Ms. Haleh Akrami Profile

Haleh Akrami

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Publications (6)

Proceedings Article | 4 April 2022 Poster + Paper

Prediction of tumor mutation burden from H&E whole-slide images: a comparison of training strategies with convolutional neural networks

Antong Chen, Tosha Shah, Andrew Brown, Haleh Akrami, Albert Swiston, Amir Vajdi, Radha Krishnan, Razvan Cristescu

Proceedings Volume 12039, 120391D (2022) https://doi.org/10.1117/12.2613196

KEYWORDS: Tumors, Melanoma, Convolutional neural networks, Tissues, Lung cancer, Cancer, Tumor growth modeling, Image classification, Receivers, Oncology

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Proceedings Article | 15 February 2021 Poster + Presentation + Paper

Bias field correction in 3D-MRIs using convolutional autoencoders.

Shashank N. Sridhara, Haleh Akrami, Vaishnavi Krishnamurthy, Anand Joshi

Proceedings Volume 11596, 115962H (2021) https://doi.org/10.1117/12.2582042

KEYWORDS: Tissues, Magnetic resonance imaging, Image classification, Neural networks, Network architectures, Magnetism, Image segmentation, Image processing algorithms and systems, Denoising

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Proceedings Article | 15 February 2021 Presentation + Paper

A pairwise approach for fMRI group studies using the BrainSync Transform

Anand Joshi, Soyoung Choi, Jian Li, Haleh Akrami, Richard Leahy

Proceedings Volume 11596, 115960G (2021) https://doi.org/10.1117/12.2580980

KEYWORDS: Functional magnetic resonance imaging, Traumatic brain injury, Epilepsy, Distance measurement, Statistical methods, Statistical analysis, Independent component analysis

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Proceedings Article | 15 February 2021 Poster + Presentation + Paper

Prediction of posttraumatic epilepsy using machine learning

Haleh Akrami, Andrei Irimia, Wenhui Cui, Anand Joshi, Richard Leahy

Proceedings Volume 11600, 116001Q (2021) https://doi.org/10.1117/12.2580953

KEYWORDS: Machine learning, Epilepsy, Traumatic brain injury, Neurological disorders, Injuries, Functional imaging, Brain

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Post-traumatic Epilepsy is one of the common aftereffects of brain injury. This neurological disorder can persist throughout the lifetime of patients and impacts their quality of life significantly. Identification of markers that indicate the likelihood of developing PTE can help develop preventive care for subjects identified as at risk. Despite the relatively high prevalence of PTE, brain imaging-based biomarkers for the diagnosis of PTE are lacking. This is due in part to the heterogeneity of injury in traumatic brain injury patients. Here we investigate the use of structural and functional imaging features for training machine learning models. Recently, applying state of the art machine learning methods such as neural networks on clinical data to help diagnosis attract a considerable amount of attention. However, the choice of a good algorithm and subset of features is fundamental to achieve reliable classification. Our goal is to explore if different Machine Learning techniques could be leveraged for the early diagnosis of PTE. We compared four popular machine learning methods performance to predict PTE after brain injury (1) support vector machine and (2) random forest (3) fully connected neural network and (4) graph convolutional network. Our result demonstrates the advantage of using a combination of connectivity features (functional) and lesion volume (structural) in conjunction with a Kernel SVM approach in predicting PTE. We also shown using a feature reduction method such as principal component analysis (PCA) is more effective than penalizing the classifiers. This might be due to the limitation of penalized models for a framework where features are correlatedPost-traumatic Epilepsy is one of the common aftereffects of brain injury. This neurological disorder can persist throughout the lifetime of patients and impacts their quality of life significantly. Identification of markers that indicate the likelihood of developing PTE can help develop preventive care for subjects identified as at risk. Despite the relatively high prevalence of PTE, brain imaging-based biomarkers for the diagnosis of PTE are lacking. This is due in part to the heterogeneity of injury in traumatic brain injury patients. Here we investigate the use of structural and functional imaging features for training machine learning models. Recently, applying state of the art machine learning methods such as neural networks on clinical data to help diagnosis attract a considerable amount of attention. However, the choice of a good algorithm and subset of features is fundamental to achieve reliable classification. Our goal is to explore if different Machine Learning techniques could be leveraged for the early diagnosis of PTE. We compared four popular machine learning methods performance to predict PTE after brain injury (1) support vector machine and (2) random forest (3) fully connected neural network and (4) graph convolutional network. Our result demonstrates the advantage of using a combination of connectivity features (functional) and lesion volume (structural) in conjunction with a Kernel SVM approach in predicting PTE. We also shown using a feature reduction method such as principal component analysis (PCA) is more effective than penalizing the classifiers. This might be due to the limitation of penalized models for a framework where features are correlated.

Proceedings Article | 15 March 2019 Paper

Predicting cognitive scores from resting fMRI data and geometric features of the brain

Anand Joshi, Jian Li, Haleh Akrami, Richard Leahy

Proceedings Volume 10949, 109492E (2019) https://doi.org/10.1117/12.2512063

KEYWORDS: Functional magnetic resonance imaging, Brain, Magnetic resonance imaging, Neural networks, Neuroimaging, Image processing, Data processing, Machine learning, Feature extraction, Signal processing

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Proceedings Article | 15 March 2019 Paper

Group-wise alignment of resting fMRI in space and time

Haleh Akrami, Anand Joshi, Jian Li, Richard Leahy

Proceedings Volume 10949, 109492W (2019) https://doi.org/10.1117/12.2512564

KEYWORDS: Functional magnetic resonance imaging, Brain, Independent component analysis, Distance measurement, Matrices, Databases, Signal processing, Image processing

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Spontaneous brain activity is an important biomarker for various neurological and psychological conditions and can be measured using resting functional Magnetic Resonance Imaging (rfMRI). Since brain activity during resting is spontaneous, it is not possible to directly compare rfMRI time-courses across subjects. Moreover, the spatial configuration of functionally specialized brain regions can vary across subjects throughout the cortex limiting our ability to make precise spatial comparisons. We describe a new approach to jointly align and synchronize fMRI data in space and time, across a group of subjects. We build on previously described methods for inter-subject spatial “Hyper-Alignment” and temporal synchronization through the “BrainSync” transform. We first describe BrainSync Alignment (BSA), a group-based extension of the pair-wise BrainSync transform, that jointly synchronizes resting or task fMRI data across time for multiple subjects. We then explore the combination of BSA with Response Hyper-Alignment (RHA) and compare with Connectivity Hyper-Alignment (CHA), an alternative approach to spatial alignment based on resting fMRI. The result of applying RHA and BSA is both to produce improved functional spatial correspondence across a group of subjects, and to align their time-series so that, even for spontaneous resting data, we see highly correlated temporal dynamics at homologous locations across the group. These spatiotemporally aligned data can then be used as an atlas in future applications. We validate these transfer functions by applying them to z-score maps of an independent dataset and calculating inter-subject correlation. The results show that RHA can be calculated from rfMRI and have comparable output with CHA by leveraging BSA. Moreover, through calculation and application to task fMRI-based spatial transformations on an independent dataset, we show that the combination of RHA and BSA produces improved spatial functional alignment significantly relative to either RHA or CHA alone.

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