A Review of Transformer Models for Liver Segmentation from CT Images
DOI:
https://doi.org/10.18287/JBPE26.12.040201Keywords:
Transformer architectures, Liver segmentation, Medical image analysis, Computed tomography, Attention mechanisms, Deep learningAbstract
Accurate liver segmentation in computed tomography imaging is a critical component of diagnostic workflows, surgical planning, and therapy monitoring. Transformer architectures have recently reshaped medical image analysis by providing global contextual modeling capabilities that exceed those of traditional convolutional networks. This review synthesizes Transformer-based liver segmentation models identified through a PRISMA-guided literature search. The included models are systematically categorized according to three principal axes: their architectural integration strategy, the attention mechanism scope, and their inherent transformer dimensionality. Performance comparisons across several benchmarks, LiTS, 3DIRCADb, Synapse, and SLIVER07, demonstrate that contemporary approaches regularly achieve Dice similarity coefficients above 97%. In addition, we analyze training practices, computational complexity, and design trade-offs that influence model performance and clinical applicability. The review concludes by highlighting persistent challenges and discussing promising research avenues toward efficient, generalizable, and clinically deployable Transformer-based liver segmentation systems.
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