Skull Stripping Using Confidence Segmentation Convolution Neural Network

Kaiyuan Chen, Jingyue Shen, Fabien Scalzo

Abstract: Skull stripping is an important preprocessing step on cerebral Magnetic Resonance (MR) images because unnecessary brain structures, like eye balls and muscles, greatly hinder the accuracy of further automatic diagnosis. To extract important brain tissue quickly, we developed a model named Confidence Segmentation Convolutional Neural Network (CSCNet). CSCNet takes the form of a Fully Convolutional Network (FCN) that adopts an encoder-decoder architecture which gives a reconstructed bitmask with pixel-wise confidence level. During our experiments, a crossvalidation was performed on 750 MRI slices of the brain and demonstrated the high accuracy of the model (dice score: $$0.97\pm 0.005$$ ) with a prediction time of less than 0.5 s.