Keywords: Segmentation, BrainBrain 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.
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