학술논문
CU-Net: A U-Net Architecture for Efficient Brain-Tumor Segmentation on BraTS 2019 Dataset
Document Type
Conference
Author
Source
2024 4th International Conference on Machine Learning and Intelligent Systems Engineering (MLISE) Machine Learning and Intelligent Systems Engineering (MLISE), 2024 4th International Conference on. :255-258 Jun, 2024
Subject
Language
Abstract
Accurately segmenting brain tumors from MRI scans is important for developing effective treatment plans and improving patient outcomes. This study introduces a new implementation of the Columbia-University-Net (CU-Net) architecture for brain tumor segmentation using the BraTS 2019 dataset. The CU-Net model has a symmetrical U-shaped structure and uses convolutional layers, max pooling, and upsampling operations to achieve high-resolution segmentation. Our CU-Net model achieved a Dice score of 82.41%, surpassing two other stateof-the-art models. This improvement in segmentation accuracy highlights the robustness and effectiveness of the model, which helps to accurately delineate tumor boundaries, which is crucial for surgical planning and radiation therapy, and ultimately has the potential to improve patient outcomes.