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Related Experiment Video

Updated: Jun 26, 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 Based Cystoscopy Image Enhancement.

Zixing Ye¹, Shun Luo², Lianpo Wang^2,3

¹Department of Urology, Peking Union Medical College Hospital, Beijing, China.

Journal of Endourology

|May 16, 2024

Summary

This summary is machine-generated.

This study introduces a deep learning method to improve cystoscopy images degraded by blood haze. The AI approach enhances image clarity and contrast, aiding more accurate diagnoses.

Keywords:

blood haze removal cystoscopy image enhancement deep learning

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Deep Learning-Based Segmentation of Cryo-Electron Tomograms

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Published on: November 11, 2022

Area of Science:

Medical Imaging
Artificial Intelligence
Computer Vision

Background:

Cystoscopy images suffer unique degradation due to underwater conditions, particularly blood haze from mucosal bleeding.
This blood haze blurs backgrounds, hindering accurate lesion assessment and potentially leading to misdiagnosis.

Purpose of the Study:

To develop and evaluate a deep learning-based method for enhancing cystoscopy images affected by blood haze.
To improve diagnostic accuracy by providing clearer, higher-contrast cystoscopy images.

Main Methods:

A two-part approach combining a Feature Fusion Attention Network (FFA-Net) for blood haze removal and a contrast enhancement algorithm.
Utilizing transfer learning and perceptual loss for effective blood haze mitigation.
Employing grayscale remapping and weighted fusion for contrast enhancement.

Main Results:

The blood haze removal stage achieved a 15% higher peak signal-to-noise ratio than traditional methods.
Superior average structural similarity (0.9269) and perceptual image patch similarity (0.1146) compared to existing techniques.
The enhancement stage improved contrast of vessels and tissues while preserving original colors.

Conclusions:

The proposed deep learning method significantly outperforms traditional approaches in both qualitative and quantitative evaluations.
AI-driven enhancement of cystoscopy images offers clearer visualization for improved medical diagnosis.