EVAC: Multi-scale V-Net with Deep Feature Conditional Random Field Layers for Brain Extraction
Jong Sung Park, Shreyas Fadnavis, Eleftherios Garyfallidis · Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition · 2024
Brain Extraction is a complicated semantic segmentation task. While Deep Learning methods are popularly used, they are heavily biased towards the training dataset. To reduce this dependency, we present EVAC (Enhanced V-net like Architecture with Conditional Random Fields), a novel Deep Learning model for Brain Extraction. Using V-net as a skeleton, we propose three improvements: multi-scale inputs, modified CRF layer and regularizing Dice Loss. Results show that these changes not only increase accuracy but also the efficiency of the model as well. Compared to the state-of-the-art methods, our model achieves high and stable accuracy across datasets.