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441 results on '"Abolmaesumi, Purang"'

5. AI-based histopathology image analysis reveals a distinct subset of endometrial cancers

Author

Darbandsari, Amirali, Farahani, Hossein, Asadi, Maryam, Wiens, Matthew, Cochrane, Dawn, Khajegili Mirabadi, Ali, Jamieson, Amy, Farnell, David, Ahmadvand, Pouya, Douglas, Maxwell, Leung, Samuel, Abolmaesumi, Purang, Jones, Steven J. M., Talhouk, Aline, Kommoss, Stefan, Gilks, C. Blake, Huntsman, David G., Singh, Naveena, McAlpine, Jessica N., and Bashashati, Ali

Published
2024
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6. Validation of machine learning models for estimation of left ventricular ejection fraction on point-of-care ultrasound: insights on features that impact performance

Author

Luong, Christina L., Jafari, Mohammad H., Behnami, Delaram, Shah, Yaksh R., Straatman, Lynn, Van Woudenberg, Nathan, Christoff, Leah, Gwadry, Nancy, Hawkins, Nathaniel M., Sayre, Eric C., Yeung, Darwin, Tsang, Michael, Gin, Ken, Jue, John, Nair, Parvathy, Abolmaesumi, Purang, and Tsang, Teresa

Published
2024
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18. Detection and grading of prostate cancer using temporal enhanced ultrasound: combining deep neural networks and tissue mimicking simulations.

Author

Azizi, Shekoofeh, Bayat, Sharareh, Yan, Pingkun, Tahmasebi, Amir, Nir, Guy, Kwak, Jin, Xu, Sheng, Wilson, Storey, Iczkowski, Kenneth, Lucia, M, Goldenberg, Larry, Salcudean, Septimiu, Pinto, Peter, Wood, Bradford, Abolmaesumi, Purang, and Mousavi, Parvin

Subjects
Cancer grading, Deep belief network, Deep learning, Prostate cancer, Temporal enhanced ultrasound, Humans, Image-Guided Biopsy, Imaging, Three-Dimensional, Magnetic Resonance Imaging, Male, Neoplasm Staging, Neural Networks, Computer, Prostatic Neoplasms, Sensitivity and Specificity, Ultrasonography, Interventional
Abstract

PURPOSE  : Temporal Enhanced Ultrasound (TeUS) has been proposed as a new paradigm for tissue characterization based on a sequence of ultrasound radio frequency (RF) data. We previously used TeUS to successfully address the problem of prostate cancer detection in the fusion biopsies. METHODS  : In this paper, we use TeUS to address the problem of grading prostate cancer in a clinical study of 197 biopsy cores from 132 patients. Our method involves capturing high-level latent features of TeUS with a deep learning approach followed by distribution learning to cluster aggressive cancer in a biopsy core. In this hypothesis-generating study, we utilize deep learning based feature visualization as a means to obtain insight into the physical phenomenon governing the interaction of temporal ultrasound with tissue. RESULTS  : Based on the evidence derived from our feature visualization, and the structure of tissue from digital pathology, we build a simulation framework for studying the physical phenomenon underlying TeUS-based tissue characterization. CONCLUSION  : Results from simulation and feature visualization corroborated with the hypothesis that micro-vibrations of tissue microstructure, captured by low-frequency spectral features of TeUS, can be used for detection of prostate cancer.

Published
2017

21. A multi-center milestone study of clinical vertebral CT segmentation

Author

Yao, Jianhua, Burns, Joseph E, Forsberg, Daniel, Seitel, Alexander, Rasoulian, Abtin, Abolmaesumi, Purang, Hammernik, Kerstin, Urschler, Martin, Ibragimov, Bulat, Korez, Robert, Vrtovec, Tomaž, Castro-Mateos, Isaac, Pozo, Jose M, Frangi, Alejandro F, Summers, Ronald M, and Li, Shuo

Subjects
Information and Computing Sciences, Biomedical and Clinical Sciences, Clinical Sciences, Bioengineering, Biomedical Imaging, Networking and Information Technology R&D (NITRD), Aged, Aged, 80 and over, Algorithms, California, Female, Humans, Lumbar Vertebrae, Male, Middle Aged, Pattern Recognition, Automated, Radiographic Image Enhancement, Radiographic Image Interpretation, Computer-Assisted, Reference Values, Reproducibility of Results, Sensitivity and Specificity, Software Validation, Subtraction Technique, Thoracic Vertebrae, Tomography, X-Ray Computed, Biomedical Engineering, Nuclear Medicine & Medical Imaging, Clinical sciences, Biomedical engineering, Computer vision and multimedia computation
Abstract

A multiple center milestone study of clinical vertebra segmentation is presented in this paper. Vertebra segmentation is a fundamental step for spinal image analysis and intervention. The first half of the study was conducted in the spine segmentation challenge in 2014 International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) Workshop on Computational Spine Imaging (CSI 2014). The objective was to evaluate the performance of several state-of-the-art vertebra segmentation algorithms on computed tomography (CT) scans using ten training and five testing dataset, all healthy cases; the second half of the study was conducted after the challenge, where additional 5 abnormal cases are used for testing to evaluate the performance under abnormal cases. Dice coefficients and absolute surface distances were used as evaluation metrics. Segmentation of each vertebra as a single geometric unit, as well as separate segmentation of vertebra substructures, was evaluated. Five teams participated in the comparative study. The top performers in the study achieved Dice coefficient of 0.93 in the upper thoracic, 0.95 in the lower thoracic and 0.96 in the lumbar spine for healthy cases, and 0.88 in the upper thoracic, 0.89 in the lower thoracic and 0.92 in the lumbar spine for osteoporotic and fractured cases. The strengths and weaknesses of each method as well as future suggestion for improvement are discussed. This is the first multi-center comparative study for vertebra segmentation methods, which will provide an up-to-date performance milestone for the fast growing spinal image analysis and intervention.

Published
2016

28. Automated Atrial Fibrillation Diagnosis by Echocardiography without ECG: Accuracy and Applications of a New Deep Learning Approach.

Author

Lu, Nelson, Vaseli, Hooman, Mahdavi, Mobina, Taheri Dezaki, Fatemah, Luong, Christina, Yeung, Darwin, Gin, Ken, Tsang, Michael, Nair, Parvathy, Jue, John, Barnes, Marion, Behnami, Delaram, Abolmaesumi, Purang, and Tsang, Teresa S. M.

Subjects
DEEP learning, ATRIAL fibrillation, ECHOCARDIOGRAPHY, HEART beat, ELECTROCARDIOGRAPHY, DIAGNOSIS, ARTIFICIAL intelligence
Abstract

Background: Automated rhythm detection on echocardiography through artificial intelligence (AI) has yet to be fully realized. We propose an AI model trained to identify atrial fibrillation (AF) using apical 4-chamber (AP4) cines without requiring electrocardiogram (ECG) data. Methods: Transthoracic echocardiography studies of consecutive patients ≥ 18 years old at our tertiary care centre were retrospectively reviewed for AF and sinus rhythm. The study was first interpreted by level III-trained echocardiography cardiologists as the gold standard for rhythm diagnosis based on ECG rhythm strip and imaging assessment, which was also verified with a 12-lead ECG around the time of the study. AP4 cines with three cardiac cycles were then extracted from these studies with the rhythm strip and Doppler information removed and introduced to the deep learning model ResNet(2+1)D with an 80:10:10 training–validation–test split ratio. Results: 634 patient studies (1205 cines) were included. After training, the AI model achieved high accuracy on validation for detection of both AF and sinus rhythm (mean F1-score = 0.92; AUROC = 0.95). Performance was consistent on the test dataset (mean F1-score = 0.94, AUROC = 0.98) when using the cardiologist's assessment of the ECG rhythm strip as the gold standard, who had access to the full study and external ECG data, while the AI model did not. Conclusions: AF detection by AI on echocardiography without ECG appears accurate when compared to an echocardiography cardiologist's assessment of the ECG rhythm strip as the gold standard. This has potential clinical implications in point-of-care ultrasound and stroke risk stratification. [ABSTRACT FROM AUTHOR]

Published
2024
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37. Cardiac Phenotyping of SARS-CoV-2 in British Columbia: A Prospective Echo Study With Strain Imaging.

Author

Yim, Jeffrey, Tsang, Michael Y. C., Venkataraman, Anand, Balthazaar, Shane, Gin, Ken, Jue, John, Nair, Parvathy, Luong, Christina, Yeung, Darwin F., Moss, Robb, Virani, Sean A., McKay, Jane, Williams, Margot, Sayre, Eric C., Abolmaesumi, Purang, and Tsang, Teresa S. M.

Subjects
GLOBAL longitudinal strain, SPECKLE tracking echocardiography, SARS-CoV-2, VENTRICULAR ejection fraction, CORONARY artery disease, CARDIAC radionuclide imaging, FREE flaps
Abstract

BACKGROUND: There is limited data on the residual echocardiographic findings including strain analysis among post-coronavirus disease (COVID) patients. The aim of our study is to prospectively phenotype post-COVID patients. METHODS: All patients discharged following acute COVID infection were systematically followed in the post-COVID-19 Recovery Clinic at Vancouver General Hospital and St. Paul's Hospital. At 4-18 weeks post diagnosis, patients underwent comprehensive echocardiographic assessment. Left ventricular ejection fraction (LVEF) was assessed by 3D, 2D Biplane Simpson's, or visual estimate. LV global longitudinal strain (GLS) was measured using a vendor-independent 2D speckle-tracking software (TomTec). RESULTS: A total of 127 patients (53% female, mean age 58 years) were included in our analyses. At baseline, cardiac conditions were present in 58% of the patients (15% coronary artery disease, 4% heart failure, 44% hypertension, 10% atrial fibrillation) while the remainder were free of cardiac conditions. COVID-19 serious complications were present in 79% of the patients (76% pneumonia, 37% intensive care unit admission, 21% intubation, 1% myocarditis). Normal LVEF was seen in 96% of the cohort and 97% had normal right ventricular systolic function. A high proportion (53%) had abnormal LV GLS defined as < 18%. Average LV GLS of septal and inferior segments were lower compared to that of other segments. Among patients without pre-existing cardiac conditions, LVEF was abnormal in only 1.9%, but LV GLS was abnormal in 46% of the patients. CONCLUSIONS: Most post-COVID patients had normal LVEF at 4-18 weeks post diagnosis, but over half had abnormal LV GLS. [ABSTRACT FROM AUTHOR]

Published
2023
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38. Semi-supervised learning from coarse histopathology labels.

Author

Fooladgar, Fahimeh, Nguyen Nhat to, Minh, Javadi, Golara, Sojoudi, Samira, Eshumani, Walid, Chang, Silvia, Black, Peter, Mousavi, Parvin, and Abolmaesumi, Purang

Subjects
SUPERVISED learning, HISTOPATHOLOGY, ULTRASONIC imaging, DATA scrubbing, MACHINE learning, EARLY detection of cancer
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 regularisation, entropy minimisation, and the Mixup regularisation as well as the cross-entropy loss function for noisy and clean sets, respectively. We evaluate the proposed framework with prostate ultrasound data obtained from 71 subjects, while sampling 264 biopsy cores. We achieve balanced accuracy, sensitivity, and specificity of 78.6%, 80.0%, and 77.1%, respectively. In a detailed comparison study, we demonstrate that our proposed framework outperforms the fully supervised method with state-of-the-art robust loss functions. [ABSTRACT FROM AUTHOR]

Published
2023
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