TY  - JOUR
U1  - Zeitschriftenartikel, wissenschaftlich - begutachtet (reviewed)
A1  - Zeineldin, Ramy
A1  - Karar, Mohamed Esmail
A1  - Coburger, Jan
A1  - Wirtz, Christian
A1  - Burgert, Oliver
T1  - DeepSeg: deep neural network framework for automatic brain tumor segmentation using magnetic resonance FLAIR images
JF  - International Journal of Computer Assisted Radiology and Surgery
N2  - Purpose: Gliomas are the most common and aggressive type of brain tumors due to their infiltrative nature and rapid progression. The process of distinguishing tumor boundaries from healthy cells is still a challenging task in the clinical routine. Fluid attenuated inversion recovery (FLAIR) MRI modality can provide the physician with information about tumor infiltration. Therefore, this paper proposes a new generic deep learning architecture, namely DeepSeg, for fully automated detection and segmentation of the brain lesion using FLAIR MRI data.
Methods: The developed DeepSeg is a modular decoupling framework. It consists of two connected core parts based on an encoding and decoding relationship. The encoder part is a convolutional neural network (CNN) responsible for spatial information extraction. The resulting semantic map is inserted into the decoder part to get the full-resolution probability map. Based on modified U-Net architecture, different CNN models such as residual neural network (ResNet), dense convolutional network (DenseNet), and NASNet have been utilized in this study.
Results: The proposed deep learning architectures have been successfully tested and evaluated on-line based on MRI datasets of brain tumor segmentation (BraTS 2019) challenge, including s336 cases as training data and 125 cases for validation data. The dice and Hausdorff distance scores of obtained segmentation results are about 0.81 to 0.84 and 9.8 to 19.7 correspondingly.
Conclusion: This study showed successful feasibility and comparative performance of applying different deep learning models in a new DeepSeg framework for automated brain tumor segmentation in FLAIR MR images. The proposed DeepSeg is open source and freely available at https://github.com/razeineldin/DeepSeg/.
KW  - brain tumor
KW  - computer-aided diagnosis
KW  - convolutional neural networks
KW  - deep learning
Y1  - 2020
UN  - https://nbn-resolving.org/urn:nbn:de:bsz:rt2-opus4-27666
SN  - 1861-6410
SS  - 1861-6410
U6  - https://doi.org/10.1007/s11548-020-02186-z
DO  - https://doi.org/10.1007/s11548-020-02186-z
VL  - 15
IS  - 6
SP  - 909
EP  - 920
S1  - 12
PB  - Springer
CY  - Berlin ; Heidelberg
ER  -