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Updated: Jun 25, 2025

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

Iterative feedback-based models for image and video polyp segmentation.

Liang Wan¹, Zhihao Chen¹, Yefan Xiao¹

¹College of Intelligence and Computing, Tianjin University, Tianjin, 300350, China.

Computers in Biology and Medicine

|May 23, 2024

Summary

This summary is machine-generated.

This study introduces FlowICBNet for video polyp segmentation, enhancing colorectal cancer diagnosis. The novel method effectively refines segmentation using Iterative Feedback Units (IFU), overcoming challenges in endoscopic video.

Keywords:

Image polyp segmentation Iterative feedback Video polyp segmentation

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Area of Science:

Medical Imaging
Computer Vision
Artificial Intelligence

Background:

Accurate polyp segmentation in colonoscopy is vital for automated colorectal cancer diagnosis.
Existing deep learning methods often use one-stage pipelines with feature fusion or attention mechanisms.
Endoscopic imaging presents challenges like camera shake and frame defocusing impacting segmentation accuracy.

Purpose of the Study:

To propose FlowICBNet, an advanced deep learning model for video polyp segmentation.
To extend the efficacy of Iterative Feedback Units (IFU) from image to video polyp segmentation.
To address limitations of endoscopic imaging in polyp segmentation tasks.

Main Methods:

FlowICBNet extends Iterative Feedback Units (IFU) for video polyp segmentation.
The method incorporates Reference Frame Selection (RFS) and Flow Guided Warping (FGW) modules.
RFS and FGW modules select and utilize historical reference frames for improved segmentation.

Main Results:

FlowICBNet effectively mitigates challenges from camera shake and frame defocusing.
The proposed method significantly outperforms state-of-the-art techniques on large video polyp segmentation datasets.
Average metric improvements of 7.5% (SUN-SEG-Easy) and 7.4% (SUN-SEG-Hard) were achieved.

Conclusions:

FlowICBNet demonstrates superior performance in video polyp segmentation.
The IFU-based approach with RFS and FGW modules enhances robustness in endoscopic imaging.
This advancement holds promise for more accurate automated colorectal cancer diagnosis.