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Updated: Sep 18, 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

Deep learning model for gastrointestinal polyp segmentation.

Zitong Wang¹, Zeyi Wang², Pengyu Sun³

¹Imperial College London, London, South Kensington, United Kingdom.

Peerj. Computer Science

|June 26, 2025

Summary

This summary is machine-generated.

A new deep learning model enhances gastrointestinal polyp segmentation accuracy. This AI tool improves early colorectal cancer detection by analyzing colonoscopy images, aiding clinical diagnosis.

Keywords:

Deep learning Gastrointestinal polyp Image segmentation Kvasir-SEG Transformer

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Last Updated: Sep 18, 2025

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

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Published on: November 30, 2022

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Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

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Published on: July 5, 2024

Area of Science:

Medical Imaging and Artificial Intelligence
Gastroenterology and Oncology

Background:

Colorectal cancer is a leading global cause of mortality.
Early detection significantly improves patient outcomes.
Colonoscopy is effective but polyp segmentation faces challenges due to interpretation variability.

Purpose of the Study:

To introduce a novel deep learning architecture for gastrointestinal polyp segmentation.
To improve the accuracy and efficiency of polyp detection in colonoscopy images.
To address challenges in automated polyp segmentation for clinical applications.

Main Methods:

Developed an encoder-decoder deep learning architecture utilizing a pre-trained ConvNeXt model as the encoder.
Incorporated a cross-attention mechanism for enhanced feature retention between encoder and decoder.
Introduced a Residual Transformer Block with self-attention in the decoder for long-term dependency learning.

Main Results:

Achieved a Dice coefficient of 0.8715 on the Kvasir-SEG dataset.
Obtained a mean intersection over union (mIoU) of 0.8021.
Demonstrated state-of-the-art performance in gastrointestinal polyp segmentation.

Conclusions:

The proposed deep learning model significantly advances gastrointestinal polyp segmentation.
The methodology shows potential for integration into clinical pipelines for automated polyp detection and diagnosis.
This approach can aid in earlier and more accurate identification of colorectal polyps.