ReviewMedical & biological engineering & computing2024
mResU-Net: multi-scale residual U-Net-based brain tumor segmentation from multimodal MRI.
Review in Medical & biological engineering & computing, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- W-AGRU-Net: a dual-stream attention framework for robust brain tumor segmentation in clinical MRI imaging.Scientific reports · 2026Article
- Enhanced YOLOv8-seg-SPDConv for accurate Schatzker classification of tibial plateau fractures.Frontiers in medicine · 2026Article
- From Anatomy to Genomics Using a Multi-Task Deep Learning Approach for Comprehensive Glioma Profiling.Bioengineering (Basel, Switzerland) · 2025Article
- Identification of Gingival Inflammation Surface Image Features Using Intraoral Scanning and Deep Learning.International dental journal · 2025Article
- Deep learning-assisted screening and diagnosis of scoliosis: segmentation of bare-back images via an attention-enhanced convolutional neural network.Journal of orthopaedic surgery and research · 2025Article
- A deep learning-based automatic segmentation model for diffuse midline glioma with H3K27M alteration.Frontiers in oncology · 2025Article
- Efficient data labeling strategies for automated muscle segmentation in lower leg MRIs of Charcot-Marie-Tooth disease patients.PloS one · 2024Article
- Recent deep learning-based brain tumor segmentation models using multi-modality magnetic resonance imaging: a prospective survey.Frontiers in bioengineering and biotechnology · 2024Review
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Brain tumor segmentation is an important direction in medical image processing, and its main goal is to accurately mark the tumor part in brain MRI. This study proposes a brand new end-to-end model for brain tumor segmentation, which is a multi-scale deep residual convolutional neural network called mResU-Net. The semantic gap between the encoder and decoder is bridged by using skip connections in the U-Net structure. The residual structure is used to alleviate the vanishing gradient problem during training and ensure sufficient information in deep networks. On this basis, multi-scale convolution kernels are used to improve the segmentation accuracy of targets of different sizes. At the same time, we also integrate channel attention modules into the network to improve its accuracy. The proposed model has an average dice score of 0.9289, 0.9277, and 0.8965 for tumor core (TC), whole tumor (WT), and enhanced tumor (ET) on the BraTS 2021 dataset, respectively. Comparing the segmentation results of this method with existing techniques shows that mResU-Net can significantly improve the segmentation performance of brain tumor subregions.
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Registered trials
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