ReviewDiagnostics (Basel, Switzerland)2022
Review of Semantic Segmentation of Medical Images Using Modified Architectures of UNET.
Review in Diagnostics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers, 1 of them a synthesis that pooled it.
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
39 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep learning for head and neck radiation dose prediction: a systematic review and meta-analysis.Scientific reports · 2025Pooled it
- Keypoint detection network for needle localization on intra-procedural MRI in MRI-guided liver interventions.International journal of computer assisted radiology and surgery · 2026Article
- Efficient Cerebral Infarction Segmentation Using U-Net and U-Net3 + Models.Journal of imaging informatics in medicine · 2026Article
- SBM-Attention U-Net: A Hybrid Transformer Network for Liver Tumor Segmentation in Medical Images.Sensors (Basel, Switzerland) · 2026Article
- Deep Learning-Based Semantic Segmentation and Classification of Otoscopic Images for Otitis Media Diagnosis and Health Promotion.Diagnostics (Basel, Switzerland) · 2026Article
- Artificial intelligence for ultrasound-based differentiation of soft tissue liposarcoma from benign masses.Frontiers in medicine · 2026Article
- Modeling epithelial wound closure dynamics with AI: A comparative study across cell types.Regenerative therapy · 2025Article
- AI-assisted recurrent laryngeal nerve identification during endoscopic/robotic thyroid surgery based on the CMC-UNet model: a multicenter retrospective study.Journal of robotic surgery · 2025Article
- PA OmniNet: A retraining-free, generalizable deep learning framework for robust photoacoustic image reconstruction.Photoacoustics · 2025Article
- An Ensemble Learning for Automatic Stroke Lesion Segmentation Using Compressive Sensing and Multi-Resolution U-Net.Biomimetics (Basel, Switzerland) · 2025Article
- Deep Learning Spinal Cord Segmentation Based on B0 Reference for Diffusion Tensor Imaging Analysis in Cervical Spondylotic Myelopathy.Bioengineering (Basel, Switzerland) · 2025Article
- Developments in Deep Learning Artificial Neural Network Techniques for Medical Image Analysis and Interpretation.Diagnostics (Basel, Switzerland) · 2025Review
- Multi-scale conv-attention U-Net for medical image segmentation.Scientific reports · 2025Article
- Advances in Deep Learning for Semantic Segmentation of Low-Contrast Images: A Systematic Review of Methods, Challenges, and Future Directions.Sensors (Basel, Switzerland) · 2025Review
- ProtoSAM-2D: 2D Semantic Segment Anything Model with Mask-Level Prototype-Learning and Distillation.Proceedings of SPIE--the International Society for Optical Engineering · 2025Article
- Advancements in Hysteroscopic Diagnosis and Management of Endometritis.Diagnostics (Basel, Switzerland) · 2025Review
- Comparing UNet configurations for anthropogenic geomorphic feature extraction from land surface parameters.PloS one · 2025Article
- LFE-UNet: A Lightweight Full-Encoder U-shaped Network for Efficient Semantic Segmentation in Medical ImagingCurrent medical imaging · 2025Article
- Training a high accuracy model to visualize blood clots during mechanical thrombectomy for the treatment of Acute Ischemic Stroke.Frontiers in stroke · 2025Article
- Artificial Intelligence-Based Classification and Segmentation of Bladder Cancer in Cystoscope Images.Cancers · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In biomedical image analysis, information about the location and appearance of tumors and lesions is indispensable to aid doctors in treating and identifying the severity of diseases. Therefore, it is essential to segment the tumors and lesions. MRI, CT, PET, ultrasound, and X-ray are the different imaging systems to obtain this information. The well-known semantic segmentation technique is used in medical image analysis to identify and label regions of images. The semantic segmentation aims to divide the images into regions with comparable characteristics, including intensity, homogeneity, and texture. UNET is the deep learning network that segments the critical features. However, UNETs basic architecture cannot accurately segment complex MRI images. This review introduces the modified and improved models of UNET suitable for increasing segmentation accuracy.
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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 OpenQuestion graph.