TY - JOUR
T1 - Robust brain extraction tool for CT head images
AU - Akkus, Zeynettin
AU - Kostandy, Petro
AU - Philbrick, Kenneth A.
AU - Erickson, Bradley J.
N1 - Funding Information:
We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan X Pascal GPU used for this research.
Publisher Copyright:
© 2019 Elsevier B.V.
PY - 2020/6/7
Y1 - 2020/6/7
N2 - 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.
AB - 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.
KW - Brain extraction
KW - Computed tomography
KW - Convolutional neural network
KW - Deep learning
KW - Image segmentation
KW - Skull stripping
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UR - http://www.scopus.com/inward/citedby.url?scp=85064597197&partnerID=8YFLogxK
U2 - 10.1016/j.neucom.2018.12.085
DO - 10.1016/j.neucom.2018.12.085
M3 - Article
AN - SCOPUS:85064597197
VL - 392
SP - 189
EP - 195
JO - Neurocomputing
JF - Neurocomputing
SN - 0925-2312
ER -