Alumni Profile

@article{Javadi2021,

title = {Towards Targeted Ultrasound-guided Prostate Biopsy by Incorporating Model and Label Uncertainty in Cancer Detection},

author = {Golara Javadi and Sharareh Bayat and Samareh Samadi and Alireza Sedghi and Samira Sojoudi and Antonio Hurtado and Silvia Chang and Peter Black and Parvin Mousavi and Purang Abolmaesumi},

year  = {2021},

date = {2021-06-22},

journal = {International Journal of Computer Assisted Radiology and Surgery, special issue of International Conference on Information Processing in Computer-Assisted Interventions (IPCAI)},

keywords = {},

pubstate = {forthcoming},

tppubtype = {article}

}

@article{santilli2021domain,

title = {Domain adaptation and self-supervised learning for surgical margin detection},

author = {Alice Santilli and Amoon Jamzad and Alireza Sedghi and Martin Kaufmann and Kathryn Logan and Julie Wallis and Kevin YM Ren and Natasja Janssen and Shaila Merchant and Jay Engel and Doug McKay and Sonal Varma and Ami Wang and Gabor Fichtinger and John F. Rudan and Parvin Mousavi},

year  = {2021},

date = {2021-05-06},

journal = {International Journal of Computer Assisted Radiology and Surgery (IPCAI)},

volume = {16},

number = {5},

pages = {861--869},

publisher = {Springer},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{sedghi2021image,

title = {Image registration: Maximum likelihood, minimum entropy and deep learning},

author = {Alireza Sedghi and Lauren J O’Donnell and Tina Kapur and Erik Learned-Miller and Parvin Mousavi and William M Wells III},

year  = {2021},

date = {2021-04-01},

journal = {Medical Image Analysis (MEDIA)},

volume = {69},

pages = {101939},

publisher = {Elsevier},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{chan2020generalizable,

title = {Generalizable deep temporal models for predicting episodes of sudden hypotension in critically ill patients: a personalized approach},

author = {Brandon Chan and Brian Chen and Alireza Sedghi and Philip Laird and David Maslove and Parvin Mousavi},

year  = {2020},

date = {2020-07-10},

journal = {Scientific Reports},

volume = {10},

number = {1},

pages = {1--10},

publisher = {Nature Publishing Group},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Sedghi2020,

title = {Improving Detection of Prostate Cancer Foci via Information Fusion of MRI and Temporal Enhanced Ultrasound},

author = {Alireza Sedghi and Alireza Mehrtash and Amoon Jamzad and Amel Amalou and William M Wells III and Tina Kapur and Jin Tae Kwak and Baris Turkbey and Peter Choyke and Peter Pinto and Bradford Wood and Sheng Xu and Purang Abolmaesumi and Parvin Mousavi},

year  = {2020},

date = {2020-05-05},

journal = {International Journal of Computer Assisted Radiology and Surgery, special issue of International Conference on Information Processing in Computer-Assisted Interventions (IPCAI)},

volume = {15},

number = {7},

pages = {1215--1223},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Wang2020,

title = {Desorptive Electrospray Ionization Mass Spectrometry Imaging (DESI-MSI) in the Application of Cancer Identification in Prostate},

author = {Jenny Wang and Nicole Morse and Tamara Jamaspishvili and Martin Kaufmann and Kevin Ren and Alireza Sedghi and Amoon Jamzad and Parvin Mousavi and David Berman},

year  = {2020},

date = {2020-03-26},

journal = {The 18th Annual Symposium of the Imaging Network of Ontario (ImNO)},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{525,

title = {Deep neural maps for unsupervised visualization of high-grade cancer in prostate biopsies.},

author = {Alireza Sedghi and Mehran Pesteie and Golara Javadi and Shekoofeh Azizi and Pingkun Yan and Jin Tae Kwak and Sheng Xu and Baris Turkbey and Peter Choyke and Peter Pinto and Bradford Wood and Robert Rohling and Purang Abolmaesumi and Parvin Mousavi},

doi = {10.1007/s11548-019-01950-0},

issn = {1861-6429},

year  = {2019},

date = {2019-03-01},

journal = {Int J Comput Assist Radiol Surg},

abstract = {\<p\>Prostate cancer (PCa) is the most frequent noncutaneous cancer in men. Early detection of PCa is essential for clinical decision making, and reducing metastasis and mortality rates. The current approach for PCa diagnosis is histopathologic analysis of core biopsies taken under transrectal ultrasound guidance (TRUS-guided). Both TRUS-guided systematic biopsy and MR-TRUS-guided fusion biopsy have limitations in accurately identifying PCa, intraoperatively. There is a need to augment this process by visualizing highly probable areas of PCa. Temporal enhanced ultrasound (TeUS) has emerged as a promising modality for PCa detection. Prior work focused on supervised classification of PCa verified by gold standard pathology. Pathology labels are noisy, and data from an entire core have a single label even when significantly heterogeneous. Additionally, supervised methods are limited by data from cores with known pathology, and a significant portion of prostate data is discarded without being used. We provide an end-to-end unsupervised solution to map PCa distribution from TeUS data using an innovative representation learning method, deep neural maps. TeUS data are transformed to a topologically arranged hyper-lattice, where similar samples are closer together in the lattice. Therefore, similar regions of malignant and benign tissue in the prostate are clustered together. Our proposed method increases the number of training samples by several orders of magnitude. Data from biopsy cores with known labels are used to associate the clusters with PCa. Cancer probability maps generated using the unsupervised clustering of TeUS data help intuitively visualize the distribution of abnormal tissue for augmenting TRUS-guided biopsies.\</p\>},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{548,

title = {Using the variogram for vector outlier screening: application to feature-based image registration.},

author = {Jie Luo and Sarah Frisken and Ines Machado and Miaomiao Zhang and Steve Pieper and Polina Golland and Matthew Toews and Prashin Unadkat and Alireza Sedghi and Haoyin Zhou and Alireza Mehrtash and Frank Preiswerk and Cheng-Chieh Cheng and Alexandra Golby and Masashi Sugiyama and William M Wells},

doi = {10.1007/s11548-018-1840-5},

issn = {1861-6429},

year  = {2018},

date = {2018-12-01},

journal = {Int J Comput Assist Radiol Surg},

volume = {13},

pages = {1871-1880},

abstract = {\<p\>\textbf{PURPOSE: }Matching points that are derived from features or landmarks in image data is a key step in some medical imaging applications. Since most robust point matching algorithms claim to be able to deal with outliers, users may place high confidence in the matching result and use it without further examination. However, for tasks such as feature-based registration in image-guided neurosurgery, even a few mismatches, in the form of invalid displacement vectors, could cause serious consequences. As a result, having an effective tool by which operators can manually screen all matches for outliers could substantially benefit the outcome of those applications.\</p\>\<p\>\textbf{METHODS: }We introduce a novel variogram-based outlier screening method for vectors. The variogram is a powerful geostatistical tool for characterizing the spatial dependence of stochastic processes. Since the spatial correlation of invalid displacement vectors, which are considered as vector outliers, tends to behave differently than normal displacement vectors, they can be efficiently identified on the variogram.\</p\>\<p\>\textbf{RESULTS: }We validate the proposed method on 9 sets of clinically acquired ultrasound data. In the experiment, potential outliers are flagged on the variogram by one operator and further evaluated by 8 experienced medical imaging researchers. The matching quality of those potential outliers is approximately 1.5 lower, on a scale from 1 (bad) to 5 (good), than valid displacement vectors.\</p\>\<p\>\textbf{CONCLUSION: }The variogram is a simple yet informative tool. While being used extensively in geostatistical analysis, it has not received enough attention in the medical imaging field. We believe there is a good deal of potential for clinically applying the proposed outlier screening method. By way of this paper, we also expect researchers to find variogram useful in other medical applications that involve motion vectors analyses.\</p\>},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{551,

title = {Model-based registration of preprocedure MR and intraprocedure US of the lumbar spine.},

author = {Delaram Behnami and Alireza Sedghi and Emran Mohammad Abu Anas and Abtin Rasoulian and Alexander Seitel and Victoria Lessoway and Tamas Ungi and David Yen and Jill Osborn and Parvin Mousavi and Robert Rohling and Purang Abolmaesumi},

doi = {10.1007/s11548-017-1552-2},

issn = {1861-6429},

year  = {2017},

date = {2017-06-01},

journal = {Int J Comput Assist Radiol Surg},

volume = {12},

pages = {973-982},

abstract = {\<p\>\textbf{PURPOSE: }Epidural and spinal needle insertions, as well as facet joint denervation and injections are widely performed procedures on the lumbar spine for delivering anesthesia and analgesia. Ultrasound (US)-based approaches have gained popularity for accurate needle placement, as they use a non-ionizing, inexpensive and accessible modality for guiding these procedures. However, due to the inherent difficulties in interpreting spinal US, they yet to become the clinical standard-of-care.\</p\>\<p\>\textbf{METHODS: }A novel statistical shape~[Formula: see text]~pose~[Formula: see text]~scale (s~[Formula: see text]~p~[Formula: see text]~s) model of the lumbar spine is jointly registered to preoperative magnetic resonance (MR) and US images. An instance of the model is created for each modality. The shape and scale model parameters are jointly computed, while the pose parameters are estimated separately for each modality.\</p\>\<p\>\textbf{RESULTS: }The proposed method is successfully applied to nine pairs of preoperative clinical MR volumes and their corresponding US images. The results are assessed using the target registration error (TRE) metric in both MR and US domains. The s~[Formula: see text]~p~[Formula: see text]~s model in the proposed joint registration framework results in a mean TRE of 2.62 and 4.20~mm for MR and US images, respectively, on different landmarks.\</p\>\<p\>\textbf{CONCLUSION: }The joint framework benefits from the complementary features in both modalities, leading to significantly smaller TREs compared to a model-to-US registration approach. The s~[Formula: see text]~p~[Formula: see text]~s model also outperforms our previous shape~[Formula: see text]~pose model of the lumbar spine, as separating scale from pose allows to better capture pose and guarantees equally-sized vertebrae in both modalities. Furthermore, the simultaneous visualization of the patient-specific models on the MR and US domains makes it possible for clinicians to better evaluate the local registration accuracy.\</p\>},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{550,

title = {Classification of Clinical Significance of MRI Prostate Findings Using 3D Convolutional Neural Networks.},

author = {Alireza Mehrtash and Alireza Sedghi and Mohsen Ghafoorian and Mehdi Taghipour and Clare M Tempany and William M Wells and Tina Kapur and Parvin Mousavi and Purang Abolmaesumi and Andriy Fedorov},

doi = {10.1117/12.2277123},

issn = {0277-786X},

year  = {2017},

date = {2017-02-01},

journal = {Proc SPIE Int Soc Opt Eng},

volume = {10134},

abstract = {\<p\>Prostate cancer (PCa) remains a leading cause of cancer mortality among American men. Multi-parametric magnetic resonance imaging (mpMRI) is widely used to assist with detection of PCa and characterization of its aggressiveness. Computer-aided diagnosis (CADx) of PCa in MRI can be used as clinical decision support system to aid radiologists in interpretation and reporting of mpMRI. We report on the development of a convolution neural network (CNN) model to support CADx in PCa based on the appearance of prostate tissue in mpMRI, conducted as part of the SPIE-AAPM-NCI PROSTATEx challenge. The performance of different combinations of mpMRI inputs to CNN was assessed and the best result was achieved using DWI and DCE-MRI modalities together with the zonal information of the finding. On the test set, the model achieved an area under the receiver operating characteristic curve of 0.80.\</p\>},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{480,

title = {Model-based registration of preprocedure MR and intraprocedure US of the lumbar spine},

author = {Delaram Behnami and Alireza Sedghi and Emran Mohammad Abu Anas and Abtin Rasoulian and Alexander Seitel and Victoria Lessoway and Tamas Ungi and David Yen and Jill Osborn and Parvin Mousavi and others},

year  = {2017},

date = {2017-01-01},

journal = {International journal of computer assisted radiology and surgery},

volume = {12},

pages = {973\textendash982},

keywords = {},

pubstate = {published},

tppubtype = {article}

}