{"id":2111,"date":"2024-08-25T20:29:49","date_gmt":"2024-08-25T20:29:49","guid":{"rendered":"https:\/\/labs.cs.queensu.ca\/perklab\/?post_type=qsc_member&p=2111"},"modified":"2024-08-25T20:29:50","modified_gmt":"2024-08-25T20:29:50","slug":"andras-lasso","status":"publish","type":"qsc_member","link":"https:\/\/labs.cs.queensu.ca\/perklab\/members\/andras-lasso\/","title":{"rendered":"Andras\u00a0Lasso"},"content":{"rendered":"
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Andras\u00a0Lasso<\/h1><\/div>\n\t\t
Senior Researcher<\/div>\n\t\t
School of Computing<\/div>\n\t\t
Queen’s University<\/div>\n\t\t
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lasso@queensu.ca<\/a><\/div>\n\t\t\t
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Biography<\/h2>\n\n\n\n

Andras Lasso graduated at the Budapest University of Technology, Hungary (MSc in Electrical Engineering, 2000; PhD, 2011). He joined GE Healthcare as a software engineer in 2000, was appointed Lead Engineer in 2003, and Senior Engineer in 2008. He developed various software components and test infrastructure\u00a0for GE Innova interventional X-ray systems and GE Advantage Workstation and led development of advanced image visualization, quantification, real-time image fusion and guidance applications. He also participated in various research collaboration projects in medical image analysis, enhancement, segmentation, registration, and fusion. He joined the Perk Lab in 2009 as a Senior Engineer to work on image-guided intervention research and system development. In 2011 he was appointed to be Associate Director of the Perk Lab. His main interests are developing\u00a0high-quality, reusable, open-source\u00a0software components\u00a0and using them to\u00a0build systems for translational research and clinical use.<\/p>\n\n\n\n

Publications<\/h2>\n\n\n
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264 entries<\/span> «<\/a> ‹<\/a> 1 of 6 ›<\/a> »<\/a> <\/div><\/div>

Bumm, Rudolf; Zaffino, Paolo; Lasso, Andras; Est\u00e9par, Ra\u00fal San Jos\u00e9; Pieper, Steven; Wasserthal, Jakob; Spadea, Maria Francesca; Latshang, Tsogyal; Kawel-Boehm, Nadine; W\u00e4ckerlin, Adrian; Werner, Raphael; H\u00e4ssig, Gabriela; Furrer, Markus; Kikinis, Ron<\/p>

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 \u2026 Journal Article<\/span> <\/p>

In: <\/span>Journal of Thoracic Disease, <\/span>vol. 16, <\/span>no. 2, <\/span>2024<\/span>.<\/p>

Abstract<\/a><\/span> | BibTeX<\/a><\/span><\/p>

@article{bumm2024,
\r\ntitle = {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 \u2026},
\r\nauthor = {Rudolf Bumm and Paolo Zaffino and Andras Lasso and Ra\u00fal San Jos\u00e9 Est\u00e9par and Steven Pieper and Jakob Wasserthal and Maria Francesca Spadea and Tsogyal Latshang and Nadine Kawel-Boehm and Adrian W\u00e4ckerlin and Raphael Werner and Gabriela H\u00e4ssig and Markus Furrer and Ron Kikinis},
\r\nyear  = {2024},
\r\ndate = {2024-01-01},
\r\njournal = {Journal of Thoracic Disease},
\r\nvolume = {16},
\r\nnumber = {2},
\r\npublisher = {AME Publishing Company},
\r\nabstract = {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\u2019 assessments.},
\r\nkeywords = {},
\r\npubstate = {published},
\r\ntppubtype = {article}
\r\n}
\r\n<\/pre><\/div>
Close<\/a><\/p><\/div>
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\u2019 assessments.<\/div>
Close<\/a><\/p><\/div><\/div><\/div>
 Herz, Christian;  Vergnet, Nicolas;  Tian, Sijie;  Aly, Abdullah H;  Jolley, Matthew A;  Tran, Nathanael;  Arenas, Gabriel;  Lasso, Andras;  Schwartz, Nadav;  O\u2019Neill, Kathleen E;  Yushkevich, Paul A;  Pouch, Alison M<\/p>
Open-source graphical user interface for the creation of synthetic skeletons for medical image analysis Journal Article<\/span> <\/p>
In: <\/span>Journal of Medical Imaging, <\/span>vol. 11, <\/span>no. 3, <\/span>pp. 036001-036001, <\/span>2024<\/span>.<\/p>
Abstract<\/a><\/span> | BibTeX<\/a><\/span><\/p>
@article{herz2024,
\r\ntitle = {Open-source graphical user interface for the creation of synthetic skeletons for medical image analysis},
\r\nauthor = {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\u2019Neill and Paul A Yushkevich and Alison M Pouch},
\r\nyear  = {2024},
\r\ndate = {2024-01-01},
\r\njournal = {Journal of Medical Imaging},
\r\nvolume = {11},
\r\nnumber = {3},
\r\npages = {036001-036001},
\r\npublisher = {Society of Photo-Optical Instrumentation Engineers},
\r\nabstract = {Purpose 
\r\nDeformable 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. 
\r\nApproach 
\r\nA 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 \u2026},
\r\nkeywords = {},
\r\npubstate = {published},
\r\ntppubtype = {article}
\r\n}
\r\n<\/pre><\/div>
Close<\/a><\/p><\/div>
Purpose 
\r\nDeformable 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. 
\r\nApproach 
\r\nA 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 \u2026<\/div>
Close<\/a><\/p><\/div><\/div><\/div>
 d'Albenzio, Gabriella;  Hisey, Rebecca;  Srikanthan, Dilakshan;  Ungi, Tamas;  Lasso, Andras;  Aghayan, Davit;  Fichtinger, Gabor;  Palomar, Rafael<\/p>
Using NURBS for virtual resections in liver surgery planning: a comparative usability study<\/a> Journal Article<\/span> <\/p>
In: <\/span>vol. 12927, <\/span>pp. 235-241, <\/span>2024<\/span>.<\/p>
Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>
@article{fichtinger2024f,
\r\ntitle = {Using NURBS for virtual resections in liver surgery planning: a comparative usability study},
\r\nauthor = {Gabriella d'Albenzio and Rebecca Hisey and Dilakshan Srikanthan and Tamas Ungi and Andras Lasso and Davit Aghayan and Gabor Fichtinger and Rafael Palomar},
\r\nurl = {https:\/\/www.spiedigitallibrary.org\/conference-proceedings-of-spie\/12927\/129270Z\/Using-NURBS-for-virtual-resections-in-liver-surgery-planning\/10.1117\/12.3006486.short},
\r\nyear  = {2024},
\r\ndate = {2024-01-01},
\r\nvolume = {12927},
\r\npages = {235-241},
\r\npublisher = {SPIE},
\r\nabstract = {PURPOSE 
\r\nAccurate 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. 
\r\nMETHODS 
\r\nThe study compares the use of NURBS and B\u00b4ezier 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\u00b4ezier surfaces. The goal is to assess whether they can attain an optimal resection plan, effectively \u2026},
\r\nkeywords = {},
\r\npubstate = {published},
\r\ntppubtype = {article}
\r\n}
\r\n<\/pre><\/div>
Close<\/a><\/p><\/div>
PURPOSE 
\r\nAccurate 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. 
\r\nMETHODS 
\r\nThe study compares the use of NURBS and B\u00b4ezier 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\u00b4ezier surfaces. The goal is to assess whether they can attain an optimal resection plan, effectively \u2026<\/div>
Close<\/a><\/p><\/div>