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

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A Novel System for Measuring Pterygium's Progress Using Deep Learning.

Cheng Wan¹, Yiwei Shao¹, Chenghu Wang^2,3

¹College of Electronic Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China.

Frontiers in Medicine

|March 3, 2022

Summary

This summary is machine-generated.

This study introduces a deep learning system to automatically measure corneal pterygium invasion from eye images. The system accurately assesses pterygium width, aiding surgeons in treatment decisions for this common eye condition.

Keywords:

chi-square test computer-aided diagnosis deep learning image segmentation pterygium

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Area of Science:

Ophthalmology
Medical Imaging
Artificial Intelligence

Background:

Pterygium is a common ocular disease that can impair vision and eye movement when it invades the cornea.
Surgical intervention for pterygium is indicated when the corneal invasion exceeds 3 mm in width.
Accurate measurement of pterygium invasion is crucial for determining appropriate surgical treatment strategies.

Purpose of the Study:

To develop and validate a deep learning-based system for automated diagnosis and measurement of pterygium progression.
To assist ophthalmologists in surgical planning by providing efficient and accurate pterygium measurements from anterior segment images.
To classify pterygium symptom status based on image analysis.

Main Methods:

A three-module system was developed: cornea segmentation, pterygium segmentation, and measurement.
Convolutional neural networks (CNNs) were employed for segmentation tasks.
An improved U-Net++ model with Attention gates was utilized for pterygium segmentation to enhance accuracy for diverse shapes and sizes.

Main Results:

The cornea segmentation module achieved a Dice coefficient of 0.9620.
The pterygium segmentation module achieved a Dice coefficient of 0.9020.
The system demonstrated high consistency with expert clinical assessment, achieving a Kappa coefficient of 0.918 for final measurements.

Conclusions:

The proposed deep learning system accurately measures pterygium invasion width and classifies symptom status.
The system offers practical significance in assisting clinical decision-making for pterygium surgical treatment.
Automated analysis of anterior segment images provides an efficient tool for managing pterygium progression.