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 Proceedings Article
In: pp. 248-253, SPIE, 2023.
@inproceedings{srikanthan2023,
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},
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 …},
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pubstate = {published},
tppubtype = {inproceedings}
}
Morton, David; Connolly, Laura; Groves, Leah; Sunderland, Kyle; Jamzad, Amoon; Rudan, John F; Fichtinger, Gabor; Ungi, Tamas; Mousavi, Parvin
Tracked tissue sensing for tumor bed inspection Proceedings Article
In: pp. 378-385, SPIE, 2023.
@inproceedings{morton2023,
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},
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 = {inproceedings}
}
Elmi, Hanad; Jamzad, Amoon; Sharp, Mackenzie; Rodgers, Jessica R; Kaufmann, Martin; Jamaspishvili, Tamara; Iseman, Rachael; Berman, David; Rudan, J; Fichtinger, Gabor; Mousavi, Parvin
ViPRE: an open-source software implementation for end-to-end analysis of mass spectrometry data Proceedings Article
In: pp. 487-494, SPIE, 2023.
@inproceedings{elmi2023,
title = {ViPRE: an open-source software implementation for end-to-end analysis of mass spectrometry data},
author = {Hanad Elmi and Amoon Jamzad and Mackenzie Sharp and Jessica R Rodgers and Martin Kaufmann and Tamara Jamaspishvili and Rachael Iseman and David Berman and J Rudan and Gabor Fichtinger and Parvin Mousavi},
year = {2023},
date = {2023-01-01},
volume = {12466},
pages = {487-494},
publisher = {SPIE},
abstract = {Mass Spectrometry Imaging (MSI) is a powerful tool capable of visualizing molecular patterns to identify disease markers in tissue analysis. However, data analysis is computationally heavy and currently time-consuming as there is no single platform capable of performing the entire preprocessing, visualization, and analysis pipeline end-to-end. Using different software tools and file formats required for such tools also makes the process prone to error. The purpose of this work is to develop a free, open-source software implementation called “Visualization, Preprocessing, and Registration Environment” (ViPRE), capable of end-to-end analysis of MSI data. ViPRE was developed to provide various functionalities required for MSI analysis including data import, data visualization, data registration, Region of Interest (ROI) selection, spectral data alignment and data analysis. The software implementation is offered as an …},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Ndiaye, Fatou Bintou; Groves, Leah; Hisey, Rebecca; Ungi, Tamas; Diop, Idy; Mousavi, Parvin; Fichtinger, Gabor; Camara, Mamadou Samba
Desing and realization of a computer-assisted nephrostomy guidance system Journal Article
In: pp. 1-6, 2023.
@article{ndiaye2023,
title = {Desing and realization of a computer-assisted nephrostomy guidance system},
author = {Fatou Bintou Ndiaye and Leah Groves and Rebecca Hisey and Tamas Ungi and Idy Diop and Parvin Mousavi and Gabor Fichtinger and Mamadou Samba Camara},
year = {2023},
date = {2023-01-01},
pages = {1-6},
publisher = {IEEE},
abstract = {Background and purpose
Nowadays, computerized nephrostomy techniques exist. Although relatively safe, several factors make it difficult for inexperienced users. A computer-assisted nephrostomy guidance system has been studied to increase the success rate of this intervention and reduce the work and difficulties encountered by the actors.
Methods
To design the system, two methods will be studied. Following this study, this system was designed based on method 2. SmartSysNephro is composed of a hardware part whose manipulations made by the user are visualized and assisted by the computer. This nephrostomy procedure that the user simulates is monitored by webcam. Using the data from this Intel Real Sense webcam, allowed to propose a CNN YOLO model.
Results
The results obtained show that the objectives set have been achieved globally. The SmartSysNephro system gives real time warning …},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Nowadays, computerized nephrostomy techniques exist. Although relatively safe, several factors make it difficult for inexperienced users. A computer-assisted nephrostomy guidance system has been studied to increase the success rate of this intervention and reduce the work and difficulties encountered by the actors.
Methods
To design the system, two methods will be studied. Following this study, this system was designed based on method 2. SmartSysNephro is composed of a hardware part whose manipulations made by the user are visualized and assisted by the computer. This nephrostomy procedure that the user simulates is monitored by webcam. Using the data from this Intel Real Sense webcam, allowed to propose a CNN YOLO model.
Results
The results obtained show that the objectives set have been achieved globally. The SmartSysNephro system gives real time warning …
Fooladgar, Fahimeh; to, Minh Nguyen Nhat; Javadi, Golara; Sojoudi, Samira; Eshumani, Walid; Chang, Silvia; Black, Peter; Mousavi, Parvin; Abolmaesumi, Purang
Semi-supervised learning from coarse histopathology labels Journal Article
In: Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, vol. 11, no. 4, pp. 1143-1150, 2023.
@article{fooladgar2023,
title = {Semi-supervised learning from coarse histopathology labels},
author = {Fahimeh Fooladgar and Minh Nguyen Nhat to and Golara Javadi and Samira Sojoudi and Walid Eshumani and Silvia Chang and Peter Black and Parvin Mousavi and Purang Abolmaesumi},
year = {2023},
date = {2023-01-01},
journal = {Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization},
volume = {11},
number = {4},
pages = {1143-1150},
publisher = {Taylor & Francis},
abstract = {Ultrasound imaging is commonly used to guide sampling the prostate tissue in transrectal biopsies, followed by detection of cancer through histopathological analysis and coarse labelling of sampled tissue. Ideally, the procedure should be improved by developing machine learning solutions that can identify the presence of cancer in ultrasound images to guide the biopsy procedure. Training a fully supervised learning model using coarse histopathology labels suffers from weakly annotated data which introduce label noise for each image pixel. To address this challenge, we propose a semi-supervised framework for learning with noisy labels. We leverage a two-component mixture model to cluster the training data into clean and noisy label samples based on their loss values. Then, during the semi-supervised training phase, we utilise the well-known MixMatch algorithm which incorporates consistency …},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Zhou, Meng; Jamzad, Amoon; Izard, Jason; Menard, Alexandre; Siemens, Robert; Mousavi, Parvin
Domain Transfer Through Image-to-Image Translation for Uncertainty-Aware Prostate Cancer Classification Journal Article
In: arXiv preprint arXiv:2307.00479, 2023.
@article{zhou2023,
title = {Domain Transfer Through Image-to-Image Translation for Uncertainty-Aware Prostate Cancer Classification},
author = {Meng Zhou and Amoon Jamzad and Jason Izard and Alexandre Menard and Robert Siemens and Parvin Mousavi},
year = {2023},
date = {2023-01-01},
journal = {arXiv preprint arXiv:2307.00479},
abstract = {Prostate Cancer (PCa) is often diagnosed using High-resolution 3.0 Tesla(T) MRI, which has been widely established in clinics. However, there are still many medical centers that use 1.5T MRI units in the actual diagnostic process of PCa. In the past few years, deep learning-based models have been proven to be efficient on the PCa classification task and can be successfully used to support radiologists during the diagnostic process. However, training such models often requires a vast amount of data, and sometimes it is unobtainable in practice. Additionally, multi-source MRIs can pose challenges due to cross-domain distribution differences. In this paper, we have presented a novel approach for unpaired image-to-image translation of prostate mp-MRI for classifying clinically significant PCa, to be applied in data-constrained settings. First, we introduce domain transfer, a novel pipeline to translate unpaired 3.0T multi-parametric prostate MRIs to 1.5T, to increase the number of training data. Second, we estimate the uncertainty of our models through an evidential deep learning approach; and leverage the dataset filtering technique during the training process. Furthermore, we introduce a simple, yet efficient Evidential Focal Loss that incorporates the focal loss with evidential uncertainty to train our model. Our experiments demonstrate that the proposed method significantly improves the Area Under ROC Curve (AUC) by over 20% compared to the previous work (98.4% vs. 76.2%). We envision that providing prediction uncertainty to radiologists may help them focus more on uncertain cases and thus expedite the diagnostic process effectively. Our …},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Gilany, Mahdi; Wilson, Paul; Perera-Ortega, Andrea; Jamzad, Amoon; To, Minh Nguyen Nhat; Fooladgar, Fahimeh; Wodlinger, Brian; Abolmaesumi, Purang; Mousavi, Parvin
TRUSformer: Improving prostate cancer detection from micro-ultrasound using attention and self-supervision Journal Article
In: International Journal of Computer Assisted Radiology and Surgery, vol. 18, no. 7, pp. 1193-1200, 2023.
@article{gilany2023,
title = {TRUSformer: Improving prostate cancer detection from micro-ultrasound using attention and self-supervision},
author = {Mahdi Gilany and Paul Wilson and Andrea Perera-Ortega and Amoon Jamzad and Minh Nguyen Nhat To and Fahimeh Fooladgar and Brian Wodlinger and Purang Abolmaesumi and Parvin Mousavi},
year = {2023},
date = {2023-01-01},
journal = {International Journal of Computer Assisted Radiology and Surgery},
volume = {18},
number = {7},
pages = {1193-1200},
publisher = {Springer International Publishing},
abstract = {Purpose
A large body of previous machine learning methods for ultrasound-based prostate cancer detection classify small regions of interest (ROIs) of ultrasound signals that lie within a larger needle trace corresponding to a prostate tissue biopsy (called biopsy core). These ROI-scale models suffer from weak labeling as histopathology results available for biopsy cores only approximate the distribution of cancer in the ROIs. ROI-scale models do not take advantage of contextual information that are normally considered by pathologists, i.e., they do not consider information about surrounding tissue and larger-scale trends when identifying cancer. We aim to improve cancer detection by taking a multi-scale, i.e., ROI-scale and biopsy core-scale, approach.
Methods
Our multi-scale approach combines (i) an “ROI-scale” model trained using self-supervised learning to extract features from small ROIs and (ii) a “core-scale …},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
A large body of previous machine learning methods for ultrasound-based prostate cancer detection classify small regions of interest (ROIs) of ultrasound signals that lie within a larger needle trace corresponding to a prostate tissue biopsy (called biopsy core). These ROI-scale models suffer from weak labeling as histopathology results available for biopsy cores only approximate the distribution of cancer in the ROIs. ROI-scale models do not take advantage of contextual information that are normally considered by pathologists, i.e., they do not consider information about surrounding tissue and larger-scale trends when identifying cancer. We aim to improve cancer detection by taking a multi-scale, i.e., ROI-scale and biopsy core-scale, approach.
Methods
Our multi-scale approach combines (i) an “ROI-scale” model trained using self-supervised learning to extract features from small ROIs and (ii) a “core-scale …
Groves, Leah A; Keita, Mohamed; Talla, Saidou; Kikinis, Ron; Fichtinger, Gabor; Mousavi, Parvin; Camara, Mamadou
A review of low-cost ultrasound compatible phantoms Journal Article
In: vol. 70, no. 12, pp. 3436-3448, 2023.
@article{groves2023,
title = {A review of low-cost ultrasound compatible phantoms},
author = {Leah A Groves and Mohamed Keita and Saidou Talla and Ron Kikinis and Gabor Fichtinger and Parvin Mousavi and Mamadou Camara},
year = {2023},
date = {2023-01-01},
volume = {70},
number = {12},
pages = {3436-3448},
publisher = {IEEE},
abstract = {Ultrasound-compatible phantoms are used to develop novel US-based systems and train simulated medical interventions. The price difference between lab-made and commercially available ultrasound-compatible phantoms lead to the publication of many papers categorized as low-cost in the literature. The aim of this review was to improve the phantom selection process by summarizing the pertinent literature. We compiled papers on US-compatible spine, prostate, vascular, breast, kidney, and li ver phantoms. We reviewed papers for cost and accessibility, providing an overview of the materials, construction time, shelf life, needle insertion limits, and manufacturing and evaluation methods. This information was summarized by anatomy. The clinical application associated with each phantom was also reported for those interested in a particular intervention. Techniques and common practices for building low-cost …},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
March, Lucas; Rodgers, Jessica R; Hisey, Rebecca; Jamzad, Amoon; Santilli, Alice ML; McKay, Doug; Rudan, John F; Kaufmann, Martin; Ren, Kevin Yi Mi; Fichtinger, Gabor; Mousavi, Parvin
Cautery tool state detection using deep learning on intraoperative surgery videos Proceedings Article
In: pp. 89-95, SPIE, 2023.
@inproceedings{march2023,
title = {Cautery tool state detection using deep learning on intraoperative surgery videos},
author = {Lucas March and Jessica R Rodgers and Rebecca Hisey and Amoon Jamzad and Alice ML Santilli and Doug McKay and John F Rudan and Martin Kaufmann and Kevin Yi Mi Ren and Gabor Fichtinger and Parvin Mousavi},
year = {2023},
date = {2023-01-01},
volume = {12466},
pages = {89-95},
publisher = {SPIE},
abstract = {Treatment for Basal Cell Carcinoma (BCC) includes an excisional surgery to remove cancerous tissues, using a cautery tool to make burns along a defined resection margin around the tumor. Margin evaluation occurs post-surgically, requiring repeat surgery if positive margins are detected. Rapid Evaporative Ionization Mass Spectrometry (REIMS) can help distinguish healthy and cancerous tissue but does not provide spatial information about the cautery tool location where the spectra are acquired. We propose using intraoperative surgical video recordings and deep learning to provide surgeons with guidance to locate sites of potential positive margins. Frames from 14 intraoperative videos of BCC surgery were extracted and used to train a sequence of networks. The first network extracts frames showing surgery in-progress, then, an object detection network localizes the cautery tool and resection margin. Finally …},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Yeung, Chris; Ehrlich, Joshua; Jamzad, Amoon; Kaufmann, Martin; Rudan, John; Engel, Cecil Jay; Mousavi, Parvin; Ungi, Tamas; Fichtinger, Gabor
Cautery trajectory analysis for evaluation of resection margins in breast-conserving surgery Proceedings Article
In: pp. 495-501, SPIE, 2023.
@inproceedings{yeung2023,
title = {Cautery trajectory analysis for evaluation of resection margins in breast-conserving surgery},
author = {Chris Yeung and Joshua Ehrlich and Amoon Jamzad and Martin Kaufmann and John Rudan and Cecil Jay Engel and Parvin Mousavi and Tamas Ungi and Gabor Fichtinger},
year = {2023},
date = {2023-01-01},
volume = {12466},
pages = {495-501},
publisher = {SPIE},
abstract = {After breast-conserving surgery, positive margins occur when breast cancer cells are found on the resection margin, leading to a higher chance of recurrence and the need for repeat surgery. The NaviKnife is an electromagnetic tracking-based surgical navigation system that helps to provide visual and spatial feedback to the surgeon. In this study, we conduct a gross evaluation of this navigation system with respect to resection margins. The trajectory of the surgical cautery relative to ultrasound-visible tumor will be visualized, and its distance and location from the tumor will be compared with pathology reports. Six breast-conserving surgery cases that resulted in positive margins were performed using the NaviKnife system. Trackers were placed on the surgical tools and their positions in three-dimensional space were recorded throughout the procedure. The closest distance between the cautery and the tumor …},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Syeda, Ayesha; Fooladgar, Fahimeh; Jamzad, Amoon; Srikanthan, Dilakshan; Kaufmann, Martin; Ren, Kevin; Engel, Jay; Walker, Ross; Merchant, Shaila; McKay, Doug; Varma, Sonal; Fichtinger, Gabor; Rudan, John; Mousavi, Parvin
Self-supervised learning and uncertainty estimation for surgical margin detection Proceedings Article
In: pp. 76-83, SPIE, 2023.
@inproceedings{syeda2023,
title = {Self-supervised learning and uncertainty estimation for surgical margin detection},
author = {Ayesha Syeda and Fahimeh Fooladgar and Amoon Jamzad and Dilakshan Srikanthan and Martin Kaufmann and Kevin Ren and Jay Engel and Ross Walker and Shaila Merchant and Doug McKay and Sonal Varma and Gabor Fichtinger and John Rudan and Parvin Mousavi},
year = {2023},
date = {2023-01-01},
volume = {12466},
pages = {76-83},
publisher = {SPIE},
abstract = {Up to 35% of breast-conserving surgeries fail to resect all the tumors completely. Ideally, machine learning methods using the iKnife data, which uses Rapid Evaporative Ionization Mass Spectrometry (REIMS), can be utilized to predict tissue type in real-time during surgery, resulting in better tumor resections. As REIMS data is heterogeneous and weakly labeled, and datasets are often small, model performance and reliability can be adversely affected. Self-supervised training and uncertainty estimation of the prediction can be used to mitigate these challenges by learning the signatures of input data without their label as well as including predictive confidence in output reporting. We first design an autoencoder model using a reconstruction pretext task as a self-supervised pretraining step without considering tissue type. Next, we construct our uncertainty-aware classifier using the encoder part of the model with …},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
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 Article
In: pp. 234-241, SPIE, 2023.
@inproceedings{radcliffe2023,
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},
year = {2023},
date = {2023-01-01},
volume = {12466},
pages = {234-241},
publisher = {SPIE},
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 = {inproceedings}
}
Ehrlich, Josh; Yeung, Chris; Kaufman, Martin; Jamzad, Amoon; Rudan, J; Mousavi, Parvin; Fichtinger, Gabor; Ungi, Tamas
Determining the time-delay of a mass spectrometry-based tissue sensor Proceedings Article
In: pp. 324-327, SPIE, 2023.
@inproceedings{ehrlich2023,
title = {Determining the time-delay of a mass spectrometry-based tissue sensor},
author = {Josh Ehrlich and Chris Yeung and Martin Kaufman and Amoon Jamzad and J Rudan and Parvin Mousavi and Gabor Fichtinger and Tamas Ungi},
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 …},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Greenspan, Hayit; Madabhushi, Anant; Mousavi, Parvin; Salcudean, Septimiu; Duncan, James; Syeda-Mahmood, Tanveer; Taylor, Russell
Medical Image Computing and Computer Assisted Intervention–MICCAI 2023: 26th International Conference, Vancouver, BC, Canada, October 8–12, 2023, Proceedings, Part V Journal Article
In: vol. 14224, 2023.
@article{greenspan2023,
title = {Medical Image Computing and Computer Assisted Intervention–MICCAI 2023: 26th International Conference, Vancouver, BC, Canada, October 8–12, 2023, Proceedings, Part V},
author = {Hayit Greenspan and Anant Madabhushi and Parvin Mousavi and Septimiu Salcudean and James Duncan and Tanveer Syeda-Mahmood and Russell Taylor},
year = {2023},
date = {2023-01-01},
volume = {14224},
publisher = {Springer Nature},
abstract = {The ten-volume set LNCS 14220, 14221, 14222, 14223, 14224, 14225, 14226, 14227, 14228, and 14229 constitutes the refereed proceedings of the 26th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2023, which was held in Vancouver, Canada, in October 2023. The 730 revised full papers presented were carefully reviewed and selected from a total of 2250 submissions. The papers are organized in the following topical sections: Part I: Machine learning with limited supervision and machine learning–transfer learning; Part II: Machine learning–learning strategies; machine learning–explainability, bias, and uncertainty; Part III: Machine learning–explainability, bias and uncertainty; image segmentation; Part IV: Image segmentation; Part V: Computer-aided diagnosis; Part VI: Computer-aided diagnosis; computational pathology; Part VII: Clinical applications–abdomen; clinical applications–breast; clinical applications–cardiac; clinical applications–dermatology; clinical applications–fetal imaging; clinical applications–lung; clinical applications–musculoskeletal; clinical applications–oncology; clinical applications–ophthalmology; clinical applications–vascular; Part VIII: Clinical applications–neuroimaging; microscopy; Part IX: Image-guided intervention, surgical planning, and data science; Part X: Image reconstruction and image registration.},
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 Proceedings Article
In: pp. 562-571, Springer Nature Switzerland, 2023.
@inproceedings{jamzad2023,
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},
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 = {inproceedings}
}
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 …
Tomalty, Diane; Giovannetti, Olivia; Velikonja, Leah; Munday, Jasica; Kaufmann, Martin; Iaboni, Natasha; Jamzad, Amoon; Rubino, Rachel; Fichtinger, Gabor; Mousavi, Parvin; Nicol, Christopher JB; Rudan, John F; Adams, Michael A
Molecular characterization of human peripheral nerves using desorption electrospray ionization mass spectrometry imaging Journal Article
In: Journal of Anatomy, vol. 243, no. 5, pp. 758-769, 2023.
@article{tomalty2023,
title = {Molecular characterization of human peripheral nerves using desorption electrospray ionization mass spectrometry imaging},
author = {Diane Tomalty and Olivia Giovannetti and Leah Velikonja and Jasica Munday and Martin Kaufmann and Natasha Iaboni and Amoon Jamzad and Rachel Rubino and Gabor Fichtinger and Parvin Mousavi and Christopher JB Nicol and John F Rudan and Michael A Adams},
year = {2023},
date = {2023-01-01},
journal = {Journal of Anatomy},
volume = {243},
number = {5},
pages = {758-769},
abstract = {Desorption electrospray ionization mass spectrometry imaging (DESI‐MSI) is a molecular imaging method that can be used to elucidate the small‐molecule composition of tissues and map their spatial information using two‐dimensional ion images. This technique has been used to investigate the molecular profiles of variety of tissues, including within the central nervous system, specifically the brain and spinal cord. To our knowledge, this technique has yet to be applied to tissues of the peripheral nervous system (PNS). Data generated from such analyses are expected to advance the characterization of these structures. The study aimed to: (i) establish whether DESI‐MSI can discriminate the molecular characteristics of peripheral nerves and distinguish them from surrounding tissues and (ii) assess whether different peripheral nerve subtypes are characterized by unique molecular profiles. Four different nerves for …},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Imtiaz, Tashifa; Nanayakkara, Jina; Fang, Alexis; Jomaa, Danny; Mayotte, Harrison; Damiani, Simona; Javed, Fiza; Jones, Tristan; Kaczmarek, Emily; Adebayo, Flourish Omolara; Imtiaz, Uroosa; Li, Yiheng; Zhang, Richard; Mousavi, Parvin; Renwick, Neil; Tyryshkin, Kathrin
A user-driven machine learning approach for RNA-based sample discrimination and hierarchical classification Journal Article
In: STAR Protocols, vol. 4, no. 4, pp. 102661, 2023.
@article{imtiaz2023,
title = {A user-driven machine learning approach for RNA-based sample discrimination and hierarchical classification},
author = {Tashifa Imtiaz and Jina Nanayakkara and Alexis Fang and Danny Jomaa and Harrison Mayotte and Simona Damiani and Fiza Javed and Tristan Jones and Emily Kaczmarek and Flourish Omolara Adebayo and Uroosa Imtiaz and Yiheng Li and Richard Zhang and Parvin Mousavi and Neil Renwick and Kathrin Tyryshkin},
year = {2023},
date = {2023-01-01},
journal = {STAR Protocols},
volume = {4},
number = {4},
pages = {102661},
publisher = {Elsevier},
abstract = {RNA-based sample discrimination and classification can be used to provide biological insights and/or distinguish between clinical groups. However, finding informative differences between sample groups can be challenging due to the multidimensional and noisy nature of sequencing data. Here, we apply a machine learning approach for hierarchical discrimination and classification of samples with high-dimensional miRNA expression data. Our protocol comprises data preprocessing, unsupervised learning, feature selection, and machine-learning-based hierarchical classification, alongside open-source MATLAB code.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Gilany, Mahdi; Wilson, Paul; Jamzad, Amoon; Fooladgar, Fahimeh; To, Minh Nguyen Nhat; Wodlinger, Brian; Abolmaesumi, Purang; Mousavi, Parvin
Towards Confident Detection of Prostate Cancer using High Resolution Micro-ultrasound Proceedings Article Forthcoming
In: Medical Image Computing and Computer Assisted Interventions (MICCAI 2022), Forthcoming.
@inproceedings{nokey,
title = {Towards Confident Detection of Prostate Cancer using High Resolution Micro-ultrasound},
author = {Mahdi Gilany and Paul Wilson and Amoon Jamzad and Fahimeh Fooladgar and Minh Nguyen Nhat To and Brian Wodlinger and Purang Abolmaesumi and Parvin Mousavi},
year = {2022},
date = {2022-09-18},
urldate = {2022-09-18},
booktitle = {Medical Image Computing and Computer Assisted Interventions (MICCAI 2022)},
keywords = {},
pubstate = {forthcoming},
tppubtype = {inproceedings}
}
Javadi, Golara; Samadi, Samareh; Bayat, Sharareh; Sojoudi, Samira; Hurtado, Antonio; Eshumani, Walid; Chang, Silvia; Black, Peter; Mousavi, Parvin; Abolmaesumi, Purang
Training Deep Neural Networks with Noisy Clinical Labels: Towards Accurate Detection of Prostate Cancer in US Data Journal Article
In: International Journal of Computer Assisted Radiology and Surgery (IJCARS), 2022.
@article{nokeyb,
title = {Training Deep Neural Networks with Noisy Clinical Labels: Towards Accurate Detection of Prostate Cancer in US Data},
author = {Golara Javadi and Samareh Samadi and Sharareh Bayat and Samira Sojoudi and Antonio Hurtado and Walid Eshumani and Silvia Chang and Peter Black and Parvin Mousavi and Purang Abolmaesumi},
year = {2022},
date = {2022-06-21},
urldate = {2022-06-21},
journal = {International Journal of Computer Assisted Radiology and Surgery (IJCARS)},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Hu, Zoe; Fauerbach, Paola Nasute; Yeung, Chris; Ungi, Tamas; Rudan, John; Engel, Cecil Jay; Mousavi, Parvin; Fichtinger, Gabor; Jabs, Doris
Real-time automatic tumor segmentation for ultrasound-guided breast-conserving surgery navigation Journal Article
In: International Journal of Computer Assisted Radiology and Surgery (IJCARS), 2022.
@article{Hu2022,
title = {Real-time automatic tumor segmentation for ultrasound-guided breast-conserving surgery navigation},
author = {Zoe Hu and Paola Nasute Fauerbach and Chris Yeung and Tamas Ungi and John Rudan and Cecil Jay Engel and Parvin Mousavi and Gabor Fichtinger and Doris Jabs},
doi = {10.1007/s11548-022-02658-4},
year = {2022},
date = {2022-05-19},
journal = {International Journal of Computer Assisted Radiology and Surgery (IJCARS)},
keywords = {},
pubstate = {published},
tppubtype = {article}
}