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ShengWen Wang

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Proceedings Article | 26 May 2023 Paper

Multimodal MRI brain tumor image segmentation network based on U-Net

ShengWen Wang, ChangMing Zhu

Proceedings Volume 12700, 127003P (2023) https://doi.org/10.1117/12.2682262

KEYWORDS: Image segmentation, Tumors, Brain, Magnetic resonance imaging, Neuroimaging, Feature extraction

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Recently, the development of medical imaging technology has made computer image analysis methods an indispensable tool for clinical diagnosis in the medical field. Among these methods, Magnetic Resonance Imaging (MRI) technology provides doctors with a range of anatomical images, helping them locate lesions quickly and accurately. In this article, we propose a nested network named U2-SegNet that could be trained from scratch without relying on pre-trained networks. This architecture can effectively address segmentation challenges by capturing multi-scale information. Our approach leverages the following features: (1) a well-designed residual U-block (RSU) that captures contextual information of varying scales by mixing receptive fields of different sizes, (2) full utilization of multimodal MRI data to address data scarcity, (3) incorporation of Salient Object Detection (SOD) to enhance global and local contrast information, leading to smoother segmentation edges. We achieved impressive results on the BraTS18 dataset with our proposed model.

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