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Updated: Apr 30, 2026
Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
Published on: July 5, 2024
A systematic review of transformer-enhanced UNet architectures for 3D medical image segmentation: Trends, challenges,
Tuniki Krishnaveni1, Sreedhar Kollem1
1Department of ECE, SR University, Warangal, Telangana 506371, India.
Background:
Accurate 3D medical image segmentation plays an important role in diagnosing and treating tumors; however, it is difficult due to biological intricacies, noise within images, and insufficient training data. UNet-based models have demonstrated impressive results but tend to lack the ability to capture long-range dependencies in volumetric data.
Objective:
This systematic review highlights current trends in recent UNet architectures utilizing Transformers for 3D medical imaging segmentation and tumor detection, focusing on architecture design, clinical applicability, and research challenges.
Methods:
A systematic literature review was carried out based on the PRISMA guideline, involving research articles in the period 2016-2025 published in IEEE Xplore, SpringerLink, Elsevier, PubMed, Wiley, and arXiv. In total, 87 top-tier articles were selected and analyzed according to the developed taxonomy ATD-TᵣEEv, which is a six-dimension model involving aspects such as Architecture, Token/Attention, Data and Modality, Training/Pretraining, Efficiency, and Robustness of Evaluation.
Results:
Transformer-based hybrid models like TransUNet, FET-UNet, DS-UNETR+ +, VMAXL-UNet, and EfficientNet-Enhanced UNet demonstrated better Dice scores (0.81-0.96) in standard datasets such as BraTS (2019-2021), KiTS19, Synapse, and CE-MRI because of their multi-level attention and modeling of global contexts. Nevertheless, there are still some problems that need addressing, such as GPU memory usage, lack of statistical validation, data imbalance, and insufficient evaluation under noisy or incomplete clinical conditions.
Conclusion:
In addition to summarizing previous works, this review offers an analytical taxonomy (ATD-TᵣEEv) which incorporates architectural tendencies, analyzes efficiency-fairness trade-offs, and considers clinical translation problems. The presented taxonomy can be used as a basis for further research on efficient, fair, and clinically reliable transformer-based image segmentation approaches.
