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26 May 2023 Multimodal MRI brain tumor image segmentation network based on U-Net
ShengWen Wang, ChangMing Zhu
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Proceedings Volume 12700, International Conference on Electronic Information Engineering and Data Processing (EIEDP 2023); 127003P (2023) https://doi.org/10.1117/12.2682262
Event: International Conference on Electronic Information Engineering and Data Processing (EIEDP 2023), 2023, Nanchang, China
Abstract
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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ShengWen Wang and ChangMing Zhu "Multimodal MRI brain tumor image segmentation network based on U-Net", Proc. SPIE 12700, International Conference on Electronic Information Engineering and Data Processing (EIEDP 2023), 127003P (26 May 2023); https://doi.org/10.1117/12.2682262
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KEYWORDS
Image segmentation
Tumors
Brain
Magnetic resonance imaging
Neuroimaging
Feature extraction
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