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Related Experiment Video

Updated: Feb 6, 2026

$Comparison of Agreement and Accuracy using Binocular Wavefront Optometer with Autorefractor and Phoropter$

Comparison of Agreement and Accuracy using Binocular Wavefront Optometer with Autorefractor and Phoropter

Published on: September 16, 2025

Agreement and reliability statistics for shapes.

Travis B Smith¹, Ning Smith²

¹Casey Eye Institute, Oregon Health & Science University, Portland, Oregon, United States of America.

|August 24, 2018

Summary

This summary is machine-generated.

This study introduces a new method to measure shape reliability using the Manhattan norm, improving accuracy over area-based measurements for medical image segmentation. This shape-sensitive approach ensures comprehensive assessment of measurement consistency.

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Area of Science:

Medical image analysis
Quantitative shape analysis
Biomedical engineering

Background:

Manual segmentation of medical images is crucial but can suffer from inter-observer variability.
Existing reliability metrics often focus on area overlap, potentially missing shape-specific variations.
Need for robust methods to quantify agreement and reliability in shape measurements.

Purpose of the Study:

To develop and present a novel methodology for assessing agreement and reliability among shapes.
To apply this methodology to rasterized, binary-valued shapes from medical image segmentation.
To generalize the approach to N dimensions and various data types.

Main Methods:

Formulation of shape variance, shape correlation, and shape intraclass correlation coefficient (ICC).
Utilized the Manhattan norm as a distance metric to quantify differences between shapes.
Demonstrated applications in 1-D (line segments) and 2-D (regions) with example calculations.

Main Results:

The shape-sensitive methodology accurately captures all variations in shape measurements.
Simulated reliability analysis showed improved accuracy compared to area-based ICC.
Shape ICC provides a more precise estimate of measurement reliability than area ICC.

Conclusions:

The proposed shape analysis methodology offers a more accurate assessment of reliability in shape measurements.
This method is particularly valuable for evaluating the consistency of manual delineations in medical imaging.
The framework is adaptable for N-dimensional data and various quantitative analyses.