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Comparing the Clinical Viability of Automated Fundus Image Segmentation Methods.

Gorana Gojić^1,2, Veljko B Petrović², Dinu Dragan²

¹The Institute for Artificial Intelligence Research and Development of Serbia, 21102 Novi Sad, Serbia.

Sensors (Basel, Switzerland)

|December 11, 2022

Summary

This summary is machine-generated.

Automatic blood vessel segmentation using deep learning shows promise but isn't clinically viable alone. Objective metrics don't reflect real-world clinical usefulness for retinal vascular disease diagnosis.

Keywords:

clinical viability convolutional neural networks fundus image objective metrics segmentation segmentation mask subjective assessment subjective metrics

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From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data

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Area of Science:

Ophthalmology
Medical Imaging
Artificial Intelligence

Background:

Convolutional neural networks (CNNs) are widely used for automatic blood vessel segmentation in fundus images.
High objective performance metrics do not guarantee clinical applicability of these segmentation masks.

Purpose of the Study:

To assess the clinical viability of automatically generated blood vessel segmentation masks for diagnosing retinal vascular diseases.
To compare the clinical quality of segmentation masks generated by different methods.

Main Methods:

A pilot study involving five experienced ophthalmologists evaluating segmentation masks.
Ophthalmologists ranked different blood vessel segmentation methods based on clinical quality.
Correlation analysis between objective performance metrics and subjective clinical evaluation.

Main Results:

Automatic segmentation masks showed low classification accuracy, limiting their use as a standalone diagnostic tool.
Ophthalmologists' rankings indicated subjective performance differences among methods with high intra-observer consistency.
Objective metrics did not correlate with subjective clinical assessments, questioning their utility in selecting clinically robust methods.

Conclusions:

Current automatic blood vessel segmentation methods are not clinically viable as standalone resources for diagnosing retinal vascular diseases.
Subjective clinical evaluation is crucial and may not align with standard objective performance metrics.
Future research should focus on developing methods that correlate objective performance with clinical utility.