Dr. Muhammad Waqas Profile

Dr. Muhammad Waqas

at Univ at Buffalo

SPIE Involvement:

Author

Publications (11)

SPIE Journal Paper | 9 January 2023

Identification of infarct core and ischemic penumbra using computed tomography perfusion and deep learning

Mohammad Mahdi Shiraz Bhurwani, Timothe Boutelier, Adam Davis, Guillaume Gautier, Dennis Swetz, Ryan Rava, Dorian Raguenes, Muhammad Waqas, Kenneth Snyder, Adnan Siddiqui, Ciprian Ionita

JMI, Vol. 10, Issue 01, 014001, (January 2023) https://doi.org/10.1117/12.10.1117/1.JMI.10.1.014001

KEYWORDS: Shadows, Tunable filters, Voxels, Computed tomography, Image segmentation, Tissues, Wavelets, Diffusion weighted imaging, Optical spheres, Education and training

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Proceedings Article | 4 April 2022 Poster + Presentation + Paper

Predicting hematoma expansion after spontaneous intracranial hemorrhage through a radiomics based model

Samantha Seymour, Ryan Rava, Dennis Swetz, Andre Montiero, Ammad Baig, Kurt Schultz, Kenneth Snyder, Muhammad Waqas, Jason Davies, Elad Levy, Adnan Siddiqui, Ciprian Ionita

Proceedings Volume 12033, 120332X (2022) https://doi.org/10.1117/12.2611847

KEYWORDS: Magnetic resonance imaging, Lawrencium, Feature extraction, Medical imaging, Image segmentation, Computed tomography, Systems modeling, Process modeling, Monte Carlo methods, Data acquisition

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Purpose: Intracranial hemorrhage (ICH) is characterized as bleeding into the brain tissue, intracranial space, and ventricles and is the second most disabling form of stroke. Hematoma expansion (HE) following ICH has been correlated with significant neurological decline and death. For early detection of patients at risk, deep learning prediction models were developed to predict whether hematoma due to ICH will expand. This study aimed to explore the feasibility of HE prediction using a radiomic approach to help clinicians better stratify HE patients and tailor intensive therapies timely and effectively. Materials and Methods: Two hundred ICH patients with known hematoma evolution, were enrolled in this study. An open-source python package was utilized for the extraction of radiomic features from both non-contrast computed tomography (NCCT) and magnetic resonance imaging (MRI) scans through characterization algorithms. A total of 99 radiomic features were extracted and different features were selected for network inputs for the NCCT and MR models. Seven supervised classifiers: Support Vector Machine, Naïve Bayes, Decision Tree, Random Forest, Logistic Regression, K-Nearest Neighbor and Multilayer Perceptron were used to build the models. A training:testing split of 80:20 and 20 iterations of Monte Carlo cross validation were performed to prevent overfitting and assess the variability of the networks, respectively. The models were fed training datasets from which they learned to classify the data based on pre-determined radiomic categories. Results: The highest sensitivity among the NCCT classifier models was seen with the support vector machine (SVM) and logistic regression (LR) of 72 ± 0.3% and 73 ± 0.5%, respectively. The MRI classifier models had the highest sensitivity of 68 ± 0.5% and 72 ± 0.5% for the SVM and LR models, respectively. Conclusions: This study indicates that the NCCT radiomics model is a better predictor of HE and that SVM and LR classifiers are better predictors of HE due to their more cautious approach indicated by a higher sensitivity metric.

Proceedings Article | 4 April 2022 Presentation + Paper

Initial investigation of predicting hematoma expansion for intracerebral hemorrhage using imaging biomarkers and machine learning

Dennis Swetz, Samantha Seymour, Ryan Rava, Mohammad Mahdi Shiraz Bhurwani, Andre Monteiro, Ammad Baig, Muhammad Waqas, Kenneth Snyder, Elad Levy, Jason Davies, Adnan Siddiqui, Ciprian Ionita

Proceedings Volume 12036, 120360B (2022) https://doi.org/10.1117/12.2610672

KEYWORDS: Machine learning, Brain, Computed tomography, Blood, Data modeling, Receivers, Performance modeling, Statistical analysis, Algorithm development

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Proceedings Article | 15 February 2021 Presentation + Paper

Clot organization on histology is associated with radiomics features that predict treatment outcomes from mechanical thrombectomy

Tatsat R. Patel, Muhammad Waqas, Sarah Fricano, Adam Dmytriw, John Tomaszewski, Elad Levy, Kenneth Snyder, Jason Davies, Adnan Siddiqui, Vincent Tutino

Proceedings Volume 11603, 116030L (2021) https://doi.org/10.1117/12.2581130

KEYWORDS: Rutherfordium, Computed tomography, Statistical analysis, Image segmentation, Image analysis, Blood, Medical imaging, Machine vision, Ischemic stroke

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Proceedings Article | 15 February 2021 Presentation + Paper

Use of biplane quantitative angiographic imaging with ensemble neural networks to assess reperfusion status during mechanical thrombectomy

Mohammad Mahdi Shiraz Bhurwani, Kenneth Snyder, Muhammad Waqas, Maxim Mokin, Ryan Rava, Alexander Podgorsak, Kelsey Sommer, Jason Davies, Elad Levy, Adnan Siddiqui, Ciprian Ionita

Proceedings Volume 11597, 115971F (2021) https://doi.org/10.1117/12.2580358

KEYWORDS: Angiography, Neural networks, Hemodynamics, Visualization, Visual process modeling, Receivers, Optimization (mathematics), Ischemic stroke, Convolutional neural networks, Brain mapping

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Proceedings Article | 15 February 2021 Presentation + Paper

Use of a convolutional neural network to identify infarct core using computed tomography perfusion parameters

Ryan Rava, Alexander Podgorsak, Mohammad Waqas, Kenneth Snyder, Elad Levy, Jason Davies, Adnan Siddiqui, Ciprian Ionita

Proceedings Volume 11596, 1159611 (2021) https://doi.org/10.1117/12.2579753

KEYWORDS: Convolutional neural networks, Computed tomography, Diffusion weighted imaging, Tissues, Ischemic stroke, Image segmentation, Targeting Task Performance metric, Monte Carlo methods, Hemodynamics, Diagnostics

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SPIE Journal Paper | 10 February 2021

Investigation of convolutional neural networks using multiple computed tomography perfusion maps to identify infarct core in acute ischemic stroke patients

Ryan Rava, Alexander Podgorsak, Muhammad Waqas, Kenneth Snyder, Maxim Mokin, Elad Levy, Jason Davies, Adnan Siddiqui, Ciprian Ionita

JMI, Vol. 8, Issue 01, 014505, (February 2021) https://doi.org/10.1117/12.10.1117/1.JMI.8.1.014505

KEYWORDS: Image segmentation, Diffusion weighted imaging, Targeting Task Performance metric, Computed tomography, Optical spheres, Convolutional neural networks, Tissues, Ischemic stroke, Arteries, Brain

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Proceedings Article | 16 March 2020 Paper

Multi-resolution CNN for brain vessel segmentation from cerebrovascular images of intracranial aneurysm: a comparison of U-Net and DeepMedic

Tatsat Patel, Nikhil Paliwal, Prakhar Jaiswal, Muhammad Waqas, Maxim Mokin, Adnan Siddiqui, Hui Meng, Rahul Rai, Vincent Tutino

Proceedings Volume 11314, 113142W (2020) https://doi.org/10.1117/12.2549761

KEYWORDS: Image segmentation, Brain, Aneurysms, Medical imaging, Convolutional neural networks

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Proceedings Article | 16 March 2020 Paper

Predicting treatment outcome of intracranial aneurysms using angiographic parametric imaging and recurrent neural networks

Mohammad Mahdi Shiraz Bhurwani, Mohammad Waqas, Kyle Williams, Ryan Rava, Alexander Podgorsak, Kenneth Snyder, Elad Levy, Jason Davies, Adnan Siddiqui, Ciprian Ionita

Proceedings Volume 11314, 113142O (2020) https://doi.org/10.1117/12.2548635

KEYWORDS: Aneurysms, Angiography, Neural networks, Targeting Task Performance metric, Receivers, X-rays, Monte Carlo methods, Blood circulation, Binary data, Hemodynamics

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SPIE Journal Paper | 11 February 2020

Performance of angiographic parametric imaging in locating infarct core in large vessel occlusion acute ischemic stroke patients

Ryan Rava, Maxim Mokin, Kenneth Snyder, Muhammad Waqas, Adnan Siddiqui, Jason Davies, Elad Levy, Ciprian Ionita

JMI, Vol. 7, Issue 01, 016001, (February 2020) https://doi.org/10.1117/12.10.1117/1.JMI.7.1.016001

KEYWORDS: Tissues, Angiography, Magnetic resonance imaging, Targeting Task Performance metric, Image registration, Arteries, Artificial intelligence, Ischemic stroke, Hemodynamics, Image segmentation

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Proceedings Article | 1 March 2019 Presentation + Paper

Initial study of the radiomics of intracranial aneurysms using Angiographic Parametric Imaging (API) to evaluate contrast flow changes

Anusha Ramesh Chandra, Alexander Podgorsak, Mohammad Waqas, Mohammad Mahdi Shiraz Bhurwani, Hussain Shallwani, Jordan Marshall, Adnan Siddiqui, Jason Davies, Stephen Rudin, Ciprian Ionita

Proceedings Volume 10948, 1094805 (2019) https://doi.org/10.1117/12.2512457

KEYWORDS: Aneurysms, Angiography, Machine learning, Targeting Task Performance metric, Arteries, Blood circulation, Neural networks, Receivers, Neck, Statistical analysis

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Showing 5 of 11 publications

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