ReviewBioengineering (Basel, Switzerland)2024
Advances in Medical Image Segmentation: A Comprehensive Review of Traditional, Deep Learning and Hybrid Approaches.
Review in Bioengineering (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 76 papers, 1 of them a synthesis that pooled it.
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
76 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep learning for cardiac CT segmentation for congenital heart disease: A systematic review.PloS one · 2026Pooled it
- Microfluidic Light-Scattering Imaging Coupled with Deep Learning for Label-Free Single-Cell Classification of Lymphoma Cells.Biosensors · 2026Article
- A UAV-based semi-supervised segmentation framework for pine wilt disease via vegetation Index-RGB synergy.Plant phenomics (Washington, D.C.) · 2026Article
- Artificial Intelligence-Powered Histopathology in Stem Cell Research: Bridging Morphology, Function, and Omics.Current issues in molecular biology · 2026Review
- Contrast-Enhanced CT of the Coelom in a Female Bald Eagle (Animals : an open access journal from MDPI · 2026Article
- Systematic evaluation of magnetic particle imaging quantification strategies in biologically relevant scenarios using anatomically correct 3D printed mouse phantoms.Physics in medicine and biology · 2026Article
- Cascaded deep learning for automated lung cancer tumour burden quantification on [Quantitative imaging in medicine and surgery · 2026Article
- U-Swing: An Adaptive U-Net and Swin Fusion for WB-MRI Whole Spine Bone Marrow Segmentation.Journal of imaging informatics in medicine · 2026Article
- Quantifying User Satisfaction: Weighted Metric Approach for Evaluating Deep Learning-Based Thigh MRI Segmentations.Diagnostics (Basel, Switzerland) · 2026Article
- From pixels to precision: a narrative review of AI-driven 3D morphological analysis and digital twinning in alveolar cleft management.Translational pediatrics · 2026Review
- A generalizable computational framework for integrating heterogeneous biomarkers into interpretable scalar risk representations.BMC medical informatics and decision making · 2026Article
- Adversarial vulnerability and robustness of deep learning models for panoramic dental X-ray segmentation.Scientific reports · 2026Article
- Balancing accuracy and efficiency in lumbar spine image segmentation using multi-scale attention and residual learning.Scientific reports · 2026Article
- Deep Learning-Based Automated Segmentation and Quantification of the Ellipsoid Zone and the RPE-Bruch's Membrane Complex in Healthy Subjects and in Geographic Atrophy.Diagnostics (Basel, Switzerland) · 2026Article
- A Pilot Study Protocol for AI-Assisted Interpretation of Chest X-rays for Pulmonary Abnormalities in Uganda.Cureus · 2026Article
- QCNN-Swin-UNet: Quantum Convolutional Neural Network Integrated with Optimized Swin-UNet for Efficient Liver Tumor Segmentation and Classification on Edge Devices.Journal of imaging informatics in medicine · 2026Article
- Cracking the Code: Computational Image Analysis Tools for Histopathological and Morphometric Insights.Journal of imaging · 2026Review
- Quality Assurance Strategies for Brain State Characterization by MEMRI.bioRxiv : the preprint server for biology · 2026Article
- DNAi: an open-source AI tool for unbiased DNA fiber analysis.Nucleic acids research · 2026Article
- RetinaDetachNet: Automated Deep Learning Quantification of Photoreceptor Cell Death for Neuroprotection Studies in Experimental Retinal Detachment.Translational vision science & technology · 2026Article
16 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Medical image segmentation plays a critical role in accurate diagnosis and treatment planning, enabling precise analysis across a wide range of clinical tasks. This review begins by offering a comprehensive overview of traditional segmentation techniques, including thresholding, edge-based methods, region-based approaches, clustering, and graph-based segmentation. While these methods are computationally efficient and interpretable, they often face significant challenges when applied to complex, noisy, or variable medical images. The central focus of this review is the transformative impact of deep learning on medical image segmentation. We delve into prominent deep learning architectures such as Convolutional Neural Networks (CNNs), Fully Convolutional Networks (FCNs), U-Net, Recurrent Neural Networks (RNNs), Adversarial Networks (GANs), and Autoencoders (AEs). Each architecture is analyzed in terms of its structural foundation and specific application to medical image segmentation, illustrating how these models have enhanced segmentation accuracy across various clinical contexts. Finally, the review examines the integration of deep learning with traditional segmentation methods, addressing the limitations of both approaches. These hybrid strategies offer improved segmentation performance, particularly in challenging scenarios involving weak edges, noise, or inconsistent intensities. By synthesizing recent advancements, this review provides a detailed resource for researchers and practitioners, offering valuable insights into the current landscape and future directions of medical image segmentation.
Indexed as
Identifiers
What Socratic 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 Socratic graph.