Publications

@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 …},

keywords = {},

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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pubstate = {published},

tppubtype = {article}

}

@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}

}

@article{yeung2024,

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},

urldate = {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}

}

@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}

}

@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 …},

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pubstate = {published},

tppubtype = {article}

}

@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 …},

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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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pubstate = {published},

tppubtype = {article}

}

@article{fichtinger2023x,

title = {Tracked tissue sensing for tumor bed inspection},

author = {David Morton and Laura Connolly and Leah Groves and Kyle Sunderland and Amoon Jamzad and John F Rudan and Gabor Fichtinger and Tamas Ungi and Parvin Mousavi},

url = {https://www.spiedigitallibrary.org/conference-proceedings-of-spie/12466/124661K/Tracked-tissue-sensing-for-tumor-bed-inspection/10.1117/12.2654217.short},

year  = {2023},

date = {2023-01-01},

volume = {12466},

pages = {378-385},

publisher = {SPIE},

abstract = {Up to 30% of breast-conserving surgery patients require secondary surgery to remove cancerous tissue missed in the initial intervention. We hypothesize that tracked tissue sensing can improve the success rate of breast-conserving surgery. Tissue sensor tracking allows the surgeon to intraoperatively scan the tumor bed for leftover cancerous tissue. In this study, we characterize the performance of our tracked optical scanning testbed using an experimental pipeline. We assess the Dice similarity coefficient, accuracy, and latency of the testbed.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{fichtinger2023w,

title = {Analyzing colonoscopy training learning curves using comparative hand tracking assessment},

author = {Keiran Barr and Lawrence Hookey and Tamas Ungi and Gabor Fichtinger and Matthew Holden},

url = {https://www.spiedigitallibrary.org/conference-proceedings-of-spie/12466/124661Y/Analyzing-colonoscopy-training-learning-curves-using-comparative-hand-tracking-assessment/10.1117/12.2654309.short},

year  = {2023},

date = {2023-01-01},

volume = {12466},

pages = {466-472},

publisher = {SPIE},

abstract = {A competency-based approach for colonoscopy training is particularly important, since the amount of practice required for proficiency varies widely between trainees. Though numerous objective proficiency assessment frameworks have been validated in the literature, these frameworks rely on expert observers. This process is time-consuming, and as a result, there has been increased interest in automated proficiency rating of colonoscopies. This work aims to investigate sixteen automatically computed performance metrics, and whether they can measure improvements in novices following a series of practice attempts. This involves calculating motion-tracking parameters for three groups: untrained novices, those same novices after undergoing training exercises, and experts. Both groups had electromagnetic tracking markers fixed to their hands and the scope tip. Each participant performed eight testing …},

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tppubtype = {article}

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@article{fichtinger2023v,

title = {Determining the time-delay of a mass spectrometry-based tissue sensor},

author = {J Ehrlich and C Yeung and M Kaufman and A Jamzad and J Rudan and P Mousavi and G Fichtinger and T Ungi},

url = {https://www.spiedigitallibrary.org/conference-proceedings-of-spie/12466/124661D/Determining-the-time-delay-of-a-mass-spectrometry-based-tissue/10.1117/12.2654359.short},

year  = {2023},

date = {2023-01-01},

volume = {12466},

pages = {324-327},

publisher = {SPIE},

abstract = {Breast cancer commonly requires surgical treatment. A procedure used to remove breast cancer is lumpectomy, which removes a minimal healthy tissue margin surrounding the tumor, called a negative margin. A cancer-free margin is difficult to achieve because tumors are not visible or palpable, and the breast deforms during surgery. One notable solution is Rapid Evaporative Ionization Mass Spectrometry (REIMS), which differentiates tumor from healthy tissue with high accuracy from the vapor generated by the surgical cautery. REIMS combined with navigation could detect where the surgical cautery breaches tumor tissue. However, fusing position tracking and REIMS data for navigation is challenging. REIMS has a time-delay dependent on a series of factors. Our objective was to evaluate REIMS time-delay for surgical navigation. The average time-delay of REIMS classifications was measured by video …},

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tppubtype = {article}

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