PRNet: Progressive Reduction Network for Accurate Colorectal Polyp Segmentation in Uncertain Regions

PRNet: A Progressive Reduction Network for Colorectal Polyp Segmentation in Uncertain Regions

PRNet is a novel deep learning network designed for precise segmentation of colorectal polyps, particularly in regions with high uncertainty. It employs a progressive reduction strategy to effectively handle ambiguous boundaries and improve segmentation accuracy. The network progressively reduces the uncertainty in the segmentation process by iteratively refining the predicted polyp boundary. This approach allows PRNet to achieve superior performance compared to existing methods, especially in challenging cases where the polyp boundaries are poorly defined or obscured by surrounding tissues.

Key features of PRNet include:

• Progressive Reduction Strategy: The network iteratively refines the predicted polyp boundary, reducing uncertainty with each step.
• Efficient Architecture: PRNet utilizes a lightweight architecture that is computationally efficient and can be easily implemented on resource-constrained devices.
• Robust Performance: Extensive evaluation on a benchmark dataset demonstrates that PRNet achieves state-of-the-art performance, particularly in handling ambiguous boundaries.

PRNet offers a significant advancement in colorectal polyp segmentation, paving the way for more accurate and reliable automated polyp detection and diagnosis. It holds the potential to improve clinical workflow and facilitate early intervention for colorectal cancer.