Multimodal 3D Brain Tumor Segmentation with Adversarial Training and Conditional Random Field
This work addresses the challenge of accurate brain tumor segmentation for medical imaging applications, representing an incremental improvement over existing methods.
The paper tackled brain tumor segmentation by proposing a multimodal 3D Volume Generative Adversarial Network (3D-vGAN) that integrates adversarial training and conditional random fields, achieving specificity over 99.8% on the BraTS-2018 dataset and outperforming classical models like U-net and FCN.
Accurate brain tumor segmentation remains a challenging task due to structural complexity and great individual differences of gliomas. Leveraging the pre-eminent detail resilience of CRF and spatial feature extraction capacity of V-net, we propose a multimodal 3D Volume Generative Adversarial Network (3D-vGAN) for precise segmentation. The model utilizes Pseudo-3D for V-net improvement, adds conditional random field after generator and use original image as supplemental guidance. Results, using the BraTS-2018 dataset, show that 3D-vGAN outperforms classical segmentation models, including U-net, Gan, FCN and 3D V-net, reaching specificity over 99.8%.