ReviewFrontiers in bioengineering and biotechnology2024
Recent deep learning-based brain tumor segmentation models using multi-modality magnetic resonance imaging: a prospective survey.
Review in Frontiers in bioengineering and biotechnology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
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Who cites it
35 citing papers in PubMed.
- Brain Tumor Segmentation and Grading on MRI Using Deep Learning: A Systematic Literature Review and Benchmark-Driven Comparative Analysis.Diagnostics (Basel, Switzerland) · 2026Review
- Enhancing brain tumor detection through deep learning and explainable AI techniques.Scientific reports · 2026Article
- BRAIN-META: A reproducible CNN-vision transformer meta-ensemble pipeline for explainable brain tumor classification.MethodsX · 2026Article
- Enhanced ResU-Net for brain tumor segmentation using EfficientNetB0, channel attention, and ASPP.Biomedical physics & engineering express · 2026Article
- Physics-based simulation of ultrasound propagation from MRI volumes for cross-modal breast imaging.Journal of biological engineering · 2026Article
- Distilling population specific expertise into a unified model for generalizable brain tumor segmentation.Scientific reports · 2026Article
- Improving Pre-trained Adult Glioma Segmentation Models using only Post-processing Techniques.Segmentation, classification, and synthesis for brain tumors and traumatic brain injuries : MICCAI 2025 Challenges: BraTS-Lighthouse 2025 and AIMS-TBI 2025, held in conjunction with MICCAI 2025, Daejeon, South Korea, September 23, 2025,... · 2026Article
- A dual-prototype morphological topology evolution network for clinically oriented brain tumor MRI segmentation.Frontiers in oncology · 2026Article
- A Multimodal Diagnostic Model for Hepatocellular Carcinoma Integrating Biomarkers Related to Programmed Cell Death and Radiomics Features.Current medicinal chemistry · 2026Article
- Web of Science-Based Visualization of Mapping Global Scientific Production on Epstein-Barr Virus-Associated Autoimmune Diseases: A Bibliometric Study.Health science reports · 2025Article
- Enhanced brain tumor segmentation in medical imaging using multi-modal multi-scale contextual aggregation and attention fusion.Scientific reports · 2025Article
- Advanced Neuroimaging and Emerging Systemic Therapies in Glioblastoma: Current Evidence and Future Directions.Biomedicines · 2025Review
- Multimodal large language models in ultrasound diagnosis of breast masses: a multicenter comparative analysis based on GPT-4o, radiologists, and convolutional neural network (CNN).Quantitative imaging in medicine and surgery · 2025Article
- Clinical evaluation of two glioblastoma delineation methods based on neural networks.Technical innovations & patient support in radiation oncology · 2025Article
- A Review on Deep Learning Methods for Glioma Segmentation, Limitations, and Future Perspectives.Journal of imaging · 2025Review
- Deep learning strategies for semantic segmentation of pediatric brain tumors in multiparametric MRI.Scientific reports · 2025Article
- Multi-level channel-spatial attention and light-weight scale-fusion network (MCSLF-Net): multi-level channel-spatial attention and light-weight scale-fusion transformer for 3D brain tumor segmentation.Quantitative imaging in medicine and surgery · 2025Article
- Utilizing shallow features and spatial context for weakly supervised intracerebral hemorrhage segmentation.Quantitative imaging in medicine and surgery · 2025Article
- Association between estimated plasma volume status and the risk of 30-day mortality in patients with severe acute pancreatitis: a retrospective study based on the MIMIC-IV database.BMC gastroenterology · 2025Article
- Lights and Shadows on Artificial Intelligence in Glaucoma: Transforming Screening, Monitoring, and Prognosis.Journal of clinical medicine · 2025Review
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6 authors.
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Abstract
Radiologists encounter significant challenges when segmenting and determining brain tumors in patients because this information assists in treatment planning. The utilization of artificial intelligence (AI), especially deep learning (DL), has emerged as a useful tool in healthcare, aiding radiologists in their diagnostic processes. This empowers radiologists to understand the biology of tumors better and provide personalized care to patients with brain tumors. The segmentation of brain tumors using multi-modal magnetic resonance imaging (MRI) images has received considerable attention. In this survey, we first discuss multi-modal and available magnetic resonance imaging modalities and their properties. Subsequently, we discuss the most recent DL-based models for brain tumor segmentation using multi-modal MRI. We divide this section into three parts based on the architecture: the first is for models that use the backbone of convolutional neural networks (CNN), the second is for vision transformer-based models, and the third is for hybrid models that use both convolutional neural networks and transformer in the architecture. In addition, in-depth statistical analysis is performed of the recent publication, frequently used datasets, and evaluation metrics for segmentation tasks. Finally, open research challenges are identified and suggested promising future directions for brain tumor segmentation to improve diagnostic accuracy and treatment outcomes for patients with brain tumors. This aligns with public health goals to use health technologies for better healthcare delivery and population health management.
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