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Enhanced U-Net: A Feature Enhancement Network for Polyp Segmentation.

Krushi Patel¹, Andrés M Bur², Guanghui Wang³

¹Department of Electrical Engineering and Computer Science, University of Kansas, Lawrence KS, USA, 66045.

Proceedings of the International Robots & Vision Conference. International Robots & Vision Conference

|August 9, 2021

Summary

This summary is machine-generated.

Detecting colorectal polyps via colonoscopy is crucial for preventing cancer. This study introduces a novel feature enhancement network with Semantic Feature Enhance Module (SFEM) and Adaptive Global Context Module (AGCM) for improved polyp segmentation accuracy.

Keywords:

Polyp segmentation U-Net global context semantic feature

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Area of Science:

Medical Imaging
Artificial Intelligence
Gastroenterology

Background:

Colorectal polyps are precursors to colorectal cancer, necessitating accurate detection during colonoscopy.
Polyp segmentation in colonoscopic images is challenging due to variations in polyp appearance, low contrast, and background similarity.

Purpose of the Study:

To develop an advanced feature enhancement network for precise polyp segmentation in colonoscopy.
To improve the accuracy of polyp detection, aiding in early diagnosis and prevention of colorectal cancer.

Main Methods:

Proposed a novel feature enhancement network incorporating a Semantic Feature Enhance Module (SFEM) to boost semantic information.
Introduced an Adaptive Global Context Module (AGCM) to selectively focus on significant and challenging fine-grained encoder features.
Integrated SFEM and AGCM to enhance feature representation layer by layer for improved segmentation quality.

Main Results:

The proposed network demonstrated superior performance in polyp segmentation across five diverse colonoscopy datasets.
The integration of SFEM and AGCM significantly improved feature quality and representation compared to existing methods.
Achieved state-of-the-art results in accurate polyp segmentation.

Conclusions:

The developed feature enhancement network effectively addresses the challenges of polyp segmentation in colonoscopy.
The novel SFEM and AGCM modules contribute to enhanced accuracy and robustness in polyp detection.
This approach holds significant potential for improving colorectal cancer screening and diagnosis.