Resolution preservation over architectural complexity: high-resolution patch-based semi-supervised learning for pancreas segmentation from CT images
DOI: https://doi.org/10.3846/ntcs.2026.26765Abstract
Precise segmentation of the pancreas from computed tomography (CT) scans is critical for diagnosis and surgical planning but remains challenging due to the organ’s small volume (<0.5% of the abdomen), high anatomical variability, and low contrast with surrounding tissues. Conventional deep learning methods typically downsample inputs to fit GPU memory, destroying fine-grained boundary information essential for small-organ delineation. This study proposes a high-resolution patch-based framework that preserves native CT resolution (512 × 512) through 256 × 256 patch extraction with domain-specific Hounsfield unit (HU) windowing. A lightweight 2D U-Net (7.8M parameters) achieves 0.809±0.015 Dice (peak 0.849) on the Medical Segmentation Decathlon (MSD) Task07 Pancreas dataset, competitive with nnU-Net (0.823 Dice, 88M parameters) and far above SAM (0.705) and MedSAM (0.439). Five-fold cross-validation across all 281 volumes yields a population-level Dice of 0.630±0.025. Integrating uncertainty-aware mean teacher (UA-MT) semi-supervised learning recovers 99.3% of supervised performance using only 50% of labelled data, with reproducible results across three seeds (σ = 0.012). On 80 external TCIA volumes, UA-MT (0.603 Dice) outperforms the supervised model (0.544), demonstrating superior cross-domain generalisation.
Keywords:
pancreas segmentation, computed tomography, deep learning, resolution preservation, semi-supervised learning, uncertainty-aware mean teacher, patch-based segmentatio, convolutional neural networks, medical image segmentation, annotation efficiencyHow to Cite
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Copyright (c) 2026 The Author(s). Published by Vilnius Gediminas Technical University.
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Copyright (c) 2026 The Author(s). Published by Vilnius Gediminas Technical University.
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