MAPSeg: self-supervised colorectal polyp segmentation via memory-augmented framework and synthetic polyp simulation.

Introduction: Colorectal cancer originates in most cases from polyps that progressively become malignant over time and colonoscopy offers a highly studied early diagnostic strategy to ensure a timely treatment plan. Automatic image segmentation of polyps, using intelligent supervised approaches, achieved good performance, but the need of their large annotated datasets limits the clinical applicability. Methods: This study presents MAPSeg (Memory-Augmented Polyp Segmentation), a fully self-supervised and annotation-free framework for colorectal polyp segmentation, trained exclusively on images of healthy mucosa within an anomaly detection paradigm. The key novelty of MAPSeg mainly lies in SIMPO (Simulation of Polyps), a synthetic augmentation strategy that generates realistic polyp shapes and textures in a colon-specific context, combined with a memory-augmented encoder that models structural priors of normal tissue. Results: Extensive experiments demonstrate that MAPSeg outperforms the strongest unsupervised methods by approximately 23% in Intersection over Union and 12% in DICE score on the Hyper-Kvasir dataset and consistently maintains this performance margin across multiple out-of-distribution benchmarks, indicating strong generalization capability. Discussion: The results highlight MAPSeg supported by SIMPO as a viable solution for unsupervised colorectal polyp segmentation, significantly reducing the dependency on manual annotations while maintaining high segmentation accuracy.