ReviewBrain sciences2021
Brain Image Segmentation in Recent Years: A Narrative Review.
Review in Brain sciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
27 citing papers in PubMed.
- TumorSynth: Integrated Brain Tumor and Tissue Segmentation on Brain MRI Scans of Any Resolution and Contrast.Radiology. Imaging cancer · 2026Article
- InnerEye-HS: a disease-agnostic clinical tool for hippocampal segmentation.Brain communications · 2026Article
- RAUM-GANs: a multi-layer GAN-enhanced framework for accurate multiple sclerosis lesion segmentation in MRI.Scientific reports · 2025Article
- Improved COOT optimization: An approach to multilevel thresholding in image segmentation.Scientific reports · 2025Article
- A dual-stage framework for segmentation of the brain anatomical regions with high accuracy.Magma (New York, N.Y.) · 2025Article
- Natural history progression of MRI brain volumetrics in type II late-infantile and juvenile GM1 gangliosidosis patients.Molecular genetics and metabolism · 2025Article
- Transformers for Neuroimage Segmentation: Scoping Review.Journal of medical Internet research · 2025Article
- Automatic Adaptive Algorithm for Delineation of Cerebral-Spinal Fluid Regions for Non-contrast Magnetic Resonance Imaging Volumetry and Cisternography in Mice.Bio-protocol · 2025Article
- Semi-supervised synthesis of 7T MRI from 3T using 3D FR-U-Net with anatomical segmentation consistency assessment.PloS one · 2025Article
- A review of applications of automated ventricular parcellation from magnetic resonance imaging of the brain.Frontiers in neurology · 2025Review
- RegBoost: Enhancing mouse brain image registration using geometric priors and Laplacian interpolation.NeuroImage · 2025Article
- Exploiting K-Space in Magnetic Resonance Imaging Diagnosis: Dual-Path Attention Fusion for K-Space Global and Image Local Features.Bioengineering (Basel, Switzerland) · 2024Article
- Real-time estimation of the optimal coil placement in transcranial magnetic stimulation using multi-task deep learning.Scientific reports · 2024Article
- Comprehensive Review: Machine and Deep Learning in Brain Stroke Diagnosis.Sensors (Basel, Switzerland) · 2024Review
- Detection of Marchiafava Bignami disease using distinct deep learning techniques in medical diagnostics.BMC medical imaging · 2024Article
- Hemodynamic property incorporated brain tumor segmentation by deep learning and density-based analysis of dynamic susceptibility contrast-enhanced magnetic resonance imaging (MRI).Quantitative imaging in medicine and surgery · 2024Article
- Marker-controlled watershed algorithm and fuzzy C-means clustering machine learning: automated segmentation of glioblastoma from MRI images in a case series.Annals of medicine and surgery (2012) · 2024Article
- Simple approach to quantify hypoxic-ischemic brain injury severity from computed tomography imaging files after cardiac arrest.Resuscitation · 2024Article
- Diagnosis of Alzheimer's Disease and Mild Cognitive Impairment Using Convolutional Neural Networks.Journal of Alzheimer's disease reports · 2024Article
- Determining regional brain growth in premature and mature infants in relation to age at MRI using deep neural networks.Scientific reports · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Brain image segmentation is one of the most time-consuming and challenging procedures in a clinical environment. Recently, a drastic increase in the number of brain disorders has been noted. This has indirectly led to an increased demand for automated brain segmentation solutions to assist medical experts in early diagnosis and treatment interventions. This paper aims to present a critical review of the recent trend in segmentation and classification methods for brain magnetic resonance images. Various segmentation methods ranging from simple intensity-based to high-level segmentation approaches such as machine learning, metaheuristic, deep learning, and hybridization are included in the present review. Common issues, advantages, and disadvantages of brain image segmentation methods are also discussed to provide a better understanding of the strengths and limitations of existing methods. From this review, it is found that deep learning-based and hybrid-based metaheuristic approaches are more efficient for the reliable segmentation of brain tumors. However, these methods fall behind in terms of computation and memory complexity.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.