ReviewExperimental biology and medicine (Maywood, N.J.)2023
Machine learning and deep learning for brain tumor MRI image segmentation.
Review in Experimental biology and medicine (Maywood, N.J.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed.
- Deep Learning and Machine Learning Algorithms for Cervical Cancer Segmentation on MRI: A Systematic Review.Journal of medical radiation sciences · 2026Review
- Hierarchical two-stage ensemble of dilated U-net models for brain tumor segmentation.Visual computing for industry, biomedicine, and art · 2026Article
- High-precision brain tumor segmentation with switchable normalization in faster R-CNN architecture.Scientific reports · 2026Article
- Leveraging machine learning for selective cannabinoid ligand discovery: methods, challenges, and opportunities.Expert opinion on drug discovery · 2026Review
- Brain tumor segmentation using dual-stream multiscale 3D-UNET with dense net and spatial attention.Scientific reports · 2026Article
- A Machine learning pipeline to investigate tissue ingrowth in cerebral aneurysms using preclinical animal models.Scientific reports · 2026Article
- Automated Ventricle Assessment via Three-dimensional Anatomical Reconstruction (AVA-TAR): a computational toolkit for autonomous lateral ventricle assessment in preclinical hydrocephalus models.bioRxiv : the preprint server for biology · 2026Article
- Dmcie: Diffusion model with concatenation of inputs and errors for enhanced brain tumor segmentation in MRI images.International journal of computer assisted radiology and surgery · 2026Article
- A dual-prototype morphological topology evolution network for clinically oriented brain tumor MRI segmentation.Frontiers in oncology · 2026Article
- Towards a better understanding of clinical disease activity scores in dogs with chronic enteropathies.The veterinary quarterly · 2025Article
- MRI multi-sequence deep learning integration with clinical profiles for pediatric viral encephalitis diagnosis.Scientific reports · 2025Article
- Machine Learning in MRI Brain Imaging: A Review of Methods, Challenges, and Future Directions.Diagnostics (Basel, Switzerland) · 2025Review
- Radiomics and deep learning model based on X-ray imaging for the assisted diagnosis of early Legg-Calvé-Perthes disease.BMC musculoskeletal disorders · 2025Article
- Integrating Molecular Dynamics, Molecular Docking, and Machine Learning for Predicting SARS-CoV-2 Papain-like Protease Binders.Molecules (Basel, Switzerland) · 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
- Concomitant Prediction of the Ki67 and PIT-1 Expression in Pituitary Adenoma Using Different Radiomics Models.Journal of imaging informatics in medicine · 2025Article
- A refined set of RxNorm drug names for enhancing unstructured data analysis in drug safety surveillance.Experimental biology and medicine (Maywood, N.J.) · 2025Article
- Harnessing machine learning to predict and prevent proximal junctional kyphosis and failure in adult spinal deformity surgery: A systematic review.Brain & spine · 2025Review
- Article
- Interpretable self-supervised contrastive learning for colorectal cancer histopathology: GRADCAM visualization.Bioinformation · 2025Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Brain tumors are often fatal. Therefore, accurate brain tumor image segmentation is critical for the diagnosis, treatment, and monitoring of patients with these tumors. Magnetic resonance imaging (MRI) is a commonly used imaging technique for capturing brain images. Both machine learning and deep learning techniques are popular in analyzing MRI images. This article reviews some commonly used machine learning and deep learning techniques for brain tumor MRI image segmentation. The limitations and advantages of the reviewed machine learning and deep learning methods are discussed. Even though each of these methods has a well-established status in their individual domains, the combination of two or more techniques is currently an emerging trend.
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