Publications

@article{jamzad2025,

title = {MassVision: An open-source end-to-end platform for AI-driven mass spectrometry image analysis},

author = {Amoon Jamzad and Jade Warren and Ayesha Syeda and Martin Kaufmann and Natasha Iaboni and Christopher Nicol and John Rudan and Kevin Ren and David Hurlbut and Sonal Varma and Gabor Fichtinger and Parvin Mousavi},

year  = {2025},

date = {2025-01-01},

journal = {bioRxiv},

pages = {2025.01. 29.635489},

publisher = {Cold Spring Harbor Laboratory},

abstract = {Mass spectrometry imaging (MSI) combines spatial and spectral data to reveal detailed molecular compositions within biological samples. Despite their immense potential, MSI workflows are hindered by the complexity and high dimensionality of the data, making their analysis computationally intensive and often requiring expertise in coding. Existing tools frequently lack the integration needed for seamless, scalable, and end-to-end workflows, forcing researchers to rely on local solutions or multiple platforms, hindering efficiency and accessibility. We introduce MassVision, a comprehensive software platform for MSI analysis. Built on the 3D Slicer ecosystem, MassVision integrates MSI-specific functionalities while addressing general user requirements for accessibility and usability. Its intuitive interface lowers barriers for researchers with varying levels of computational expertise, while its scalability supports high-throughput studies and multi-slide datasets. Key functionalities include visualization, co-localization, dataset curation, dataset merging, spectral and spatial preprocessing, AI model training, and AI deployment on full MSI data. We detail the workflow and functionalities of MassVision and demonstrate its effectiveness through different experimental use cases such as exploratory data analysis, ion identification, and tissue-type classification, on in-house and publicly available data from different MSI modalities. These use cases underscore the MassVision's ability to seamlessly integrate MSI data handling steps into a single platform, and highlight its potential to reveal new insights and structures when examining biological samples. By …},

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@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}

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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{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},

tppubtype = {article}

}

@article{connolly2024,

title = {SlicerROS2: A Research and Development Module for Image-Guided Robotic Interventions},

author = {Laura Connolly and Aravind S Kumar and Kapi Ketan Mehta and Lidia Al-Zogbi and Peter Kazanzides and Parvin Mousavi and Gabor Fichtinger and Axel Krieger and Junichi Tokuda and Russell H Taylor and Simon Leonard and Anton Deguet},

year  = {2024},

date = {2024-01-01},

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

publisher = {IEEE},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{kaufmann2024,

title = {Testing of rapid evaporative mass spectrometry for histological tissue classification and molecular diagnostics in a multi-site study},

author = {Martin Kaufmann and Pierre-Maxence Vaysse and Adele Savage and Loes FS Kooreman and Natasja Janssen and Sonal Varma and Kevin Yi Mi Ren and Shaila Merchant and Cecil Jay Engel and Steven WM Olde Damink and Marjolein L Smidt and Sami Shousha and Hemali Chauhan and Evdoxia Karali and Emine Kazanc and George Poulogiannis and Gabor Fichtinger and Boglárka Tauber and Daniel R Leff and Steven D Pringle and John F Rudan and Ron MA Heeren and Tiffany Porta Siegel and Zoltán Takáts and Júlia Balog},

year  = {2024},

date = {2024-01-01},

journal = {British Journal of Cancer},

pages = {1-11},

publisher = {Nature Publishing Group UK},

abstract = {Background 

While REIMS technology has successfully been demonstrated for the histological identification of ex-vivo breast tumor tissues, questions regarding the robustness of the approach and the possibility of tumor molecular diagnostics still remain unanswered. In the current study, we set out to determine whether it is possible to acquire cross-comparable REIMS datasets at multiple sites for the identification of breast tumors and subtypes. 

Methods 

A consortium of four sites with three of them having access to fresh surgical tissue samples performed tissue analysis using identical REIMS setups and protocols. Overall, 21 breast cancer specimens containing pathology-validated tumor and adipose tissues were analyzed and results were compared using uni- and multivariate statistics on normal, WT and PIK3CA mutant ductal carcinomas. 

Results 

Statistical analysis of data from standards showed significant …},

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pubstate = {published},

tppubtype = {article}

}

@article{hashtrudi-zaad2024,

title = {Expert-guided optimization of ultrasound segmentation models for 3D spine imaging},

author = {Kian Hashtrudi-Zaad and Tamas Ungi and Chris Yeung and Zachary Baum and Pavel-Dumitru Cernelev and Anthony N Hage and Christopher Schlenger and Gabor Fichtinger},

year  = {2024},

date = {2024-01-01},

pages = {680-685},

publisher = {IEEE},

abstract = {We explored ultrasound for imaging bones, specifically the spine, as a safer and more accessible alternative to conventional X-ray. We aimed to improve how well deep learning segmentation models filter bone signals from ultrasound frames with the goal of using these segmented images for reconstructing the 3-dimensional spine volume.Our dataset consisted of spatially tracked ultrasound scans from 25 patients. Image frames from these scans were also manually annotated to provide training data for image segmentation deep learning. To find the optimal automatic segmentation method, we assessed five different artificial neural network models and their variations by hyperparameter tuning. Our main contribution is a new approach for model selection, employing an Elo rating system to efficiently rank trained models based on their visual performance as assessed by clinical users. This method addresses the …},

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pubstate = {published},

tppubtype = {article}

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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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tppubtype = {article}

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@article{barr2024,

title = {Extracting 3D Prostate Geometry from 2D Optically-Tracked Transrectal Ultrasound Images},

author = {Colton Barr and Leah Groves and Tamas Ungi and D Robert Siemens and Babacar Diao and Ron Kikinis and Parvin Mousavi and Gabor Fichtinger},

year  = {2024},

date = {2024-01-01},

pages = {32-37},

publisher = {IEEE},

abstract = {The technical challenges of traditional transrectal ultrasound-guided prostate biopsy, combined with the limited availability of more advanced prostate imaging techniques, have exacerbated existing differences in prostate cancer outcomes between high-resource and low-resource healthcare settings. The objective of this paper is to improve the tools available to clinicians in low-resource settings by working towards an inexpensive ultrasound-guided prostate biopsy navigation system. The principal contributions detailed here are the design, implementation, and testing of a system capable of generating a 3D model of the prostate from spatially-tracked 2D ultrasound images. The system uses open-source software, low-cost materials, and deep learning to segment and localize cross-sections of the prostate in order to produce a patient-specific 3D prostate model. A user study was performed to evaluate the …},

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pubstate = {published},

tppubtype = {article}

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@article{bumm2024,

title = {Artificial intelligence (AI)-assisted chest computer tomography (CT) insights: a study on intensive care unit (ICU) admittance trends in 78 coronavirus disease 2019 (COVID-19 …},

author = {Rudolf Bumm and Paolo Zaffino and Andras Lasso and Raúl San José Estépar and Steven Pieper and Jakob Wasserthal and Maria Francesca Spadea and Tsogyal Latshang and Nadine Kawel-Boehm and Adrian Wäckerlin and Raphael Werner and Gabriela Hässig and Markus Furrer and Ron Kikinis},

year  = {2024},

date = {2024-01-01},

journal = {Journal of Thoracic Disease},

volume = {16},

number = {2},

publisher = {AME Publishing Company},

abstract = {Background: The global coronavirus disease 2019 (COVID-19) pandemic has posed substantial challenges for healthcare systems, notably the increased demand for chest computed tomography (CT) scans, which lack automated analysis. Our study addresses this by utilizing artificial intelligence-supported automated computer analysis to investigate lung involvement distribution and extent in COVID-19 patients. Additionally, we explore the association between lung involvement and intensive care unit (ICU) admission, while also comparing computer analysis performance with expert radiologists’ assessments.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{herz2024,

title = {Open-source graphical user interface for the creation of synthetic skeletons for medical image analysis},

author = {Christian Herz and Nicolas Vergnet and Sijie Tian and Abdullah H Aly and Matthew A Jolley and Nathanael Tran and Gabriel Arenas and Andras Lasso and Nadav Schwartz and Kathleen E O’Neill and Paul A Yushkevich and Alison M Pouch},

year  = {2024},

date = {2024-01-01},

journal = {Journal of Medical Imaging},

volume = {11},

number = {3},

pages = {036001-036001},

publisher = {Society of Photo-Optical Instrumentation Engineers},

abstract = {Purpose 

Deformable medial modeling is an inverse skeletonization approach to representing anatomy in medical images, which can be used for statistical shape analysis and assessment of patient-specific anatomical features such as locally varying thickness. It involves deforming a pre-defined synthetic skeleton, or template, to anatomical structures of the same class. The lack of software for creating such skeletons has been a limitation to more widespread use of deformable medial modeling. Therefore, the objective of this work is to present an open-source user interface (UI) for the creation of synthetic skeletons for a range of medial modeling applications in medical imaging. 

Approach 

A UI for interactive design of synthetic skeletons was implemented in 3D Slicer, an open-source medical image analysis application. The steps in synthetic skeleton design include importation and skeletonization of a 3D …},

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pubstate = {published},

tppubtype = {article}

}

@article{fichtinger2024i,

title = {Three-dimensional navigated mass spectrometry for intraoperative margin assessment during breast cancer surgery},

author = {Martin Kaufmann and Amoon Jamzad and Tamas Ungi and Jessica Rodgers and Teaghan Koster and Yeung Chris and Natasja Janssen and Julie McMullen and Kathryn Solberg and Joanna Cheesman and Kevin Ti Mi Ren and Sonal Varma and Shaila Merchant and Cecil Jay Engel and G Ross Walker and Andrea Gallo and Doris Jabs and Parvin Mousavi and Gabor Fichtinger and John Rudan},

url = {https://scholar.google.com/scholar?cluster=16985799098796735653&hl=en&oi=scholarr},

year  = {2024},

date = {2024-01-01},

volume = {31},

issue = {1},

pages = {S10-S10},

publisher = {SPRINGER},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{fichtinger2024g,

title = {3D volume reconstruction for pediatric scoliosis evaluation using motion-tracked ultrasound},

author = {Lucas Hintz and Sarah C Nanziri and Sarah Dance and Kochai Jawed and Matthew Oetgen and Tamas Ungi and Gabor Fichtinger and Christopher Schlenger and Kevin Cleary},

url = {https://www.spiedigitallibrary.org/conference-proceedings-of-spie/12928/1292811/3D-volume-reconstruction-for-pediatric-scoliosis-evaluation-using-motion-tracked/10.1117/12.3008629.short},

year  = {2024},

date = {2024-01-01},

volume = {12928},

pages = {223-227},

publisher = {SPIE},

abstract = {We have evaluated AI-segmented 3D spine ultrasound for scoliosis measurement in a feasibility study of pediatric patients enrolled over two months in the orthopedic clinic at Children’s National Hospital. Patients who presented to clinic for scoliosis evaluation were invited to participate and their spines were scanned using the method. Our system consists of three Optitrack cameras which track a Clarius wireless ultrasound probe and infrared marked waistbelt. Proprietary SpineUs software uses neural networks to build a volumetric reproduction of the spine in real-time using a laptop computer. We can approximate the maximal lateral curvature using the transverse process angle of the virtual reconstruction; these angles were compared to those from the radiographic exams for each patient from the same visit. Scans and radiographs from five patients were examined and demonstrate a linear correlation between …},

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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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}