Year: 2024
Type: article
Abstract: Brain tumor segmentation in medical image analysis is a challenging task. Deep learning techniques have recently shown promise in resolving a variety of computer vision problems, such as semantic segmentation and image classification. Brain MRI (magnetic resonance imaging) requires precise brain image segmentation for effective, rapid diagnosis and treatment planning. However, it is quite difficult to manually segment the brain image rapidly and accurately in low-quality, noisy images. This paper proposes a U-Net and combined attention mechanism-based method. This research enhances the segmentation of images of tumors in the brain using modified U-net. Traditional U-net segmentation techniques are still widely used in the medical field, but they have a number of limitations when dealing with small targets and fuzzier boundaries. To address this issue, we made the following modifications to U-net: We propose attention mechanisms to assist the network in concentrating on important regions. The multiscale feature fusion strategy improves the efficacy of network segmentation at various scales. Cross-entropy loss function and data augmentation improve the performance of the network further. Our method was validated using the Brats2019 dataset. The experimental results demonstrate that our proposed methodology exhibits superior speed and efficiency compared to existing techniques in the context of brain image segmentation. The dice coefficients for the multiple branch TS-U-net model were 0.876, 0.868, and 0.814 in the tumor subregions of WT, TC, and ET, respectively. This exemplifies the feasibility and potential of our methodology for the segmentation of medical images.
Authors: Yajie Zhang, Hea Choon Ngo, Yifan Zhang, Noor Fazilla Abd Yusof, Xiaohan Wang
Language: English
Subfield: Neurology
Field: Neuroscience
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $539
OpenAlex ID: W4405673968