TY - GEN
T1 - TPUAR-net
T2 - 16th International Conference on Image Analysis and Recognition, ICIAR 2019
AU - Abd-Ellah, Mahmoud Khaled
AU - Khalaf, Ashraf A.M.
AU - Awad, Ali Ismail
AU - Hamed, Hesham F.A.
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2019.
PY - 2019
Y1 - 2019
N2 - The utilization of different types of brain images has been expanding, which makes manually examining each image a labor-intensive task. This study introduces a brain tumor segmentation method that uses two parallel U-Net with an asymmetric residual-based deep convolutional neural network (TPUAR-Net). The proposed method is customized to segment high and low grade glioblastomas identified from magnetic resonance imaging (MRI) data. Varieties of these tumors can appear anywhere in the brain and may have practically any shape, contrast, or size. Thus, this study used deep learning techniques based on adaptive, high-efficiency neural networks in the proposed model structure. In this paper, several high-performance models based on convolutional neural networks (CNNs) have been examined. The proposed TPUAR-Net capitalizes on different levels of global and local features in the upper and lower paths of the proposed model structure. In addition, the proposed method is configured to use the skip connection between layers and residual units to accelerate the training and testing processes. The TPUAR-Net model provides promising segmentation accuracy using MRI images from the BRATS 2017 database, while its parallelized architecture considerably improves the execution speed. The results obtained in terms of Dice, sensitivity, and specificity metrics demonstrate that TPUAR-Net outperforms other methods and achieves the state-of-the-art performance for brain tumor segmentation.
AB - The utilization of different types of brain images has been expanding, which makes manually examining each image a labor-intensive task. This study introduces a brain tumor segmentation method that uses two parallel U-Net with an asymmetric residual-based deep convolutional neural network (TPUAR-Net). The proposed method is customized to segment high and low grade glioblastomas identified from magnetic resonance imaging (MRI) data. Varieties of these tumors can appear anywhere in the brain and may have practically any shape, contrast, or size. Thus, this study used deep learning techniques based on adaptive, high-efficiency neural networks in the proposed model structure. In this paper, several high-performance models based on convolutional neural networks (CNNs) have been examined. The proposed TPUAR-Net capitalizes on different levels of global and local features in the upper and lower paths of the proposed model structure. In addition, the proposed method is configured to use the skip connection between layers and residual units to accelerate the training and testing processes. The TPUAR-Net model provides promising segmentation accuracy using MRI images from the BRATS 2017 database, while its parallelized architecture considerably improves the execution speed. The results obtained in terms of Dice, sensitivity, and specificity metrics demonstrate that TPUAR-Net outperforms other methods and achieves the state-of-the-art performance for brain tumor segmentation.
KW - Brain tumor segmentation
KW - Computer-aided diagnosis
KW - Convolutional neural networks
KW - Deep learning
KW - MRI images
KW - Parallel U-Net
KW - TPUAR-Net
UR - http://www.scopus.com/inward/record.url?scp=85071456776&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85071456776&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-27272-2_9
DO - 10.1007/978-3-030-27272-2_9
M3 - Conference contribution
AN - SCOPUS:85071456776
SN - 9783030272715
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 106
EP - 116
BT - Image Analysis and Recognition - 16th International Conference, ICIAR 2019, Proceedings
A2 - Karray, Fakhri
A2 - Yu, Alfred
A2 - Campilho, Aurélio
PB - Springer Verlag
Y2 - 27 August 2019 through 29 August 2019
ER -