Prediction for TSCH Blacklisting in Highly Dynamic Networks: A Self-Supervised Deep Learning Approach},

author = {Mohammad Farahmand and Majid Nabi},

doi = {10.1109/jsen.2021.3093424},

issn = {1558-1748},

year  = {2021},

date = {2021-09-15},

urldate = {2021-09-15},

journal = {IEEE Sensors J.},

volume = {21},

number = {18},

pages = {21059--21068},

publisher = {Institute of Electrical and Electronics Engineers (IEEE)},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Fichtinger2021,

title = {Design of an Ultrasound-Navigated Prostate Cancer Biopsy System for Nationwide Implementation in Senegal},

author = {Gabor Fichtinger and Parvin Mousavi and Tamas Ungi and Aaron Fenster and Purang Abolmaesumi and Gernot Kronreif and Juan Ruiz-Alzola and Alain Ndoye and Babacar Diao and Ron Kikinis},

url = {https://www.mdpi.com/2313-433X/7/8/154},

doi = {10.3390/jimaging7080154},

issn = {2313-433X},

year  = {2021},

date = {2021-08-01},

urldate = {2021-08-01},

journal = {Journal of Imaging},

volume = {7},

number = {8},

pages = {154},

abstract = {<p>This paper presents the design of NaviPBx, an ultrasound-navigated prostate cancer biopsy system. NaviPBx is designed to support an affordable and sustainable national healthcare program in Senegal. It uses spatiotemporal navigation and multiparametric transrectal ultrasound to guide biopsies. NaviPBx integrates concepts and methods that have been independently validated previously in clinical feasibility studies and deploys them together in a practical prostate cancer biopsy system. NaviPBx is based entirely on free open-source software and will be shared as a free open-source program with no restriction on its use. NaviPBx is set to be deployed and sustained nationwide through the Senegalese Military Health Service. This paper reports on the results of the design process of NaviPBx. Our approach concentrates on “frugal technology”, intended to be affordable for low–middle income (LMIC) countries. Our project promises the wide-scale application of prostate biopsy and will foster time-efficient development and programmatic implementation of ultrasound-guided diagnostic and therapeutic interventions in Senegal and beyond.</p>},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{NasuteFauerbach2021,

title = {Lack of definitive presurgical pathological diagnosis is associated with inadequate surgical margins in breast-conserving surgery},

author = {Paola V. Nasute Fauerbach and Kathrin Tyryshkin and Silvia Perez Rodrigo and John Rudan and Gabor Fichtinger and Michael Reedijk and Sonal Varma and David M. Berman},

url = {https://www.sciencedirect.com/science/article/pii/S0748798321005424},

doi = {https://doi.org/10.1016/j.ejso.2021.05.047},

issn = {0748-7983},

year  = {2021},

date = {2021-06-01},

urldate = {2021-06-01},

journal = {European Journal of Surgical Oncology},

abstract = {<p>Purpose To determine the impact of definitive presurgical diagnosis on surgical margins in breast-conserving surgery (BCS) for primary carcinomas; clinicopathological features were also analyzed. Methods This retrospective study included women who underwent BCS for primary carcinomas in 2016 and 2017. Definitive presurgical diagnosis was defined as having a presurgical core needle biopsy (CNB) and not being upstaged between biopsy and surgery. Biopsy data and imaging findings including breast density were retrieved. Inadequate surgical margins (IM) were defined per latest ASCO and ASTRO guidelines. Univariable and multivariable analyses were performed. Results 360 women (median age, 66) met inclusion criteria with 1 having 2 cancers. 82.5% (298/361) were invasive cancers while 17.5% (63/361) were ductal carcinoma in situ (DCIS). Most biopsies were US-guided (284/346, 82.0%), followed by mammographic (60/346, 17.3%), and MRI-guided (2/346, 0.6%). US and mammographic CNB yielded median samples of 2 and 4, respectively, with a 14G needle. 15 patients (4.2%) lacked presurgical CNB. The IM rate was 30.0%. In multivariable analysis, large invasive cancers (>20 mm), dense breasts, and DCIS were associated with IM (p = 0.029, p = 0.010, and p = 0.013, respectively). Most importantly, lack of definitive presurgical diagnosis was a risk factor for IM (OR, 2.35; 95% CI: 1.23–4.51, p = 0.010). In contrast, neither patient age (<50) nor aggressive features (e.g., LVI) were associated with IM. Conclusion Lack of a definitive presurgical diagnosis was associated with a two-fold increase of IM in BCS; other risk factors were dense breasts, large invasive cancers, and DCIS.</p>},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Gauvin2019,

title = {Real-time electromagnetic navigation for breast-conserving surgery using NaviKnife technology: A matched case-control study},

author = {Gabrielle Gauvin and Caitlin T Yeo and Tamas Ungi and Shaila Merchant and Andras Lasso and Doris Jabs and Thomas Vaughan and John Rudan and Ross Walker and Gabor Fichtinger and C. Jay Engel},

doi = {10.1111/tbj.13480},

year  = {2020},

date = {2020-09-01},

urldate = {2020-09-01},

journal = {The Breast Journal},

volume = {26},

number = {3},

pages = {399-405},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Pinter2020a,

title = {Using Fuzzy Logics to Determine Optimal Oversampling Factor for Voxelizing 3D Surfaces in Radiation Therapy},

author = {Csaba Pinter and Tim Olding and L. John Schreiner and Gabor Fichtinger},

url = {https://link.springer.com/article/10.1007/s00500-020-05126-w

https://labs.cs.queensu.ca/perklab/wp-content/uploads/sites/3/2024/03/Pinter2020a_0.pdf},

doi = {10.1007/s00500-020-05126-w},

year  = {2020},

date = {2020-06-01},

urldate = {2020-06-01},

journal = {Soft Computing},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Pinter2020,

title = {SlicerVR for Medical Intervention Training and Planning in Immersive Virtual Reality},

author = {Csaba Pinter and Andras Lasso and Saleh Choueib and Mark Asselin and Jean-ChristopheC. Fillion-Robin and Jean-Baptiste Vimort and Ken Martin and MatthewA Jolley and Gabor Fichtinger},

url = {https://labs.cs.queensu.ca/perklab/wp-content/uploads/sites/3/2024/03/Pinter2020a_0.pdf},

doi = {10.1109/TMRB.2020.2983199},

year  = {2020},

date = {2020-03-01},

urldate = {2020-03-01},

journal = {IEEE Transactions on Medical Robotics and Bionics},

volume = {2},

number = {2},

pages = {108-117},

abstract = {<p>Virtual reality (VR) provides immersive visualization that has proved to be useful in a variety of medical applications. Currently, however, no free open-source software platform exists that would provide comprehensive support for translational clinical researchers in prototyping experimental VR scenarios in training, planning or guiding medical interventions. By integrating VR functions in 3D Slicer, an established medical image analysis and visualization platform, SlicerVR enables virtual reality experience by a single click. It provides functions to navigate and manipulate the virtual scene, as well as various settings to abate the feeling of motion sickness. SlicerVR allows for shared collaborative VR experience both locally and remotely. We present illustrative scenarios created with SlicerVR in a wide spectrum of applications, including echocardiography, neurosurgery, spine surgery, brachytherapy, intervention training and personalized patient education. SlicerVR is freely available under BSD type license as an extension to 3D Slicer and it has been downloaded over 7,800 times at the time of writing this article.</p>},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Ungi2020,

title = {Automatic spine ultrasound segmentation for scoliosis visualization and measurement},

author = {Tamas Ungi and Hastings Greer and Kyle R. Sunderland and Victoria Wu and Zachary M C Baum and Christopher Schlenger and Matthew Oetgen and Kevin Cleary and Stephen Aylward and Gabor Fichtinger},

url = {https://ieeexplore.ieee.org/document/9034149

https://labs.cs.queensu.ca/perklab/wp-content/uploads/sites/3/2024/02/Ungi2020.pdf},

doi = {10.1109/TBME.2020.2980540},

year  = {2020},

date = {2020-03-01},

urldate = {2020-03-01},

journal = {IEEE Transactions on Biomedical Engineering},

volume = {67},

number = {11},

pages = {3234 - 3241},

abstract = {<p>\emph{Objective:} Integrate tracked ultrasound and AI methods to provide a safer and more accessible alternative to X-ray for scoliosis measurement. We propose automatic ultrasound segmentation for 3-dimensional spine visualization and scoliosis measurement to address difficulties in using ultrasound for spine imaging. \emph{Methods:} We trained a convolutional neural network for spine segmentation on ultrasound scans using data from eight healthy adult volunteers. We tested the trained network on eight pediatric patients. We evaluated image segmentation and 3-dimensional volume reconstruction for scoliosis measurement. \emph{Results:} As expected, fuzzy segmentation metrics reduced when trained networks were translated from healthy volunteers to patients. Recall decreased from 0.72 to 0.64 (8.2% decrease), and precision from 0.31 to 0.27 (3.7% decrease). However, after finding optimal thresholds for prediction maps, binary segmentation metrics performed better on patient data. Recall decreased from 0.98 to 0.97 (1.6% decrease), and precision from 0.10 to 0.06 (4.5% decrease). Segmentation prediction maps were reconstructed to 3-dimensional volumes and scoliosis was measured in all patients. Measurement in these reconstructions took less than 1 minute and had a maximum error of 2.2° compared to X-ray. \emph{Conclusion:} automatic spine segmentation makes scoliosis measurement both efficient and accurate in tracked ultrasound scans. \emph{Significance:} Automatic segmentation may overcome the limitations of tracked ultrasound that so far prevented its use as an alternative of X-ray in scoliosis measurement.</p>},

keywords = {},

pubstate = {published},

tppubtype = {article}

}