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Brain tumor segmentation using deep learning techniques on multi-institution al MRI datasets
Karji, Fatemeh
Karji, Fatemeh
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t24056s_Karji.pdf
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2024-12
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Brain tumor segmentation uses medical pictures, usually MRI scans, to locate and characterize tumor areas. Separating tumor tissue from healthy brain structures allows for a more detail analysis of tumor size, shape, and location. Accurate segmentation is vital for diagnosing brain tumors, as it helps clinicians differentiate between benign and malignant tumors, understand their growth patterns, and assess their impact on surrounding brain regions. It also plays an essential role in treatment planning, allowing precise targeting of the tumor during surgery, radiation, or other therapies while minimizing damage to healthy brain tissue. This thesis tackles the crucial topic of accurate and effective brain tumor segmentation in MRI data for clinical diagnosis and therapy planning. This study offers a deep learning-based brain tumor segmentation method to overcome the drawbacks of manual segmentation, which is time-consuming, error-prone, and requires specialized expertise.
The experimental pipeline includes multi-modal MRI scan pre-processing, data augmentation to overcome data scarcity, and rigorous training using the BraTS2020 and BraTS2024 datasets. In this thesis, we aim to improve the generalizability of deep learning model for accurate detection of brain tumour segmentation. This cross-data validation technique ensures model robustness and generalizability. The study also compares 2D and 3D U-Net segmentation accuracy using Dice Similarity Coefficient measures. This comparison highlights the pros and cons of each model type in representing brain tumor morphology's complexity. Additionally, this study addresses complex tumor forms, MRI modalities, post-treatment effects, and data imbalance to obtain high segmentation accuracy, showing that the proposed models could considerably impact clinical practice.
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Thesis (M.S.)-- Wichita State University, College of Engineering, School of Computing
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Wichita State University
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© Copyright 2024 by Fatemeh Karji
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