Advancing Colorectal Polyp Segmentation With Watershed Algorithm-Enhanced Parallel Self-Supervised Learning

Colorectal cancer (CRC) is a major health concern globally, known for its high prevalence and mortality rate. It typically arises from precancerous growths called polyps within the colon or rectum. Early detection and removal of these polyps are crucial for preventing CRC and improving patient outcomes. Traditional deep learning models require large amounts of labeled data, making them costly and time-consuming to develop. To address this, we present an innovative self-supervised learning (SSL) approach for colorectal polyp segmentation, which integrates watershed algorithm-enhanced pseudo-label generation with a novel parallel pretraining method. This approach leverages dual-task pre-training on image reconstruction and pseudo-segmentation to extract more relevant feature representations. Validated on the Kvasir-SEG dataset using three different models, our methodology demonstrates significant improvements in segmentation accuracy and efficiency, particularly in low-data scenarios, suggesting broader applicability in medical image analysis.