SLIC-Former: a superpixel-guided transformer framework for automatic liver segmentation in CT images.

Sarah F Elqersh, Amira Y Haikal, Mahmoud M Saafan, Noha A Sakr

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Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 authors.

Sarah F ElqershDepartment of Computers and Systems, Faculty of Engineering, Mansoura University, Mansoura, 35516, Egypt. sarahelqersh174@std.mans.edu.eg.

Amira Y HaikalDepartment of Computers and Systems, Faculty of Engineering, Mansoura University, Mansoura, 35516, Egypt.

Mahmoud M SaafanDepartment of Computers and Systems, Faculty of Engineering, Mansoura University, Mansoura, 35516, Egypt.

Noha A SakrDepartment of Computers and Systems, Faculty of Engineering, Mansoura University, Mansoura, 35516, Egypt.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Accurate and efficient liver segmentation from computed tomography (CT) images remains a critical challenging task due to the organ's irregular shape, variable intensity, and lies close to surrounding organs with similar appearance. In this study, we propose SLIC-Former, a superpixel-guided transformer framework for automatic liver segmentation in abdominal CT scans. The method replaces fixed square image patches with adaptive superpixels generated by the Simple Linear Iterative Clustering (SLIC) algorithm, so that each token follows anatomical boundaries and reduces redundant computation. Quantitative evaluation on the Liver Tumor Segmentation (LiTS) dataset demonstrated strong segmentation performance, achieving a Dice coefficient of 0.93, an IoU of 0.87, and a VOE of 13.5%, indicating high overlap with expert annotations. Qualitative results confirm that the model produces smooth and coherent liver masks. Overall, SLIC-Former offers an accurate and computationally efficient tool for automatic liver segmentation and provides a promising basis for future extensions to more organs, larger datasets, and clinical decision-support systems.

Indexed as

Image Processing, Computer-AssistedLiverLiver NeoplasmsTomography, X-Ray ComputedAlgorithmsClustering AlgorithmsHumansComputed Tomography (CT)Deep learningLiver segmentationMedical image analysisSuperpixels (SLIC)Vision transformers

Identifiers

PMID42432062

DOI10.1038/s41598-026-60656-x

PMCPMC13354556

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