LAUSR

Abstract Extracting brain parenchyma from computed tomography (CT) images of the head is an important prerequisite step in a number of image processing applications, as it improves the computational speed and accuracy of quantitative analyses and image co-registration. In this study, we present a robust method based on fully convolutional neural networks (CNN) to remove non-brain tissues from head CT scans in a computationally efficient manner. The method includes an encoding part, which has sequential convolutional filters that produce feature representation of the input image in low dimensional space, and a decoding part, which consists of convolutional filters that reconstruct the input image from the reduced representation. We trained several CNN models on 122 volumetric head CT scans and tested our models on 22 withheld volumetric CT head scans based on two experts’ manual brain segmentation. The performance of our best CNN model on the test set is: Dice Coefficient = 0.998 ± 0.001 (mean ± standard deviation), recall = 0.999 ± 0.001, precision = 0.998 ± 0.001, and accuracy = 1. Our method extracts complete volumetric brain from head CT images in about 2 s which is substantially faster than currently available methods. To the best of our knowledge, this is the first study using CNN to perform brain extraction from CT images. In conclusion, the proposed approach based on CNN provides accurate extraction of brain tissue from head CT images in a computationally efficient manner.