An improved 3D U-Net-based deep learning system for brain tumor segmentation using multi-modal MRI

Cancer is a lethal illness that requires an initial stage prognosis to enhance patient survival rate. Accurate brain tumor and their sub-structure segmentation through Magnetic Resonance Images (MRIs) is a tough endeavor. Owing to the heterogeneous tumor areas, automatically segmenting brain tumors...

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Vydáno v:Multimedia tools and applications Ročník 83; číslo 37; s. 85027 - 85046
Hlavní autoři: Ali, Saqib, Khurram, Rooha, Rehman, Khalil ur, Yasin, Anaa, Shaukat, Zeeshan, Sakhawat, Zareen, Mujtaba, Ghulam
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Springer US 01.11.2024
Springer Nature B.V
Témata:
Accuracy
Algorithms
Brain
Brain cancer
Comparative studies
Computer Communication Networks
Computer Science
Data Structures and Information Theory
Datasets
Deep learning
Encoders-Decoders
Image enhancement
Image segmentation
Machine learning
Magnetic resonance imaging
Medical prognosis
Multimedia Information Systems
Neural networks
Performance evaluation
Special Purpose and Application-Based Systems
Three dimensional models
Track 8: Medical Imaging
Transition layers
Tumors
Brain tumor diagnosis
Deep learning
Multi-modal MRI
Computer-aid diagnosis
Tumor segmentation
ISSN:1573-7721, 1380-7501, 1573-7721
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Shrnutí:Cancer is a lethal illness that requires an initial stage prognosis to enhance patient survival rate. Accurate brain tumor and their sub-structure segmentation through Magnetic Resonance Images (MRIs) is a tough endeavor. Owing to the heterogeneous tumor areas, automatically segmenting brain tumors has proved to be a critical task even for neural network-based algorithms, some tumor regions remain unidentified due to their small size and the variation in area occupancy among tumor sub-classes. Current progress in the area of neural networks has been employed to enhance the segmentation performance. This study designed an intelligent 3D U-Net encoder-decoder-based system for automatic detection and brain tumor sub-structure segmentation. Our proposed 3D model constitutes neural units (the basic building blocks) followed by transition layer blocks and skip connections. BraTS 2018 and private local datasets are used to evaluate the proposed model which segments the Whole Tumor (WT), Tumor Core (TC), and the Enhancing Tumor (ET). The training accuracy, validation accuracy, dice score, sensitivities, and specificities of WT, CT, and ET zones are computed. The experimental results demonstrate that dice scores are 0.913, 0.874, and 0.801 for the BraTS 2018 dataset. The developed models performance was further evaluated by utilizing the dataset from a local hospital containing 71 subjects. The dice scores of 0.891, 0.834, and 0.776 are achieved by the proposed model on the private dataset. The practicability of the proposed model was assessed by the comparative studies of our model with existing literature.
Bibliografie:ObjectType-Article-1
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ISSN:1573-7721
1380-7501
1573-7721
DOI:10.1007/s11042-024-19406-2