ReviewTomography (Ann Arbor, Mich.)2025
Medical Image Segmentation: A Comprehensive Review of Deep Learning-Based Methods.
Review in Tomography (Ann Arbor, Mich.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers.
What it found
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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.
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Who cites it
41 citing papers in PubMed.
- Clinician-accessible automated machine learning in oral healthcare: A systematic review.The Japanese dental science review · 2026Review
- In search of truth: evaluating concordance of AI-based anatomy segmentation models.Journal of medical imaging (Bellingham, Wash.) · 2026Article
- TransUNet-GradCAM: a hybrid transformer-U-Net with self-attention and explainable visualizations for foot ulcer segmentation.BMC medical imaging · 2026Article
- Intelligent Real-Time Healthcare and Biomedical Monitoring Systems: A Narrative Review of AI, IoT, and Emerging Technologies.Bioengineering (Basel, Switzerland) · 2026Review
- Deep Learning-Assisted Quality Control of Histology Teaching Slides: Detection and Localization of Tissue Fold Artifacts in H&E-Stained Images.Bioengineering (Basel, Switzerland) · 2026Article
- Faithful Explanation Regeneration via Cauchy-Schwarz Mixture Information Bottleneck.Entropy (Basel, Switzerland) · 2026Article
- Automatic Segmentation of Ischaemic Stroke Lesions Using Transformers and Convolutional Neural Networks Applied to Multimodal Neuroimaging.Sensors (Basel, Switzerland) · 2026Article
- Clinically Validated XAI for Calcified Plaque Segmentation in Coronary CT Angiography.Journal of clinical medicine · 2026Article
- Application of artificial intelligence in paediatric oncology imaging.Pediatric radiology · 2026Review
- Comparative Performance Evaluation of Federated and Centralized Learning for Velum and OTE Segmentation in Sleep Endoscopy Images.Journal of imaging informatics in medicine · 2026Article
- Masked autoencoder pretraining for peripheral nerve segmentation in ultrasound images.Scientific reports · 2026Article
- Abdominal multi-organ segmentation on 3D negative-contrast CT cholangiopancreatography: a comparative study of deep learning methods.Abdominal radiology (New York) · 2026Article
- A Multi-Strategy Enhanced Bionic-Inspired Secretary Bird Optimization Algorithm for Numerical Optimization and Artistic Image Segmentation.Biomimetics (Basel, Switzerland) · 2026Article
- Medical Image Segmentation Methods: A Decision-Guided Survey Covering 2D/3D CNNs, Transformers, VLMs, SAM-Based Models and Diffusion Approaches.Bioengineering (Basel, Switzerland) · 2026Review
- Segmentation and 3D Visualization of Spinal Motion Segments from MSCT Images Using a 3D U‑Net Framework.Journal of imaging informatics in medicine · 2026Article
- NURSE-AI: A Nurse-by-Design Framework for Multi-Sensor, AI-Enabled Chronic Wound Assessment in Community Healthcare.Sensors (Basel, Switzerland) · 2026Article
- Research on segmentation method of elderly cardiovascular disease feature images based on artificial intelligence multi-scale feature fusion.Scientific reports · 2026Article
- Foreign Body Response to Neuroimplantation: Machine Learning-Assisted Quantitative Analysis of Astrogliosis.International journal of molecular sciences · 2026Article
- Integrating AI Segmentation, Simulated Digital Twins, and Extended Reality into Medical Education: A Narrative Technical Review and Proof-of-Concept Case Study.Journal of personalized medicine · 2026Review
- Optimized K-means algorithm for image segmentation based on improved dung beetle algorithm.Scientific reports · 2026Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Medical image segmentation is a critical application of computer vision in the analysis of medical images. Its primary objective is to isolate regions of interest in medical images from the background, thereby assisting clinicians in accurately identifying lesions, their sizes, locations, and their relationships with surrounding tissues. However, compared to natural images, medical images present unique challenges, such as low resolution, poor contrast, inconsistency, and scattered target regions. Furthermore, the accuracy and stability of segmentation results are subject to more stringent requirements. In recent years, with the widespread application of Convolutional Neural Networks (CNNs) in computer vision, deep learning-based methods for medical image segmentation have become a focal point of research. This paper categorizes, reviews, and summarizes the current representative methods and research status in the field of medical image segmentation. A comparative analysis of relevant experiments is presented, along with an introduction to commonly used public datasets, performance evaluation metrics, and loss functions in medical image segmentation. Finally, potential future research directions and development trends in this field are predicted and analyzed.
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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.