BrainSeg-Net: Brain Tumor MR Image Segmentation via Enhanced Encoder–Decoder Network

Efficient segmentation of Magnetic Resonance (MR) brain tumor images is of the utmost value for the diagnosis of tumor region. In recent years, advancement in the field of neural networks has been used to refine the segmentation performance of brain tumor sub-regions. The brain tumor segmentation ha...

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Vydáno v:Diagnostics (Basel) Ročník 11; číslo 2; s. 169
Hlavní autoři: Rehman, Mobeen Ur, Cho, SeungBin, Kim, Jeehong, Chong, Kil To
Médium: Journal Article
Jazyk:angličtina
Vydáno: Switzerland MDPI 25.01.2021
MDPI AG
Témata:
brain tumor
diagnostics
Feature Enhancer (FE)
Magnetic Resonance (MR) Images
medical imaging
semantic segmentation
brain tumor
Magnetic Resonance (MR) Images
diagnostics
semantic segmentation
Feature Enhancer (FE)
medical imaging
ISSN:2075-4418, 2075-4418
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Shrnutí:Efficient segmentation of Magnetic Resonance (MR) brain tumor images is of the utmost value for the diagnosis of tumor region. In recent years, advancement in the field of neural networks has been used to refine the segmentation performance of brain tumor sub-regions. The brain tumor segmentation has proven to be a complicated task even for neural networks, due to the small-scale tumor regions. These small-scale tumor regions are unable to be identified, the reason being their tiny size and the huge difference between area occupancy by different tumor classes. In previous state-of-the-art neural network models, the biggest problem was that the location information along with spatial details gets lost in deeper layers. To address these problems, we have proposed an encoder–decoder based model named BrainSeg-Net. The Feature Enhancer (FE) block is incorporated into the BrainSeg-Net architecture which extracts the middle-level features from low-level features from the shallow layers and shares them with the dense layers. This feature aggregation helps to achieve better performance of tumor identification. To address the problem associated with imbalance class, we have used a custom-designed loss function. For evaluation of BrainSeg-Net architecture, three benchmark datasets are utilized: BraTS2017, BraTS 2018, and BraTS 2019. Segmentation of Enhancing Core (EC), Whole Tumor (WT), and Tumor Core (TC) is carried out. The proposed architecture have exhibited good improvement when compared with existing baseline and state-of-the-art techniques. The MR brain tumor segmentation by BrainSeg-Net uses enhanced location and spatial features, which performs better than the existing plethora of brain MR image segmentation approaches.
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ISSN:2075-4418
2075-4418
DOI:10.3390/diagnostics11020169