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Related Experiment Video

Updated: Nov 12, 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

Real-Time Polyp Detection, Localization and Segmentation in Colonoscopy Using Deep Learning.

Debesh Jha^1,2, Sharib Ali^2,3, Nikhil Kumar Tomar¹

¹SimulaMet0167OsloNorway.

IEEE Access : Practical Innovations, Open Solutions

|March 22, 2021

Summary

This summary is machine-generated.

Benchmarking computer-aided polyp detection and segmentation methods is crucial for advancing colonoscopy. ColonSegNet offers a superior balance of accuracy and speed for real-time polyp identification and delineation.

Keywords:

ColonSegNet Kvasir-SEG Medical image segmentation benchmarking colonoscopy deep learning detection localisation polyps

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Area of Science:

Medical Imaging
Computer Vision
Gastroenterology

Background:

Computer-aided detection, localization, and segmentation methods enhance colonoscopy.
Numerous methods exist for automatic polyp detection and segmentation, but benchmarking remains challenging.
Reproducible benchmarking is essential for fair comparison and guiding development in automated polyp tasks.

Purpose of the Study:

To benchmark state-of-the-art computer vision methods for polyp detection, localization, and segmentation using the Kvasir-SEG dataset.
To evaluate both the accuracy and speed of various methods.
To identify methods offering a favorable trade-off between performance and efficiency for clinical application.

Main Methods:

Utilized the Kvasir-SEG dataset, an open-access collection of colonoscopy images.
Benchmarked several recent state-of-the-art computer vision methods.
Evaluated methods based on accuracy metrics (Average Precision, Mean IoU, Dice Coefficient) and processing speed (frames per second).

Main Results:

ColonSegNet demonstrated a strong trade-off, achieving an Average Precision of 0.8000 and Mean IoU of 0.8100 with a speed of 180 frames per second for detection and localization.
For segmentation, ColonSegNet achieved a Dice Coefficient of 0.8206 and the highest average speed of 182.38 frames per second.
The study highlighted that while many methods show competitive accuracy, speed is a critical factor for real-time applications.

Conclusions:

Benchmarking deep learning methods is vital for automated, real-time polyp identification and delineation in colonoscopy.
ColonSegNet presents a promising solution, offering excellent accuracy and speed.
These advancements have the potential to transform clinical practices and reduce miss-detection rates in colonoscopies.