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Related Experiment Video

Updated: May 14, 2026

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

DSF-BRNet: Dual-Gated Semantic Fusion and Boundary Refinement for Efficient Endoscopic Polyp Segmentation.

Botao Liu¹, Changqi Shi¹, Ming Zhao²

¹School of Computer Science, Yangtze University, Jingzhou 434023, China.

Sensors (Basel, Switzerland)

|May 13, 2026

Summary

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A new deep learning model, DSF-BRNet, accurately segments colorectal polyps in colonoscopies. This method improves early cancer detection by enhancing lesion localization and boundary refinement for computer-aided diagnosis.

Area of Science:

Medical Imaging
Computer Vision
Artificial Intelligence

Background:

Accurate colorectal polyp segmentation is vital for colorectal cancer prevention.
Automated segmentation faces challenges like inter-class variance, complex backgrounds, and blurred boundaries.

Purpose of the Study:

To develop an efficient and accurate deep learning model for endoscopic polyp segmentation.
To address limitations in current automated polyp segmentation methods.

Main Methods:

Introduced Dual-Gated Semantic Fusion (DSF) module for feature alignment and semantic localization.
Implemented High-Frequency Boundary Refinement (HBR) module for contour sharpening.
Developed an Align-then-Refine framework for improved segmentation.

Keywords:

boundary refinement efficient network polyp segmentation real-time segmentation semantic fusion

Related Experiment Videos

Last Updated: May 14, 2026

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Main Results:

Achieved competitive performance on four public datasets (Kvasir-SEG, CVC-ClinicDB, CVC-ColonDB, ETIS-LaribPolypDB).
Obtained mean Dice scores of 0.943 on CVC-ClinicDB and 0.818 on ETIS-LaribPolypDB.
Demonstrated favorable computational efficiency with 25.55 M parameters and 80.08 FPS inference speed.

Conclusions:

The DSF-BRNet model effectively achieves accurate semantic localization and fine boundary preservation.
The method shows promise for real-time computer-aided diagnosis (CAD) in colonoscopy.