Rebecca Hisey

@article{ordaz2025b,

title = {C-CASE 2024: Surgical Education Through Innovation: Canadian Conference for the Advancement of Surgical Education, Oct. 17-18, 2024, Toronto, Ontario},

author = {Daniel Josué Guerra Ordaz and Magdalena Cordoba and Éolie Delisle and Rocío Branes and Sophie Nguyen and Waiel Abdulaziz Daghistani and Maryam Mozafarinia and Carlos Cordoba and Jessica Maher and Marisa Dorling and Kirk Haan and Danah Fahad and Alexander Moise and Gizelle Francis and Youssef Omar and Elysia Grose and Timothy Phillips and Alexandra D'Souza and Shaishav Datta and Kyle Wanzel and Retage Al Bader and Clementine Affana and Ashish Kumar and Nancy Posel and David Fleiszer and Emily Lan-Vy Nguyen and Prachikumari Patel and Ahmer Irfan and Jason Aubrey and Taylor M Coe and Hala Muaddi and Roxana Bucur and Nadia Rukavina and Chaya Shwaartz and Khaled Skaik and Wassim Elmasry and Devon Haseltine and Matthew Bilson and Mahmoud Moustafa and Amrit Das and Maryam Wagner and Carlos Gomez-Garibello and Cariane Driad and Xavier Sonesaksith-Turcotte and Émilie Sandman and Lily Trang Huynh and Prevost Jantchou and Marie-Lyne Nault and Jasmine Ng and Jaskarn Dhaliwal and Henna Salim and Ayesha Shakeel and Suffia Malik and Wiley Chung and Lucy Yang and Abdullah Al-Ani and Mohamed Bondok and Helen Chung and Patrick Gooi and Giancarlo Sticca and Joseph Petruccelli and Dominique Dorion and Yousef Abdelkhalek Saber Omar and Kalpesh Hathi and Timothy Philips and Lalenthra Naidoo and Xin Yu Yang and Gabrielle Massé and Jean-François Tremblay and Franck Vandenbroucke-Menu and Mai-Kim Gervais and Julien Letendre and Hugues Jeanmart and Ariane Lacaille-Ranger and Farbod Niazi and Abrar Ahmed and Zeel Patel and Saman Arfaie and Crystal Ma and Gregory Mikerov and Jack Legler and Emily Steinberg and Elie Fadel and Liam Murad and Julia Biris and Charles Desgagné and Justine Colivas and Brandon Noyon and Adam Dubrowski and Érica Patocskai and Jason Kreutz and Donald McPhalen and Claire Temple-Oberle and Sonaina Chopra and Jasmin Dhanoa and Jason M Harley and Anita Acai and Amy Keuhl and Quang Ngo and Jonathan Sherbino and Ereny Bassilious and Elif Bilgic and Anushka Pradhan and Emily Volfson and Zackary Tsang and Megan Mak and Mojgan Hodaie and Denesh Peramakumar and Rebecca Hisey and Elizabeth Klosa and Aden Wong and Farah Zaza and Gabor Fichtinger and Boris Zevin and James Lisondra and Remi Gao and Albert Fung and Alicia Belaiche and Johanie Victoria Piché and Adam Hocini and Myriam Belaiche and Louise McNaughton-Filion and Constance Bouthillier and Tomas Cordoba and Charlotte McEwen and Iqbal Jaffer and Faizan Amin and Jeffrey Barsuk and William McGaghie and Matthew Sibbald and Edgar Akuffo-Addo and Jaycie Dalson and Kwame Agyei and Samiha Mohsen and Safia Yusuf and Clara Juando-Prats and Jory Simpson and Gursharan Sohi and Bianca Giglio and Vanja Davidovic and Recai Yilmaz and Abdulmajeed Albeloushi and Mohamed Alhantoobi and Abicumaran Uthamacumaran and Jason Lapointe and Ahmad Alhaj and Rothaina Saeedi and Trisha Tee and Rolando Del Maestro and Victoria Tran},

year  = {2025},

date = {2025-01-01},

journal = {Canadian journal of surgery. Journal canadien de chirurgie},

volume = {68},

number = {1suppl1},

pages = {S1-S13},

abstract = {C-CASE 2024: Surgical Education Through Innovation: Canadian Conference for the Advancement of Surgical Education, Oct. 17-18, 2024, Toronto, Ontario C-CASE 2024: Surgical Education Through Innovation: Canadian Conference for the Advancement of Surgical Education, Oct. 17-18, 2024, Toronto, Ontario Can J Surg. 2025 Feb 6;68(1suppl1):S1-S13. doi: 10.1503/cjs.000225. Print 2025 Jan-Feb. Authors Daniel Josué Guerra Ordaz 1 , Magdalena Cordoba 1 , Éolie Delisle 1 , Rocío Branes 1 , Sophie Nguyen 1 , Waiel Abdulaziz Daghistani 1 , Maryam Mozafarinia 1 , Carlos Cordoba 1 , Jessica Maher 2 , Marisa Dorling 2 , Kirk Haan 2 , Danah Fahad 2 , Alexander Moise 2 , Gizelle Francis 2 , Youssef Omar 2 , Elysia Grose 2 , Timothy Phillips 2 , Alexandra D'Souza 3 , Shaishav Datta 3 , Kyle Wanzel 3 , Retage Al Bader 4 , Clementine Affana 4 , Ashish Kumar 4 , Nancy Posel 5 , David Fleiszer 5 , Emily Lan-Vy …},

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@article{ordaz2025,

title = {C-CASE 2024: Surgical Education Through Innovation01. A 25-year retrospective of Canadian plastic surgery research and its influence: a thorough bibliometric study02 …},

author = {Daniel Josué Guerra Ordaz and Jessica Maher and Alexandra D’Souza and Retage Al Bader and Nancy Posel and Emily Lan-Vy Nguyen and Khaled Skaik and Cariane Driad and Jasmine Ng and Lucy Yang and Giancarlo Sticca and Gizelle Francis and Xin Yu Yang and Farbod Niazi and Joseph Petruccelli and Gregory Mikerov and Justine Colivas and Jason Kreutz and Sonaina Chopra and Anushka Pradhan and Emily Volfson and Denesh Peramakumar and Prachikumari Patel and Alicia Belaiche and Constance Bouthillier and Charlotte McEwen and Edgar Akuffo-Addo and Gursharan Sohi and Bianca Giglio and Victoria Tran and Megan Skakum and Rachael Allen and Amrit Das and Alyson McKenna and Magdalena Cordoba and Éolie Delisle and Rocío Branes and Sophie Nguyen and Waiel Abdulaziz Daghistani and Maryam Mozafarinia and Carlos Cordoba and Marisa Dorling and Kirk Haan and Danah Fahad and Alexander Moise and Youssef Omar and Elysia Grose and Timothy Phillips and Shaishav Datta and Kyle Wanzel and Clementine Affana and Ashish Kumar and David Fleiszer and Ahmer Irfan and Jason Aubrey and Taylor M Coe and Hala Muaddi and Roxana Bucur and Nadia Rukavina and Chaya Shwaartz and Wassim Elmasry and Devon Haseltine and Matthew Bilson and Mahmoud Moustafa and Maryam Wagner and Carlos Gomez-Garibello and Xavier Sonesaksith-Turcotte and Émilie Sandman and Lily Trang Huynh and Prevost Jantchou and Marie-Lyne Nault and Jaskarn Dhaliwal and Henna Salim and Ayesha Shakeel and Suffia Malik and Wiley Chung and Abdullah Al-Ani and Mohamed Bondok and Helen Chung and Patrick Gooi and Dominique Dorion and Yousef Abdelkhalek Saber Omar and Kalpesh Hathi and Timothy Philips and Lalenthra Naidoo and Gabrielle Massé and Jean-François Tremblay and Franck Vandenbroucke-Menu and Mai-Kim Gervais and Julien Letendre and Hugues Jeanmart and Ariane Lacaille-Ranger and Abrar Ahmed and Zeel Patel and Saman Arfaie and Crystal Ma and Jack Legler and Emily Steinberg and Elie Fadel and Liam Murad and Julia Biris and Charles Desgagné and Brandon Noyon and Adam Dubrowski and Érica Patocskai and Donald McPhalen and Claire Temple-Oberle and Jasmin Dhanoa and Jason M Harley and Anita Acai and Amy Keuhl and Quang Ngo and Jonathan Sherbino and Ereny Bassilious and Elif Bilgic and Zackary Tsang and Megan Mak and Mojgan Hodaie and Rebecca Hisey and Elizabeth Klosa and Aden Wong and Farah Zaza and Gabor Fichtinger and Boris Zevin and James Lisondra and Remi Gao and Albert Fung and Johanie Victoria Piché and Adam Hocini and Myriam Belaiche and Louise McNaughton-Filion and Tomas Cordoba and Iqbal Jaffer and Faizan Amin and Jeffrey Barsuk and William McGaghie and Matthew Sibbald and Jaycie Dalson and Kwame Agyei and Samiha Mohsen and Safia Yusuf and Clara Juando-Prats and Jory Simpson and Vanja Davidovic and Recai Yilmaz and Abdulmajeed Albeloushi and Mohamed Alhantoobi and Abicumaran Uthamacumaran and Jason Lapointe and Ahmad Alhaj},

year  = {2025},

date = {2025-01-01},

volume = {68},

number = {1suppl1},

pages = {S1-S13},

publisher = {Canadian Journal of Surgery},

abstract = {Background: 

Bibliometric analysis is used to assess and interpret the academic output and impact within a specific field. We aimed to measure the quantity and quality of plastic surgery research conducted by Canadian authors from 1999 to 2023. 

Methods: 

An extensive bibliometric analysis was carried out using the Web of Science Core Collection, retrieving data from 60 leading plastic surgery journals, focusing on original articles and reviews published between 1999 and 2023. The InCites Benchmarking & Analytics platform was used to evaluate the quantity and quality of the publications. The quality was assessed using 2 main metrics: category-normalized citation impact (CNCI) and the percentage of publications in the top quartile of journals based on impact factors. We employed VOSviewer to visualize notable keywords and map collaborative relationships among universities over various periods. 

Results …},

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pubstate = {published},

tppubtype = {article}

}

@article{fichtinger2024f,

title = {Using NURBS for virtual resections in liver surgery planning: a comparative usability study},

author = {Gabriella d'Albenzio and Rebecca Hisey and Dilakshan Srikanthan and Tamas Ungi and Andras Lasso and Davit Aghayan and Gabor Fichtinger and Rafael Palomar},

url = {https://www.spiedigitallibrary.org/conference-proceedings-of-spie/12927/129270Z/Using-NURBS-for-virtual-resections-in-liver-surgery-planning/10.1117/12.3006486.short},

year  = {2024},

date = {2024-01-01},

volume = {12927},

pages = {235-241},

publisher = {SPIE},

abstract = {PURPOSE 

Accurate preoperative planning is crucial for liver resection surgery due to the complex anatomical structures and variations among patients. The need of virtual resections utilizing deformable surfaces presents a promising approach for effective liver surgery planning. However, the range of available surface definitions poses the question of which definition is most appropriate. 

METHODS 

The study compares the use of NURBS and B´ezier surfaces for the definition of virtual resections through a usability study, where 25 participants (19 biomedical researchers and 6 liver surgeons) completed tasks using varying numbers of control points driving surface deformations and different surface types. Specifically, participants aim to perform virtual liver resections using 16 and 9 control points for NURBS and B´ezier surfaces. The goal is to assess whether they can attain an optimal resection plan, effectively …},

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@article{hisey2024,

title = {Feasibility of video-based skill assessment for percutaneous nephrostomy training in Senegal},

author = {Rebecca Hisey and Fatou Bintou Ndiaye and Kyle Sunderland and Idrissa Seck and Moustapha Mbaye and Mohamed Keita and Mamadou Diahame and Ron Kikinis and Babacar Diao and Gabor Fichtinger and Mamadou Camara},

year  = {2024},

date = {2024-01-01},

keywords = {},

pubstate = {published},

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}

@article{yang2024,

title = {Frame Selection Methods to Streamline Surgical Video Annotation for Tool Detection Tasks},

author = {Jianming Yang and Rebecca Hisey and Joshua Bierbrier and Christine Law and Gabor Fichtinger and Matthew Holden},

year  = {2024},

date = {2024-01-01},

pages = {892-898},

publisher = {IEEE},

abstract = {Given the growing volume of surgical data and the increasing demand for annotation, there is a pressing need to streamline the annotation process for surgical videos. Previously, annotation tools for object detection tasks have greatly evolved, reducing time expense and enhancing ease. There are also many initial frame selection approaches for Artificial Intelligence (AI) assisted annotation tasks to further reduce human effort. However, these methods have rarely been implemented and reported in the context of surgical datasets, especially in cataract surgery datasets. The identification of initial frames to annotate before the use of any tools or algorithms determines annotation efficiency. Therefore, in this paper, we chose to prioritize the development of a method for selecting initial frames to facilitate the subsequent automated annotation process. We propose a customized initial frames selection method based on …},

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@article{hisey2024a,

title = {Feasibility of video‐based skill assessment for percutaneous nephrostomy training in Senegal},

author = {Rebecca Hisey and Fatou Bintou Ndiaye and Kyle Sunderland and Idrissa Seck and Moustapha Mbaye and Mohammed Keita and Mamadou Diahame and Ron Kikinis and Babacar Diao and Gabor Fichtinger and Mamadou Camara},

year  = {2024},

date = {2024-01-01},

journal = {Healthcare Technology Letters},

abstract = {Percutaneous nephrostomy can be an effective means of preventing irreparable renal damage from obstructive renal disease thereby providing patients with more time to access treatment to remove the source of the blockage. In sub‐Saharan Africa, where there is limited access to treatments such as dialysis and transplantation, a nephrostomy can be life‐saving. Training this procedure in simulation can allow trainees to develop their technical skills without risking patient safety, but still requires an ex‐pert observer to provide performative feedback. In this study, the feasibility of using video as an accessible method to assess skill in simulated percutaneous nephrostomy is evaluated. Six novice urology residents and six expert urologists from Ouakam Military Hospital in Dakar, Senegal performed 4 nephrostomies each using the setup. Motion‐based metrics were computed for each trial from the predicted bounding …},

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@article{fichtinger2023o,

title = {Cautery tool state detection using deep learning on intraoperative surgery videos},

author = {Lucas March and Jessica R. Rodgers and Rebecca Hisey and Amoon Jamzad and AML Santilli and D McKay and JF Rudan and M Kaufmann and KYM Ren and G Fichtinger and P Mousavi},

url = {https://www.spiedigitallibrary.org/conference-proceedings-of-spie/12466/124660D/Cautery-tool-state-detection-using-deep-learning-on-intraoperative-surgery/10.1117/12.2654234.short},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

volume = {12466},

pages = {89-95},

publisher = {SPIE},

abstract = {Treatment for Basal Cell Carcinoma (BCC) includes an excisional surgery to remove cancerous tissues, using a cautery tool to make burns along a defined resection margin around the tumor. Margin evaluation occurs post-surgically, requiring repeat surgery if positive margins are detected. Rapid Evaporative Ionization Mass Spectrometry (REIMS) can help distinguish healthy and cancerous tissue but does not provide spatial information about the cautery tool location where the spectra are acquired. We propose using intraoperative surgical video recordings and deep learning to provide surgeons with guidance to locate sites of potential positive margins. Frames from 14 intraoperative videos of BCC surgery were extracted and used to train a sequence of networks. The first network extracts frames showing surgery in-progress, then, an object detection network localizes the cautery tool and resection margin. Finally …},

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pubstate = {published},

tppubtype = {article}

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@article{fichtinger2023y,

title = {Using uncertainty quantification to improve reliability of video-based skill assessment metrics in central venous catheterization},

author = {Catherine Austin and Rebecca Hisey and Olivia O'Driscoll and Tamas Ungi and Gabor Fichtinger},

url = {https://www.spiedigitallibrary.org/conference-proceedings-of-spie/12466/124660C/Using-uncertainty-quantification-to-improve-reliability-of-video-based-skill/10.1117/12.2654419.short},

year  = {2023},

date = {2023-01-01},

volume = {12466},

pages = {84-88},

publisher = {SPIE},

abstract = {Computed-based skill assessment relies on accurate metrics to provide comprehensive feedback to trainees. Improving the accuracy of video-based metrics computed using object detection is generally done by improving the performance of the object detection network, however increasing its performance requires resources that cannot always be obtained. This study aims to improve the accuracy of metrics in central venous catheterization without requiring a high performing object detection network by removing false positive predictions identified using uncertainty quantification. The uncertainty for each bounding box was calculated using an entropy equation. The uncertainties were then compared to an uncertainty threshold computed using the optimal point of a Receiver Operating Characteristic curve. Predictions were removed if the uncertainty fell below the predefined threshold. 50 videos were recorded and …},

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tppubtype = {article}

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@article{fichtinger2023s,

title = {Identifying tool-tissue interactions to distinguish steps in simulated open inguinal hernia repair},

author = {Elizabeth Klosa and Rebecca Hisey and Kian Hashtrudi-Zaad and Boris Zevin and Tamas Ungi and Gabor Fichtinger},

url = {https://www.spiedigitallibrary.org/conference-proceedings-of-spie/12466/1246620/Identifying-tool-tissue-interactions-to-distinguish-steps-in-simulated-open/10.1117/12.2654394.short},

year  = {2023},

date = {2023-01-01},

volume = {12466},

pages = {479-486},

publisher = {SPIE},

abstract = {As medical education adopts a competency-based training approach, assessment of skills and timely provision of formative feedback is required. Provision of such assessment and feedback places a substantial time burden on surgeons. To reduce this time burden, we look to develop a computer-assisted training platform to provide both instruction and feedback to residents learning open Inguinal Hernia Repairs (IHR). To provide feedback on residents’ technical skills, we must first find a method of workflow recognition of the IHR. We thus aim to recognize and distinguish between workflow steps of an open IHR based on the presence and frequencies of different tool-tissue interactions occurring during each step. Based on ground truth tissue segmentations and tool bounding boxes, we identify the visible tissues within a bounding box. This provides an estimation of which tissues a tool is interacting with. The …},

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@article{fichtinger2023p,

title = {Using object detection for surgical tool recognition in simulated open inguinal hernia repair surgery},

author = {Kian Hashtrudi-Zaad and Rebecca Hisey and Elizabeth Klosa and Boris Zevin and Tamas Ungi and Gabor Fichtinger},

url = {https://www.spiedigitallibrary.org/conference-proceedings-of-spie/12466/124660E/Using-object-detection-for-surgical-tool-recognition-in-simulated-open/10.1117/12.2654393.short},

year  = {2023},

date = {2023-01-01},

volume = {12466},

pages = {96-101},

publisher = {SPIE},

abstract = {Following the shift from time-based medical education to a competency-based approach, a computer-assisted training platform would help relieve some of the new time burden placed on physicians. A vital component of these platforms is the computation of competency metrics which are based on surgical tool motion. Recognizing the class and motion of surgical tools is one step in the development of a training platform. Object detection can achieve tool recognition. While previous literature has reported on tool recognition in minimally invasive surgeries, open surgeries have not received the same attention. Open Inguinal Hernia Repair (OIHR), a common surgery that general surgery residents must learn, is an example of such surgeries. We present a method for object detection to recognize surgical tools in simulated OIHR. Images were extracted from six video recordings of OIHR performed on phantoms. Tools …},

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pubstate = {published},

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@article{fichtinger2023l,

title = {Desing and realization of a computer-assisted nephrostomy guidance system},

author = {Fatou Bintou Ndiaye and Leah Groves and Rebecca Hisey and Tamas Ungi and Idy Diop and Parvin Mousavi and Gabor Fichtinger and Mamadou Samba Camara},

url = {https://ieeexplore.ieee.org/abstract/document/10253146/},

year  = {2023},

date = {2023-01-01},

pages = {1-6},

publisher = {IEEE},

abstract = {Background and purpose 

Nowadays, computerized nephrostomy techniques exist. Although relatively safe, several factors make it difficult for inexperienced users. A computer-assisted nephrostomy guidance system has been studied to increase the success rate of this intervention and reduce the work and difficulties encountered by the actors. 

Methods 

To design the system, two methods will be studied. Following this study, this system was designed based on method 2. SmartSysNephro is composed of a hardware part whose manipulations made by the user are visualized and assisted by the computer. This nephrostomy procedure that the user simulates is monitored by webcam. Using the data from this Intel Real Sense webcam, allowed to propose a CNN YOLO model. 

Results 

The results obtained show that the objectives set have been achieved globally. The SmartSysNephro system gives real time warning …},

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pubstate = {published},

tppubtype = {article}

}

@article{fichtinger2022r,

title = {Recognizing multiple needle insertion attempts for performance assessment in central venous catheterization training},

author = {Catherine Austin and Rebecca Hisey and Olivia O'Driscoll and Daenis Camire and Jason Erb and Daniel Howes and Tamas Ungi and Gabor Fichtinger},

url = {https://www.spiedigitallibrary.org/conference-proceedings-of-spie/12034/1203428/Recognizing-multiple-needle-insertion-attempts-for-performance-assessment-in-central/10.1117/12.2613190.short},

year  = {2022},

date = {2022-01-01},

volume = {12034},

pages = {518-524},

publisher = {SPIE},

abstract = {Purpose 

Computer-assisted skill assessment has traditionally been focused on general metrics related to tool motion and usage time. While these metrics are important for an overall evaluation of skill, they do not address critical errors made during the procedure. This study examines the effectiveness of utilizing object detection to quantify the critical error of making multiple needle insertion attempts in central venous catheterization. 

Methods 

6860 images were annotated with ground truth bounding boxes around the syringe attached to the needle. The images were registered using the location of the phantom, and the bounding boxes from the training set were used to identify the regions where the needle was most likely inserting the phantom. A Faster region-based convolutional neural network was trained to identify the syringe and produce the bounding box location for images in the test set. A needle insertion …},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{fichtinger2022j,

title = {Phase recognition and cautery localization in basal cell carcinoma surgical videos},

author = {L March and JR Rodgers and A Jamzad and AML Santilli and Rebecca Hisey and D McKay and JF Rudan and M Kaufmann and KYM Ren and G Fichtinger and P Mousavi},

url = {https://www.spiedigitallibrary.org/conference-proceedings-of-spie/12034/1203427/Phase-recognition-and-cautery-localization-in-basal-cell-carcinoma-surgical/10.1117/12.2611837.short},

year  = {2022},

date = {2022-01-01},

urldate = {2022-01-01},

volume = {12034},

pages = {508-517},

publisher = {SPIE},

abstract = {Surgical excision for basal cell carcinoma (BCC) is a common treatment to remove the affected areas of skin. Minimizing positive margins around excised tissue is essential for successful treatment. Residual cancer cells may result in repeat surgery; however, detecting remaining cancer can be challenging and time-consuming. Using chemical signal data acquired while tissue is excised with a cautery tool, the iKnife system can discriminate between healthy and cancerous tissue but lacks spatial information, making it difficult to navigate back to suspicious margins. Intraoperative videos of BCC excision allow cautery locations to be tracked, providing the sites of potential positive margins. We propose a deep learning approach using convolutional neural networks to recognize phases in the videos and subsequently track the cautery location, comparing two localization methods (supervised and semi-supervised …},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Hisey2021b,

title = {System for central venous catheterization training using computer vision-based workflow feedback},

author = {Rebecca Hisey and Daenis Camire and Jason Erb and Daniel Howes and Gabor Fichtinger and Tamas Ungi},

year  = {2021},

date = {2021-10-01},

urldate = {2021-10-01},

journal = {IEEE Transactions on Biomedical Engineering},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{fichtinger2021l,

title = {Ultrasound probe pose classification for task recognition in central venous catheterization},

author = {Colton Barr and Rebecca Hisey and Tamas Ungi and Gabor Fichtinger},

url = {https://ieeexplore.ieee.org/abstract/document/9630033/},

year  = {2021},

date = {2021-01-01},

pages = {5023-5026},

publisher = {IEEE},

abstract = {Central Line Tutor is a system that facilitates real-time feedback during training for central venous catheterization. One limitation of Central Line Tutor is its reliance on expensive, cumbersome electromagnetic tracking to facilitate various training aids, including ultrasound task identification and segmentation of neck vasculature. The purpose of this study is to validate deep learning methods for vessel segmentation and ultrasound pose classification in order to mitigate the system’s reliance on electromagnetic tracking. A large dataset of segmented and classified ultrasound images was generated from participant data captured using Central Line Tutor. A U-Net architecture was used to perform vessel segmentation, while a shallow Convolutional Neural Network (CNN) architecture was designed to classify the pose of the ultrasound probe. A second classifier architecture was also tested that used the U-Net output as …},

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pubstate = {published},

tppubtype = {article}

}

@article{fichtinger2018i,

title = {A learning curve analysis of ultrasound-guided in-plane and out-of-plane vascular access training with Perk Tutor},

author = {Sean Xia and Zsuzsanna Keri and Matthew S Holden and Rebecca Hisey and Hillary Lia and Tamas Ungi and Christopher H Mitchell and Gabor Fichtinger},

url = {https://www.spiedigitallibrary.org/conference-proceedings-of-spie/10576/1057625/A-learning-curve-analysis-of-ultrasound-guided-in-plane-and/10.1117/12.2293789.short},

year  = {2018},

date = {2018-01-01},

volume = {10576},

pages = {512-519},

publisher = {SPIE},

abstract = {PURPOSE 

Under ultrasound guidance, procedures that have been traditionally performed using landmark approaches have become safer and more efficient. However, inexperienced trainees struggle with coordinating probe handling and needle insertion. We aimed to establish learning curves to identify the rate of acquisition of in-plane and out-of-plane vascular access skill in novice medical trainees. 

METHODS 

Thirty-eight novice participants were randomly assigned to perform either in-plane or out-of-plane insertions. Participants underwent baseline testing, four practice insertions (with 3D visualization assistance), and final testing; performance metrics were computed for all procedures. Five expert participants performed insertions in both approaches to establish expert performance metric benchmarks. 

RESULTS In-plane novices (n=19) demonstrated significant final reductions in needle path inefficiency (45.8 …},

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tppubtype = {article}

}