Pancreas Segmentation in Computed Tomography Using an Atlas-Guided Ensemble and Swin Transformer Backbone

• Felipe R. S. Teles UFMA
• Neilson P. Ribeiro UFMA/ IFMA
• Celso L. S. Soares Filho UFMA
• Luana B. da Cruz UFCA
• João O. B. Diniz UFMA / IFMA
• Geraldo B. Júnior UFMA
• Anselmo C. de Paiva UFMA

DOI: https://doi.org/10.5753/sbcas.2026.21333

Abstract

Automatic segmentation of the pancreas in abdominal computed tomography scans is a challenging task due to the organ’s anatomical variability and low contrast. This work proposes a deep learning-based method structured in three stages: (1) filtering irrelevant slices with a Convolutional Neural Network, (2) detection of the region of interest via probabilistic atlas, and (3) final segmentation using an ensemble of convolutional and transformer-based networks. The experiments achieve an average Dice score of 78.55%. The results demonstrate competitive performance compared to recent pancreas segmentation methods, combining preprocessing and robust deep learning techniques.

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