ArticlePhysical and engineering sciences in medicine2022
Glioma segmentation with DWI weighted images, conventional anatomical images, and post-contrast enhancement magnetic resonance imaging images by U-Net.
Article in Physical and engineering sciences in medicine, 2022. 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.
- Impact of MRI modality selection on glioma sub-region segmentation using 3D U-Net and Attention U-Net: A comparative evaluation across ten MRI sequences.European journal of radiology open · 2026Article
- Glioma Grade Classification Using Machine Learning and MRI Radiomics: A Single-Center Prospective Study Comparing Original and Wavelet-Transformed Features From Anatomical, Diffusion-Weighted, and Post-Contrast Imaging.Health science reports · 2026Article
- CSF1R and macrophage infiltration: Integrated magnetic resonance imaging radiomics and deep learning-driven models for the preoperative assessment of glioma.Chinese medical journal · 2026Article
- Automated Brain Tumor MRI Segmentation Using ARU-Net with Residual-Attention Modules.Diagnostics (Basel, Switzerland) · 2025Article
- Enhanced glioma semantic segmentation using U-net and pre-trained backbone U-net architectures.Scientific reports · 2025Article
- A Review on Deep Learning Methods for Glioma Segmentation, Limitations, and Future Perspectives.Journal of imaging · 2025Review
- A whole-body diffusion MRI normal atlas: development, evaluation and initial use.Cancer imaging : the official publication of the International Cancer Imaging Society · 2023Article
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4 authors.
Funding
Abstract
Glioma segmentation is believed to be one of the most important stages of treatment management. Recent developments in magnetic resonance imaging (MRI) protocols have led to a renewed interest in using automatic glioma segmentation with different MRI image weights. U-Net is a major area of interest within the field of automatic glioma segmentation. This paper examines the impact of different input MRI image-weight on the U-Net output performance for glioma segmentation. One hundred forty-nine glioma patients were scanned with a 1.5T MRI scanner. The main MRI image-weights acquired are diffusion-weighted imaging (DWI) weighted images (b50, b500, b1000, Apparent diffusion coefficient (ADC) map, Exponential apparent diffusion coefficient (eADC) map), anatomical image-weights (T
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