Abstract
Accurate segmentation of low-grade gliomas is crucial for treatment planning and patient management, but remains challenging due to the subtle appearance differences between tumor and normal brain tissue. Traditional segmentation methods often struggle with the complex boundaries and heterogeneous appearance of gliomas. In this work, we propose a novel approach that combines Convolutional Neural Networks (CNN) with Fully Connected Conditional Random Fields (CRF) for low-grade glioma segmentation.
Our framework consists of two main components: (1) a deep CNN that learns discriminative features for glioma detection and initial segmentation, and (2) a fully connected CRF that refines the segmentation boundaries by incorporating spatial consistency and appearance constraints. The CNN provides robust feature learning capabilities, while the CRF ensures smooth and anatomically plausible segmentation results.
We evaluate our method on a comprehensive dataset of low-grade glioma patients with expert annotations. Experimental results demonstrate that our approach significantly outperforms traditional segmentation methods and CNN-only approaches. The method shows excellent performance in handling the challenging aspects of glioma segmentation, including boundary ambiguity and tissue heterogeneity.
The proposed framework represents a significant advancement in brain tumor segmentation, providing more accurate results that could improve treatment planning and patient outcomes.
BibTeX
@article{li2017low,
title={Low-grade glioma segmentation based on CNN with fully connected CRF},
author={Li, Zeju and Wang, Yuanyuan and Yu, Jinhua and Shi, Zhifeng and Guo, Yi and Chen, Liang and Mao, Ying},
journal={Journal of Healthcare Engineering},
year={2017},
publisher={Hindawi},
doi={10.1155/2017/9283480}
}