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Predicting human effort needed to correct auto-segmentations.

Da He^1,2, Jayaram K Udupa¹, Yubing Tong¹

¹Medical Image Processing Group, 602 Goddard building, 3710 Hamilton Walk, Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, United States.

Proceedings of Spie--The International Society for Optical Engineering

|July 3, 2024

Summary

This summary is machine-generated.

This study evaluates segmentation metrics for predicting manual correction effort in medical imaging. A novel Mendability Index (MIhd) shows the best performance, suggesting its utility in assessing auto-segmentation clinical value.

Keywords:

Auto-segmentation correction effort prediction segmentation metrics

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Area of Science:

Medical Imaging and Analysis
Computational Pathology
Artificial Intelligence in Medicine

Background:

Medical image auto-segmentation is crucial for personalized medicine and clinical efficiency.
Current metrics like Dice Coefficient (DC) and Hausdorff Distance (HD) do not directly account for human correction effort.
Existing metrics may not reliably indicate the manual effort needed for auto-segmentation refinement.

Purpose of the Study:

To investigate the efficacy of standard segmentation metrics (DC, HD, surDC, APL) and a novel Mendability Index (MI) in predicting human correction effort.
To develop and validate regression models for estimating manual segmentation adjustments.
To identify metrics that can reliably indicate the clinical value of auto-segmentations.

Main Methods:

Utilized 265 3D CT scans across three institutions and objects of interest.
Trained and tested linear and support vector regression models using auto-segmentations and ground truth segmentations.
Evaluated the predictive performance of DC, HD, surDC, APL, MI, and an improved variant, MIhd.

Main Results:

Meaningful prediction of human correction effort was achieved using segmentation metrics.
Prediction accuracy varied across different objects of interest.
The improved Mendability Index (MIhd) demonstrated superior prediction performance compared to other metrics.

Conclusions:

Segmentation metrics can predict human correction effort in auto-segmentation tasks.
The novel MIhd metric shows significant potential for reliably indicating the clinical value of auto-segmentations.
This research contributes to developing more efficient and clinically relevant auto-segmentation tools.