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Polyp segmentation with interference filtering and dynamic uncertainty mining.

Yunhua Zhang^1,2, Gang Yang¹, Congjin Gong¹

¹Northeastern University, Shenyang 110819, People's Republic of China.

Physics in Medicine and Biology

|February 21, 2024

Summary

This study introduces a two-stage method using a preprocessing sub-network and a dynamic uncertainty mining network for accurate polyp segmentation in colonoscopy images, improving early colorectal cancer detection.

Keywords:

colonoscopy medical image segmentation polyp segmentation preprocess

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Last Updated: May 5, 2026

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Area of Science:

Medical Imaging
Computer Vision
Artificial Intelligence

Background:

Accurate polyp segmentation is vital for early colorectal cancer diagnosis.
Image noise and ambiguous boundaries challenge existing segmentation methods.
Improving polyp segmentation performance is crucial for clinical decision-making.

Purpose of the Study:

To develop a novel two-stage method for accurate polyp segmentation.
To address challenges posed by image noise and unclear polyp boundaries.
To enhance the accuracy and generalization of polyp segmentation models.

Main Methods:

A two-stage approach combining a preprocessing sub-network (Pre-Net) and a dynamic uncertainty mining network (DUMNet).
Pre-Net filters interference regions in colonoscopy images.
DUMNet utilizes an uncertainty mining module (UMM) for coarse-to-fine segmentation by focusing on pixel confidences.

Main Results:

The proposed method achieved state-of-the-art performance on five benchmark datasets (ETIS, CVC-ClinicDB, CVC-ColonDB, EndoScene, Kvasir).
The Pre-Net demonstrated portability, enhancing the accuracy of existing segmentation models.
The DUMNet effectively improved segmentation by eliminating interference and mining uncertain regions.

Conclusions:

The novel two-stage method significantly improves polyp segmentation accuracy.
This advancement aids clinicians in precise diagnosis and reduces colorectal cancer risks.
The developed Pre-Net offers a versatile tool for enhancing current polyp segmentation techniques.