Kim, Andrew S.; Yeung, Chris; Szabo, Robert; Sunderland, Kyle; Hisey, Rebecca; Morton, David; Kikinis, Ron; Diao, Babacar; Mousavi, Parvin; Ungi, Tamas; Fichtinger, Gabor
SPIE, 2024.
@proceedings{Kim2024,
title = {Percutaneous nephrostomy needle guidance using real-time 3D anatomical visualization with live ultrasound segmentation},
author = {Andrew S. Kim and Chris Yeung and Robert Szabo and Kyle Sunderland and Rebecca Hisey and David Morton and Ron Kikinis and Babacar Diao and Parvin Mousavi and Tamas Ungi and Gabor Fichtinger},
editor = {Maryam E. Rettmann and Jeffrey H. Siewerdsen},
doi = {10.1117/12.3006533},
year = {2024},
date = {2024-03-29},
urldate = {2024-03-29},
publisher = {SPIE},
abstract = {
PURPOSE: Percutaneous nephrostomy is a commonly performed procedure to drain urine to provide relief in patients with hydronephrosis. Conventional percutaneous nephrostomy needle guidance methods can be difficult, expensive, or not portable. We propose an open-source real-time 3D anatomical visualization aid for needle guidance with live ultrasound segmentation and 3D volume reconstruction using free, open-source software. METHODS: Basic hydronephrotic kidney phantoms were created, and recordings of these models were manually segmented and used to train a deep learning model that makes live segmentation predictions to perform live 3D volume reconstruction of the fluid-filled cavity. Participants performed 5 needle insertions with the visualization aid and 5 insertions with ultrasound needle guidance on a kidney phantom in randomized order, and these were recorded. Recordings of the trials were analyzed for needle tip distance to the center of the target calyx, needle insertion time, and success rate. Participants also completed a survey on their experience. RESULTS: Using the visualization aid showed significantly higher accuracy, while needle insertion time and success rate were not statistically significant at our sample size. Participants mostly responded positively to the visualization aid, and 80% found it easier to use than ultrasound needle guidance. CONCLUSION: We found that our visualization aid produced increased accuracy and an overall positive experience. We demonstrated that our system is functional and stable and believe that the workflow with this system can be applied to other procedures. This visualization aid system is effective on phantoms and is ready for translation with clinical data.},
keywords = {},
pubstate = {published},
tppubtype = {proceedings}
}
PURPOSE: Percutaneous nephrostomy is a commonly performed procedure to drain urine to provide relief in patients with hydronephrosis. Conventional percutaneous nephrostomy needle guidance methods can be difficult, expensive, or not portable. We propose an open-source real-time 3D anatomical visualization aid for needle guidance with live ultrasound segmentation and 3D volume reconstruction using free, open-source software. METHODS: Basic hydronephrotic kidney phantoms were created, and recordings of these models were manually segmented and used to train a deep learning model that makes live segmentation predictions to perform live 3D volume reconstruction of the fluid-filled cavity. Participants performed 5 needle insertions with the visualization aid and 5 insertions with ultrasound needle guidance on a kidney phantom in randomized order, and these were recorded. Recordings of the trials were analyzed for needle tip distance to the center of the target calyx, needle insertion time, and success rate. Participants also completed a survey on their experience. RESULTS: Using the visualization aid showed significantly higher accuracy, while needle insertion time and success rate were not statistically significant at our sample size. Participants mostly responded positively to the visualization aid, and 80% found it easier to use than ultrasound needle guidance. CONCLUSION: We found that our visualization aid produced increased accuracy and an overall positive experience. We demonstrated that our system is functional and stable and believe that the workflow with this system can be applied to other procedures. This visualization aid system is effective on phantoms and is ready for translation with clinical data.
d'Albenzio, Gabriella; Hisey, Rebecca; Srikanthan, Dilakshan; Ungi, Tamas; Lasso, Andras; Aghayan, Davit; Fichtinger, Gabor; Palomar, Rafael
Using NURBS for virtual resections in liver surgery planning: a comparative usability study Journal Article
In: vol. 12927, pp. 235-241, 2024.
@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 …},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
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 …
Connolly, Laura; Fooladgar, Fahimeh; Jamzad, Amoon; Kaufmann, Martin; Syeda, Ayesha; Ren, Kevin; Abolmaesumi, Purang; Rudan, John F; McKay, Doug; Fichtinger, Gabor; Mousavi, Parvin
ImSpect: Image-driven self-supervised learning for surgical margin evaluation with mass spectrometry Journal Article
In: International Journal of Computer Assisted Radiology and Surgery, pp. 1-8, 2024.
@article{fichtinger2024e,
title = {ImSpect: Image-driven self-supervised learning for surgical margin evaluation with mass spectrometry},
author = {Laura Connolly and Fahimeh Fooladgar and Amoon Jamzad and Martin Kaufmann and Ayesha Syeda and Kevin Ren and Purang Abolmaesumi and John F Rudan and Doug McKay and Gabor Fichtinger and Parvin Mousavi},
url = {https://link.springer.com/article/10.1007/s11548-024-03106-1},
year = {2024},
date = {2024-01-01},
journal = {International Journal of Computer Assisted Radiology and Surgery},
pages = {1-8},
publisher = {Springer International Publishing},
abstract = {Purpose
Real-time assessment of surgical margins is critical for favorable outcomes in cancer patients. The iKnife is a mass spectrometry device that has demonstrated potential for margin detection in cancer surgery. Previous studies have shown that using deep learning on iKnife data can facilitate real-time tissue characterization. However, none of the existing literature on the iKnife facilitate the use of publicly available, state-of-the-art pretrained networks or datasets that have been used in computer vision and other domains.
Methods
In a new framework we call ImSpect, we convert 1D iKnife data, captured during basal cell carcinoma (BCC) surgery, into 2D images in order to capitalize on state-of-the-art image classification networks. We also use self-supervision to leverage large amounts of unlabeled, intraoperative data to accommodate the data requirements of these networks.
Results
Through extensive ablation …},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Real-time assessment of surgical margins is critical for favorable outcomes in cancer patients. The iKnife is a mass spectrometry device that has demonstrated potential for margin detection in cancer surgery. Previous studies have shown that using deep learning on iKnife data can facilitate real-time tissue characterization. However, none of the existing literature on the iKnife facilitate the use of publicly available, state-of-the-art pretrained networks or datasets that have been used in computer vision and other domains.
Methods
In a new framework we call ImSpect, we convert 1D iKnife data, captured during basal cell carcinoma (BCC) surgery, into 2D images in order to capitalize on state-of-the-art image classification networks. We also use self-supervision to leverage large amounts of unlabeled, intraoperative data to accommodate the data requirements of these networks.
Results
Through extensive ablation …
Yeung, Chris; Ungi, Tamas; Hu, Zoe; Jamzad, Amoon; Kaufmann, Martin; Walker, Ross; Merchant, Shaila; Engel, Cecil Jay; Jabs, Doris; Rudan, John; Mousavi, Parvin; Fichtinger, Gabor
From quantitative metrics to clinical success: assessing the utility of deep learning for tumor segmentation in breast surgery Journal Article
In: International Journal of Computer Assisted Radiology and Surgery, pp. 1-9, 2024.
@article{fichtinger2024d,
title = {From quantitative metrics to clinical success: assessing the utility of deep learning for tumor segmentation in breast surgery},
author = {Chris Yeung and Tamas Ungi and Zoe Hu and Amoon Jamzad and Martin Kaufmann and Ross Walker and Shaila Merchant and Cecil Jay Engel and Doris Jabs and John Rudan and Parvin Mousavi and Gabor Fichtinger},
url = {https://link.springer.com/article/10.1007/s11548-024-03133-y},
year = {2024},
date = {2024-01-01},
journal = {International Journal of Computer Assisted Radiology and Surgery},
pages = {1-9},
publisher = {Springer International Publishing},
abstract = {Purpose
Preventing positive margins is essential for ensuring favorable patient outcomes following breast-conserving surgery (BCS). Deep learning has the potential to enable this by automatically contouring the tumor and guiding resection in real time. However, evaluation of such models with respect to pathology outcomes is necessary for their successful translation into clinical practice.
Methods
Sixteen deep learning models based on established architectures in the literature are trained on 7318 ultrasound images from 33 patients. Models are ranked by an expert based on their contours generated from images in our test set. Generated contours from each model are also analyzed using recorded cautery trajectories of five navigated BCS cases to predict margin status. Predicted margins are compared with pathology reports.
Results
The best-performing model using both quantitative evaluation and our visual …},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Preventing positive margins is essential for ensuring favorable patient outcomes following breast-conserving surgery (BCS). Deep learning has the potential to enable this by automatically contouring the tumor and guiding resection in real time. However, evaluation of such models with respect to pathology outcomes is necessary for their successful translation into clinical practice.
Methods
Sixteen deep learning models based on established architectures in the literature are trained on 7318 ultrasound images from 33 patients. Models are ranked by an expert based on their contours generated from images in our test set. Generated contours from each model are also analyzed using recorded cautery trajectories of five navigated BCS cases to predict margin status. Predicted margins are compared with pathology reports.
Results
The best-performing model using both quantitative evaluation and our visual …
Kaufmann, Martin; Jamzad, Amoon; Ungi, Tamas; Rodgers, Jessica R; Koster, Teaghan; Yeung, Chris; Ehrlich, Josh; Santilli, Alice; Asselin, Mark; Janssen, Natasja; McMullen, Julie; Solberg, Kathryn; Cheesman, Joanna; Carlo, Alessia Di; Ren, Kevin Yi Mi; Varma, Sonal; Merchant, Shaila; Engel, Cecil Jay; Walker, G Ross; Gallo, Andrea; Jabs, Doris; Mousavi, Parvin; Fichtinger, Gabor; Rudan, John F
Abstract PO2-23-07: Three-dimensional navigated mass spectrometry for intraoperative margin assessment during breast cancer surgery Journal Article
In: Cancer Research, vol. 84, iss. 9_Supplement, pp. PO2-23-07-PO2-23-07, 2024.
@article{fichtinger2024c,
title = {Abstract PO2-23-07: Three-dimensional navigated mass spectrometry for intraoperative margin assessment during breast cancer surgery},
author = {Martin Kaufmann and Amoon Jamzad and Tamas Ungi and Jessica R Rodgers and Teaghan Koster and Chris Yeung and Josh Ehrlich and Alice Santilli and Mark Asselin and Natasja Janssen and Julie McMullen and Kathryn Solberg and Joanna Cheesman and Alessia Di Carlo and Kevin Yi 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 F Rudan},
url = {https://aacrjournals.org/cancerres/article/84/9_Supplement/PO2-23-07/743683},
year = {2024},
date = {2024-01-01},
journal = {Cancer Research},
volume = {84},
issue = {9_Supplement},
pages = {PO2-23-07-PO2-23-07},
publisher = {The American Association for Cancer Research},
abstract = {Positive resection margins occur in approximately 25% of breast cancer (BCa) surgeries, requiring re-operation. Margin status is not routinely available during surgery; thus, technologies that identify residual cancer on the specimen or cavity are needed to provide intraoperative decision support that may reduce positive margin rates. Rapid evaporative ionization mass spectrometry (REIMS) is an emerging technique that chemically profiles the plume generated by tissue cauterization to classify the ablated tissue as either cancerous or non-cancerous, on the basis of detected lipid species. Although REIMS can distinguish cancer and non-cancerous breast tissue by the signals generated, it does not indicate the location of the classified tissue in real-time. Our objective was to combine REIMS with spatio-temporal navigation (navigated REIMS), and to compare performance of navigated REIMS with conventional …},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Amin, Silvani; Dewey, Hannah; Lasso, Andras; Sabin, Patricia; Han, Ye; Vicory, Jared; Paniagua, Beatriz; Herz, Christian; Nam, Hannah; Cianciulli, Alana; Flynn, Maura; Laurence, Devin W; Harrild, David; Fichtinger, Gabor; Cohen, Meryl S; Jolley, Matthew A
Euclidean and shape-based analysis of the dynamic mitral annulus in children using a novel open-source framework Journal Article
In: Journal of the American Society of Echocardiography, vol. 37, iss. 2, pp. 259-267, 2024.
@article{fichtinger2024b,
title = {Euclidean and shape-based analysis of the dynamic mitral annulus in children using a novel open-source framework},
author = {Silvani Amin and Hannah Dewey and Andras Lasso and Patricia Sabin and Ye Han and Jared Vicory and Beatriz Paniagua and Christian Herz and Hannah Nam and Alana Cianciulli and Maura Flynn and Devin W Laurence and David Harrild and Gabor Fichtinger and Meryl S Cohen and Matthew A Jolley},
url = {https://www.sciencedirect.com/science/article/pii/S0894731723005941},
year = {2024},
date = {2024-01-01},
journal = {Journal of the American Society of Echocardiography},
volume = {37},
issue = {2},
pages = {259-267},
publisher = {Mosby},
abstract = {Background
The dynamic shape of the normal adult mitral annulus has been shown to be important to mitral valve function. However, annular dynamics of the healthy mitral valve in children have yet to be explored. The aim of this study was to model and quantify the shape and major modes of variation of pediatric mitral valve annuli in four phases of the cardiac cycle using transthoracic echocardiography.
Methods
The mitral valve annuli of 100 children and young adults with normal findings on three-dimensional echocardiography were modeled in four different cardiac phases using the SlicerHeart extension for 3D Slicer. Annular metrics were quantified using SlicerHeart, and optimal normalization to body surface area was explored. Mean annular shapes and the principal components of variation were computed using custom code implemented in a new SlicerHeart module (Annulus Shape Analyzer). Shape was …},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The dynamic shape of the normal adult mitral annulus has been shown to be important to mitral valve function. However, annular dynamics of the healthy mitral valve in children have yet to be explored. The aim of this study was to model and quantify the shape and major modes of variation of pediatric mitral valve annuli in four phases of the cardiac cycle using transthoracic echocardiography.
Methods
The mitral valve annuli of 100 children and young adults with normal findings on three-dimensional echocardiography were modeled in four different cardiac phases using the SlicerHeart extension for 3D Slicer. Annular metrics were quantified using SlicerHeart, and optimal normalization to body surface area was explored. Mean annular shapes and the principal components of variation were computed using custom code implemented in a new SlicerHeart module (Annulus Shape Analyzer). Shape was …
Simpson, Amber L; Peoples, Jacob; Creasy, John M; Fichtinger, Gabor; Gangai, Natalie; Keshavamurthy, Krishna N; Lasso, Andras; Shia, Jinru; D’Angelica, Michael I; Do, Richard KG
Preoperative CT and survival data for patients undergoing resection of colorectal liver metastases Journal Article
In: Scientific Data, vol. 11, iss. 1, pp. 172, 2024.
@article{fichtinger2024,
title = {Preoperative CT and survival data for patients undergoing resection of colorectal liver metastases},
author = {Amber L Simpson and Jacob Peoples and John M Creasy and Gabor Fichtinger and Natalie Gangai and Krishna N Keshavamurthy and Andras Lasso and Jinru Shia and Michael I D’Angelica and Richard KG Do},
url = {https://www.nature.com/articles/s41597-024-02981-2},
year = {2024},
date = {2024-01-01},
journal = {Scientific Data},
volume = {11},
issue = {1},
pages = {172},
publisher = {Nature Publishing Group UK},
abstract = {The liver is a common site for the development of metastases in colorectal cancer. Treatment selection for patients with colorectal liver metastases (CRLM) is difficult; although hepatic resection will cure a minority of CRLM patients, recurrence is common. Reliable preoperative prediction of recurrence could therefore be a valuable tool for physicians in selecting the best candidates for hepatic resection in the treatment of CRLM. It has been hypothesized that evidence for recurrence could be found via quantitative image analysis on preoperative CT imaging of the future liver remnant before resection. To investigate this hypothesis, we have collected preoperative hepatic CT scans, clinicopathologic data, and recurrence/survival data, from a large, single-institution series of patients (n = 197) who underwent hepatic resection of CRLM. For each patient, we also created segmentations of the liver, vessels, tumors, and …},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Connolly, Laura; Fooladgar, Fahimeh; Jamzad, Amoon; Kaufmann, Martin; Syeda, Ayesha; Ren, Kevin; Abolmaesumi, Purang; Rudan, John F; McKay, Doug; Fichtinger, Gabor; others,
ImSpect: Image-driven self-supervised learning for surgical margin evaluation with mass spectrometry Journal Article
In: International Journal of Computer Assisted Radiology and Surgery, pp. 1–8, 2024.
@article{connolly2024imspect,
title = {ImSpect: Image-driven self-supervised learning for surgical margin evaluation with mass spectrometry},
author = {Laura Connolly and Fahimeh Fooladgar and Amoon Jamzad and Martin Kaufmann and Ayesha Syeda and Kevin Ren and Purang Abolmaesumi and John F Rudan and Doug McKay and Gabor Fichtinger and others},
doi = {https://doi.org/10.1007/s11548-024-03106-1},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {International Journal of Computer Assisted Radiology and Surgery},
pages = {1–8},
publisher = {Springer},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Radcliffe, Olivia; Connolly, Laura; Ungi, Tamas; Yeo, Caitlin; Rudan, John F.; Fichtinger, Gabor; Mousavi, Parvin
Navigated surgical resection cavity inspection for breast conserving surgery Proceedings
2023.
@proceedings{nokey,
title = {Navigated surgical resection cavity inspection for breast conserving surgery},
author = {Olivia Radcliffe and Laura Connolly and Tamas Ungi and Caitlin Yeo and John F. Rudan and Gabor Fichtinger and Parvin Mousavi},
doi = {https://doi.org/10.1117/12.2654015},
year = {2023},
date = {2023-04-03},
abstract = {Up to 40% of Breast Conserving Surgery (BCS) patients must undergo repeat surgery because cancer is left behind in the resection cavity. The mobility of the breast resection cavity makes it difficult to localize residual cancer and, therefore, cavity shaving is a common technique for cancer removal. Cavity shaving involves removing an additional layer of tissue from the entire resection cavity, often resulting in unnecessary healthy tissue loss. In this study, we demonstrated a navigation system and open-source software module that facilitates visualization of the breast resection cavity for targeted localization of residual cancer.},
keywords = {},
pubstate = {published},
tppubtype = {proceedings}
}
Cernelev, Pavel-Dumitru; Moga, Kristof; Groves, Leah; Haidegger, Tamás; Fichtinger, Gabor; Ungi, Tamas
Determining boundaries of accurate tracking for electromagnetic sensors Conference
SPIE, 2023.
@conference{Cernelev2023,
title = {Determining boundaries of accurate tracking for electromagnetic sensors},
author = {Pavel-Dumitru Cernelev and Kristof Moga and Leah Groves and Tamás Haidegger and Gabor Fichtinger and Tamas Ungi},
editor = {Cristian A. Linte and Jeffrey H. Siewerdsen},
doi = {10.1117/12.2654428},
year = {2023},
date = {2023-04-03},
urldate = {2023-04-03},
publisher = {SPIE},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Srikanthan, Dilakshan; Kaufmann, Martin; Jamzad, Amoon; Syeda, Ayesha; Santilli, Alice; Sedghi, Alireza; Fichtinger, Gabor; Purzner, Jamie; Rudan, John; Purzner, Teresa; Mousavi, Parvin
Attention-based multi-instance learning for improved glioblastoma detection using mass spectrometry Journal Article
In: vol. 12466, pp. 248-253, 2023.
@article{fichtinger2023i,
title = {Attention-based multi-instance learning for improved glioblastoma detection using mass spectrometry},
author = {Dilakshan Srikanthan and Martin Kaufmann and Amoon Jamzad and Ayesha Syeda and Alice Santilli and Alireza Sedghi and Gabor Fichtinger and Jamie Purzner and John Rudan and Teresa Purzner and Parvin Mousavi},
url = {https://www.spiedigitallibrary.org/conference-proceedings-of-spie/12466/1246611/Attention-based-multi-instance-learning-for-improved-glioblastoma-detection-using/10.1117/12.2654436.short},
year = {2023},
date = {2023-01-01},
volume = {12466},
pages = {248-253},
publisher = {SPIE},
abstract = {Glioblastoma Multiforme (GBM) is the most common and most lethal primary brain tumor in adults with a five-year survival rate of 5%. The current standard of care and survival rate have remained largely unchanged due to the degree of difficulty in surgically removing these tumors, which plays a crucial role in survival, as better surgical resection leads to longer survival times. Thus, novel technologies need to be identified to improve resection accuracy. Our study features a curated database of GBM and normal brain tissue specimens, which we used to train and validate a multi-instance learning model for GBM detection via rapid evaporative ionization mass spectrometry. This method enables real-time tissue typing. The specimens were collected by a surgeon, reviewed by a pathologist, and sampled with an electrocautery device. The dataset comprised 276 normal tissue burns and 321 GBM tissue burns. Our multi …},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Szabó, Róbert Zsolt; Orosz, Gábor; Ungi, Tamás; Barr, Colton; Yeung, Chris; Incze, Roland; Fichtinger, Gabor; Gál, János; Haidegger, Tamás
Automation of lung ultrasound imaging and image processing for bedside diagnostic examinations Journal Article
In: pp. 000779-000784, 2023.
@article{fichtinger2023h,
title = {Automation of lung ultrasound imaging and image processing for bedside diagnostic examinations},
author = {Róbert Zsolt Szabó and Gábor Orosz and Tamás Ungi and Colton Barr and Chris Yeung and Roland Incze and Gabor Fichtinger and János Gál and Tamás Haidegger},
url = {https://ieeexplore.ieee.org/abstract/document/10158672/},
year = {2023},
date = {2023-01-01},
pages = {000779-000784},
publisher = {IEEE},
abstract = {The causes of acute respiratory failure can be difficult to identify for physicians. Experts can differentiate these causes using bedside lung ultrasound, but lung ultrasound has a considerable learning curve. We investigate if an automated decision-support system could help novices interpret lung ultrasound scans. The system utilizes medical ultrasound, data processing, and a neural network implementation to achieve this goal. The article details the steps taken in the data preparation, and the implementation of the neural network. The best model’s accuracy and error rate are presented, along with examples of its predictions. The paper concludes with an evaluation of the results, identification of limitations, and suggestions for future improvements.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Jamzad, Amoon; Fooladgar, Fahimeh; Connolly, Laura; Srikanthan, Dilakshan; Syeda, Ayesha; Kaufmann, Martin; Ren, Kevin YM; Merchant, Shaila; Engel, Jay; Varma, Sonal; Fichtinger, Gabor; Rudan, John F; Mousavi, Parvin
Bridging Ex-Vivo Training and Intra-operative Deployment for Surgical Margin Assessment with Evidential Graph Transformer Journal Article
In: pp. 562-571, 2023.
@article{fichtinger2023g,
title = {Bridging Ex-Vivo Training and Intra-operative Deployment for Surgical Margin Assessment with Evidential Graph Transformer},
author = {Amoon Jamzad and Fahimeh Fooladgar and Laura Connolly and Dilakshan Srikanthan and Ayesha Syeda and Martin Kaufmann and Kevin YM Ren and Shaila Merchant and Jay Engel and Sonal Varma and Gabor Fichtinger and John F Rudan and Parvin Mousavi},
url = {https://link.springer.com/chapter/10.1007/978-3-031-43990-2_53},
year = {2023},
date = {2023-01-01},
pages = {562-571},
publisher = {Springer Nature Switzerland},
abstract = {PURPOSE
The use of intra-operative mass spectrometry along with Graph Transformer models showed promising results for margin detection on ex-vivo data. Although highly interpretable, these methods lack the ability to handle the uncertainty associated with intra-operative decision making. In this paper for the first time, we propose Evidential Graph Transformer network, a combination of attention mapping and uncertainty estimation to increase the performance and interpretability of surgical margin assessment.
METHODS
The Evidential Graph Transformer was formulated to output the uncertainty estimation along with intermediate attentions. The performance of the model was compared with different baselines in an ex-vivo cross-validation scheme, with extensive ablation study. The association of the model with clinical features were explored. The model was further validated for a prospective ex-vivo data, as …},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The use of intra-operative mass spectrometry along with Graph Transformer models showed promising results for margin detection on ex-vivo data. Although highly interpretable, these methods lack the ability to handle the uncertainty associated with intra-operative decision making. In this paper for the first time, we propose Evidential Graph Transformer network, a combination of attention mapping and uncertainty estimation to increase the performance and interpretability of surgical margin assessment.
METHODS
The Evidential Graph Transformer was formulated to output the uncertainty estimation along with intermediate attentions. The performance of the model was compared with different baselines in an ex-vivo cross-validation scheme, with extensive ablation study. The association of the model with clinical features were explored. The model was further validated for a prospective ex-vivo data, as …
Pose-Díez-de-la-Lastra, Alicia; Ungi, Tamas; Morton, David; Fichtinger, Gabor; Pascau, Javier
Real-time integration between Microsoft HoloLens 2 and 3D Slicer with demonstration in pedicle screw placement planning Journal Article
In: International Journal of Computer Assisted Radiology and Surgery, vol. 18, iss. 11, pp. 2023-2032, 2023.
@article{fichtinger2023f,
title = {Real-time integration between Microsoft HoloLens 2 and 3D Slicer with demonstration in pedicle screw placement planning},
author = {Alicia Pose-Díez-de-la-Lastra and Tamas Ungi and David Morton and Gabor Fichtinger and Javier Pascau},
url = {https://link.springer.com/article/10.1007/s11548-023-02977-0},
year = {2023},
date = {2023-01-01},
journal = {International Journal of Computer Assisted Radiology and Surgery},
volume = {18},
issue = {11},
pages = {2023-2032},
publisher = {Springer International Publishing},
abstract = {Purpose
Up to date, there has been a lack of software infrastructure to connect 3D Slicer to any augmented reality (AR) device. This work describes a novel connection approach using Microsoft HoloLens 2 and OpenIGTLink, with a demonstration in pedicle screw placement planning.
Methods
We developed an AR application in Unity that is wirelessly rendered onto Microsoft HoloLens 2 using Holographic Remoting. Simultaneously, Unity connects to 3D Slicer using the OpenIGTLink communication protocol. Geometrical transform and image messages are transferred between both platforms in real time. Through the AR glasses, a user visualizes a patient’s computed tomography overlaid onto virtual 3D models showing anatomical structures. We technically evaluated the system by measuring message transference latency between the platforms. Its functionality was assessed in pedicle screw placement planning …},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Up to date, there has been a lack of software infrastructure to connect 3D Slicer to any augmented reality (AR) device. This work describes a novel connection approach using Microsoft HoloLens 2 and OpenIGTLink, with a demonstration in pedicle screw placement planning.
Methods
We developed an AR application in Unity that is wirelessly rendered onto Microsoft HoloLens 2 using Holographic Remoting. Simultaneously, Unity connects to 3D Slicer using the OpenIGTLink communication protocol. Geometrical transform and image messages are transferred between both platforms in real time. Through the AR glasses, a user visualizes a patient’s computed tomography overlaid onto virtual 3D models showing anatomical structures. We technically evaluated the system by measuring message transference latency between the platforms. Its functionality was assessed in pedicle screw placement planning …
Whyne, Cari M; Underwood, Grace; Davidson, Sean RH; Robert, Normand; Huang, Christine; Akens, Margarete K; Fichtinger, Gabor; Yee, Albert JM; Hardisty, Michael
Development and validation of a radiofrequency ablation treatment planning system for vertebral metastases Journal Article
In: International Journal of Computer Assisted Radiology and Surgery, vol. 18, iss. 12, pp. 2339-2347, 2023.
@article{fichtinger2023e,
title = {Development and validation of a radiofrequency ablation treatment planning system for vertebral metastases},
author = {Cari M Whyne and Grace Underwood and Sean RH Davidson and Normand Robert and Christine Huang and Margarete K Akens and Gabor Fichtinger and Albert JM Yee and Michael Hardisty},
url = {https://link.springer.com/article/10.1007/s11548-023-02952-9},
year = {2023},
date = {2023-01-01},
journal = {International Journal of Computer Assisted Radiology and Surgery},
volume = {18},
issue = {12},
pages = {2339-2347},
publisher = {Springer International Publishing},
abstract = {Purpose
Bone-targeted radiofrequency ablation (RFA) is widely used in the treatment of vertebral metastases. While radiation therapy utilizes established treatment planning systems (TPS) based on multimodal imaging to optimize treatment volumes, current RFA of vertebral metastases has been limited to qualitative image-based assessment of tumour location to direct probe selection and access. This study aimed to design, develop and evaluate a computational patient-specific RFA TPS for vertebral metastases.
Methods
A TPS was developed on the open-source 3D slicer platform, including procedural setup, dose calculation (based on finite element modelling), and analysis/visualization modules. Usability testing was carried out by 7 clinicians involved in the treatment of vertebral metastases on retrospective clinical imaging data using a simplified dose calculation engine. In vivo evaluation was performed in a …},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Bone-targeted radiofrequency ablation (RFA) is widely used in the treatment of vertebral metastases. While radiation therapy utilizes established treatment planning systems (TPS) based on multimodal imaging to optimize treatment volumes, current RFA of vertebral metastases has been limited to qualitative image-based assessment of tumour location to direct probe selection and access. This study aimed to design, develop and evaluate a computational patient-specific RFA TPS for vertebral metastases.
Methods
A TPS was developed on the open-source 3D slicer platform, including procedural setup, dose calculation (based on finite element modelling), and analysis/visualization modules. Usability testing was carried out by 7 clinicians involved in the treatment of vertebral metastases on retrospective clinical imaging data using a simplified dose calculation engine. In vivo evaluation was performed in a …
Orosz, Gábor; Szabó, Róbert Zsolt; Ungi, Tamás; Barr, Colton; Yeung, Chris; Fichtinger, Gábor; Gál, János; Haidegger, Tamás
Lung Ultrasound Imaging and Image Processing with Artificial Intelligence Methods for Bedside Diagnostic Examinations Journal Article
In: Acta Polytechnica Hungarica, vol. 20, iss. 8, 2023.
@article{fichtinger2023d,
title = {Lung Ultrasound Imaging and Image Processing with Artificial Intelligence Methods for Bedside Diagnostic Examinations},
author = {Gábor Orosz and Róbert Zsolt Szabó and Tamás Ungi and Colton Barr and Chris Yeung and Gábor Fichtinger and János Gál and Tamás Haidegger},
url = {https://acta.uni-obuda.hu/Orosz_Szabo_Ungi_Barr_Yeung_Fichtinger_Gal_Haidegger_137.pdf},
year = {2023},
date = {2023-01-01},
journal = {Acta Polytechnica Hungarica},
volume = {20},
issue = {8},
abstract = {Artificial Intelligence-assisted radiology has shown to offer significant benefits in clinical care. Physicians often face challenges in identifying the underlying causes of acute respiratory failure. One method employed by experts is the utilization of bedside lung ultrasound, although it has a significant learning curve. In our study, we explore the potential of a Machine Learning-based automated decision-support system to assist inexperienced practitioners in interpreting lung ultrasound scans. This system incorporates medical ultrasound, advanced data processing techniques, and a neural network implementation to achieve its objective. The article provides a comprehensive overview of the steps involved in data preparation and the implementation of the neural network. The accuracy and error rate of the most effective model are presented, accompanied by illustrative examples of their predictions. Furthermore, the paper concludes with an evaluation of the results, identification of limitations, and recommendations for future enhancements.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Nam, Hannah H; Flynn, Maura; Lasso, Andras; Herz, Christian; Sabin, Patricia; Wang, Yan; Cianciulli, Alana; Vigil, Chad; Huang, Jing; Vicory, Jared; Paniagua, Beatriz; Allemang, David; Goldberg, David J; Nuri, Mohammed; Cohen, Meryl S; Fichtinger, Gabor; Jolley, Matthew A
Modeling of the tricuspid valve and right ventricle in hypoplastic left heart syndrome with a Fontan circulation Journal Article
In: Circulation: Cardiovascular Imaging, vol. 16, iss. 3, pp. e014671, 2023.
@article{fichtinger2023c,
title = {Modeling of the tricuspid valve and right ventricle in hypoplastic left heart syndrome with a Fontan circulation},
author = {Hannah H Nam and Maura Flynn and Andras Lasso and Christian Herz and Patricia Sabin and Yan Wang and Alana Cianciulli and Chad Vigil and Jing Huang and Jared Vicory and Beatriz Paniagua and David Allemang and David J Goldberg and Mohammed Nuri and Meryl S Cohen and Gabor Fichtinger and Matthew A Jolley},
url = {https://www.ahajournals.org/doi/abs/10.1161/CIRCIMAGING.122.014671},
year = {2023},
date = {2023-01-01},
journal = {Circulation: Cardiovascular Imaging},
volume = {16},
issue = {3},
pages = {e014671},
publisher = {Lippincott Williams & Wilkins},
abstract = {Background
In hypoplastic left heart syndrome, tricuspid regurgitation (TR) is associated with circulatory failure and death. We hypothesized that the tricuspid valve (TV) structure of patients with hypoplastic left heart syndrome with a Fontan circulation and moderate or greater TR differs from those with mild or less TR, and that right ventricle volume is associated with TV structure and dysfunction.
Methods
TV of 100 patients with hypoplastic left heart syndrome and a Fontan circulation were modeled using transthoracic 3-dimensional echocardiograms and custom software in SlicerHeart. Associations of TV structure to TR grade and right ventricle function and volume were investigated. Shape parameterization and analysis was used to calculate the mean shape of the TV leaflets, their principal modes of variation, and to characterize associations of TV leaflet shape to TR.
Results
In univariate modeling, patients with …},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
In hypoplastic left heart syndrome, tricuspid regurgitation (TR) is associated with circulatory failure and death. We hypothesized that the tricuspid valve (TV) structure of patients with hypoplastic left heart syndrome with a Fontan circulation and moderate or greater TR differs from those with mild or less TR, and that right ventricle volume is associated with TV structure and dysfunction.
Methods
TV of 100 patients with hypoplastic left heart syndrome and a Fontan circulation were modeled using transthoracic 3-dimensional echocardiograms and custom software in SlicerHeart. Associations of TV structure to TR grade and right ventricle function and volume were investigated. Shape parameterization and analysis was used to calculate the mean shape of the TV leaflets, their principal modes of variation, and to characterize associations of TV leaflet shape to TR.
Results
In univariate modeling, patients with …
Kitner, Nicole; Rodgers, Jessica R; Ungi, Tamas; Korzeniowski, Martin; Olding, Timothy; Mousavi, Parvin; Fichtinger, Gabor
Multi-catheter modelling in reconstructed 3D transrectal ultrasound images from prostate brachytherapy Journal Article
In: vol. 12466, pp. 126-135, 2023.
@article{fichtinger2023b,
title = {Multi-catheter modelling in reconstructed 3D transrectal ultrasound images from prostate brachytherapy},
author = {Nicole Kitner and Jessica R Rodgers and Tamas Ungi and Martin Korzeniowski and Timothy Olding and Parvin Mousavi and Gabor Fichtinger},
url = {https://www.spiedigitallibrary.org/conference-proceedings-of-spie/12466/124660I/Multi-catheter-modelling-in-reconstructed-3D-transrectal-ultrasound-images-from/10.1117/12.2654019.short},
year = {2023},
date = {2023-01-01},
volume = {12466},
pages = {126-135},
publisher = {SPIE},
abstract = {High-dose-rate brachytherapy is an accepted standard-of-care treatment for prostate cancer. In this procedure, catheters are inserted using three-dimensional (3D) transrectal ultrasound image-guidance. Their positions are manually segmented for treatment planning and delivery. The transverse ultrasound sweep, which is subject to tip and depth error for catheter localization, is a commonly used ultrasound imaging option available for image acquisition. We propose a two-step pipeline that uses a deep-learning network and curve fitting to automatically localize and model catheters in transversely reconstructed 3D ultrasound images. In the first step, a 3D U-Net was trained to automatically segment all catheters in a 3D ultrasound image. Following this step, curve fitting was implemented to detect the shapes of individual catheters using polynomial fitting. Of the 343 catheters (from 20 patients) in the testing data, the …},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Kaufmann, Martin; Iaboni, Natasha; Jamzad, Amoon; Hurlbut, David; Ren, Kevin Yi Mi; Rudan, John F; Mousavi, Parvin; Fichtinger, Gabor; Varma, Sonal; Caycedo-Marulanda, Antonio; Nicol, Christopher JB
In: Metabolites, vol. 13, iss. 4, pp. 508, 2023.
@article{fichtinger2023,
title = {Metabolically active zones involving fatty acid elongation delineated by DESI-MSI correlate with pathological and prognostic features of colorectal cancer},
author = {Martin Kaufmann and Natasha Iaboni and Amoon Jamzad and David Hurlbut and Kevin Yi Mi Ren and John F Rudan and Parvin Mousavi and Gabor Fichtinger and Sonal Varma and Antonio Caycedo-Marulanda and Christopher JB Nicol},
url = {https://www.mdpi.com/2218-1989/13/4/508},
year = {2023},
date = {2023-01-01},
journal = {Metabolites},
volume = {13},
issue = {4},
pages = {508},
publisher = {MDPI},
abstract = {Colorectal cancer (CRC) is the second leading cause of cancer deaths. Despite recent advances, five-year survival rates remain largely unchanged. Desorption electrospray ionization mass spectrometry imaging (DESI) is an emerging nondestructive metabolomics-based method that retains the spatial orientation of small-molecule profiles on tissue sections, which may be validated by ‘gold standard’ histopathology. In this study, CRC samples were analyzed by DESI from 10 patients undergoing surgery at Kingston Health Sciences Center. The spatial correlation of the mass spectral profiles was compared with histopathological annotations and prognostic biomarkers. Fresh frozen sections of representative colorectal cross sections and simulated endoscopic biopsy samples containing tumour and non-neoplastic mucosa for each patient were generated and analyzed by DESI in a blinded fashion. Sections were then hematoxylin and eosin (H and E) stained, annotated by two independent pathologists, and analyzed. Using PCA/LDA-based models, DESI profiles of the cross sections and biopsies achieved 97% and 75% accuracies in identifying the presence of adenocarcinoma, using leave-one-patient-out cross validation. Among the m/z ratios exhibiting the greatest differential abundance in adenocarcinoma were a series of eight long-chain or very-long-chain fatty acids, consistent with molecular and targeted metabolomics indicators of de novo lipogenesis in CRC tissue. Sample stratification based on the presence of lympovascular invasion (LVI), a poor CRC prognostic indicator, revealed the abundance of oxidized phospholipids, suggestive …},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Fooladgar, Fahimeh; Jamzad, Amoon; Connolly, Laura; Santilli, Alice; Kaufmann, Martin; Ren, Kevin; Abolmaesumi, Purang; Rudan, John; McKay, Doug; Fichtinger, Gabor; Mousavi, Parvin
Uncertainty estimation for margin detection in cancer surgery using mass spectrometry Journal Article
In: International Journal of Computer Assisted Radiology and Surgery, 2022.
@article{Fooladgar2022,
title = {Uncertainty estimation for margin detection in cancer surgery using mass spectrometry},
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},
doi = {https://doi.org/10.1007/s11548-022-02764-3},
year = {2022},
date = {2022-09-01},
journal = {International Journal of Computer Assisted Radiology and Surgery},
keywords = {},
pubstate = {published},
tppubtype = {article}
}