Skull-Base Tumor Segmentation and Classification
Keywords: Medical Imaging, Skull-base tumor segmentation, Unet Segmentation
Abstract: Skull-base tumor segmentation is a critical yet challenging task due to the rarity of such tumors and the complexity of the anatomical region. This work focuses on developing an automated segmentation and classification framework for central skull base tumors using multimodal 3D imaging data, including CT and MRI scans. By leveraging state-of-the-art deep learning architectures tailored for 3D medical imaging, we aim to accurately delineate tumor boundaries while preserving anatomical context. Our approach incorporates advanced data augmentation techniques and loss functions optimized for handling imbalanced datasets. Initial experiments demonstrate the framework's capability to achieve precise tumor segmentation, highlighting its potential to assist clinicians in diagnosis and treatment planning. Future work will focus on expanding the dataset and integrating interpretability mechanisms to further enhance clinical adoption.
Submission Number: 105
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