ReviewHealthcare (Basel, Switzerland)2021
Overview of Multi-Modal Brain Tumor MR Image Segmentation.
Review in Healthcare (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- FDSS-Net: feature enhancement and dual-stream semantic mixture network for polyp segmentation.Scientific reports · 2026Article
- TumorSynth: Integrated Brain Tumor and Tissue Segmentation on Brain MRI Scans of Any Resolution and Contrast.Radiology. Imaging cancer · 2026Article
- Comprehensive Morphometric MRI Assessment in Children with Breath-Holding Spells: Integration of Automated (Vol2Brain) and Semi-Automated (3D Slicer) Segmentation Methods.Tomography (Ann Arbor, Mich.) · 2026Article
- AG-MS3D-CNN multiscale attention guided 3D convolutional neural network for robust brain tumor segmentation across MRI protocols.Scientific reports · 2025Article
- Deep learning-based automated detection of endometrioid endometrial carcinoma in histopathology.Frontiers in oncology · 2025Article
- Modality redundancy for MRI-based glioblastoma segmentation.International journal of computer assisted radiology and surgery · 2024Article
- Enhancing brain tumor segmentation in MRI images using the IC-net algorithm framework.Scientific reports · 2024Article
- One Model to Synthesize Them All: Multi-Contrast Multi-Scale Transformer for Missing Data Imputation.IEEE transactions on medical imaging · 2023Article
- Article
- Z2-Journal of imaging · 2022Article
- Medical image fusion by sparse-based modified fusion framework using block total least-square update dictionary learning algorithm.Journal of medical imaging (Bellingham, Wash.) · 2022Article
- Full coverage path planning algorithm for MRgFUS therapy.The international journal of medical robotics + computer assisted surgery : MRCAS · 2022Article
- A Novel Hybrid Machine Learning Based System to Classify Shoulder Implant Manufacturers.Healthcare (Basel, Switzerland) · 2022Article
- A Framework for Interactive Medical Image Segmentation Using Optimized Swarm Intelligence with Convolutional Neural Networks.Computational intelligence and neuroscience · 2022Article
- A systematic review of deep learning-based segmentation techniques for brain tumor detection (2013-2023).Digital healthReview
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
The precise segmentation of brain tumor images is a vital step towards accurate diagnosis and effective treatment of brain tumors. Magnetic Resonance Imaging (MRI) can generate brain images without tissue damage or skull artifacts, providing important discriminant information for clinicians in the study of brain tumors and other brain diseases. In this paper, we survey the field of brain tumor MRI images segmentation. Firstly, we present the commonly used databases. Then, we summarize multi-modal brain tumor MRI image segmentation methods, which are divided into three categories: conventional segmentation methods, segmentation methods based on classical machine learning methods, and segmentation methods based on deep learning methods. The principles, structures, advantages and disadvantages of typical algorithms in each method are summarized. Finally, we analyze the challenges, and suggest a prospect for future development trends.
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