ArticleFrontiers in neuroscience2022
Residual Block Based Nested U-Type Architecture for Multi-Modal Brain Tumor Image Segmentation.
Article in Frontiers in neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Advances in artificial intelligence for the diagnosis and treatment of ovarian cancer (Review).Oncology reports · 2024Review
- MARes-Net: multi-scale attention residual network for jaw cyst image segmentation.Frontiers in bioengineering and biotechnology · 2024Article
- MMGan: a multimodal MR brain tumor image segmentation method.Frontiers in human neuroscience · 2023Article
- Medical image fusion quality assessment based on conditional generative adversarial network.Frontiers in neuroscience · 2022Article
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3 authors.
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Abstract
Multi-modal magnetic resonance imaging (MRI) segmentation of brain tumors is a hot topic in brain tumor processing research in recent years, which can make full use of the feature information of different modalities in MRI images, so that tumors can be segmented more effectively. In this article, convolutional neural networks (CNN) is used as a tool to improve the efficiency and effectiveness of segmentation. Based on this, Dense-ResUNet, a multi-modal MRI image segmentation model for brain tumors is created. The Dense-ResUNet consists of a series of nested dense convolutional blocks and a U-Net shaped model with residual connections. The nested dense convolutional blocks can bridge the semantic disparity between the feature maps of the encoder and decoder before fusion and make full use of different levels of features. The residual blocks and skip connection can extract pixel information from the image and skip the link to solve the traditional deep traditional CNN network problem. The experiment results show that our Dense-ResUNet can effectively help to extract the brain tumor and has great clinical research and application value.
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