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Access Type
WSU Access
Date of Award
January 2023
Degree Type
Dissertation
Degree Name
Ph.D.
Department
Industrial and Manufacturing Engineering
First Advisor
Ratna B. Chinnam
Second Advisor
Suzan Arslanturk
Abstract
Detecting brain abnormalities in neonates is crucial for early diagnosis and treatment of various neurological disorders. Magnetic Resonance Imaging (MRI) has been shown to be an effective imaging modality for detecting brain abnormalities in neonates. Furthermore, machine learning and deep learning algorithms that utilize MRI's for clinical tasks are increasingly popular as their architectures improve and available computational increases.
In this dissertation, we propose multiple novel unsupervised approaches for detecting brain abnormalities in neonates using multimodal and segmented MRI brain scans. We also define a framework against which the quality of our models can be assessed, and the results of which can be utilized by a neuroradiologists in a clinical setting.
Our approach consists of variational autoencoder (VAE) networks for learning low-dimensional representations of normal brain scans and postprocessing to aid in detecting abnormal brain regions based on the learned representation of normality. We evaluate our approaches on a dataset provided by the Developing Human Connectome Project (dHCP), consisting of T1-weighted and T2-weighted, as well as segmentation masks encapsulating various regions of the brain.
Our experimental results show that our approach is capable of detecting anomalies regions that do not conform to normal neonatal brain physiology, with each approach capable of highlighting different aspects of abnormality. Furthermore, we demonstrate the interpretability of our approach by visualizing the abnormal brain regions detected by our methods. Our approach has the significant potential to be used as a radiologist's aide for detecting brain abnormalities in neonates, which can lead to earlier diagnosis and better treatment outcomes.
Recommended Citation
Raad, Jad Dino, "There And Back Again: Anomaly Detection In Neonatal Mri Brain Scans" (2023). Wayne State University Dissertations. 3890.
https://digitalcommons.wayne.edu/oa_dissertations/3890