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Related Experiment Video

Updated: Sep 12, 2025

High-throughput Image Analysis of Tumor Spheroids: A User-friendly Software Application to Measure the Size of Spheroids Automatically and Accurately

High-throughput Image Analysis of Tumor Spheroids: A User-friendly Software Application to Measure the Size of Spheroids Automatically and Accurately

Published on: July 8, 2014

Robustness evaluation of an artificial intelligence-based automatic contouring software in daily routine practice.

Jimmy Fontaine¹, Maud Suszko¹, Francesca di Franco²

¹Centre Marie Curie, Valence, France.

Physica Medica : PM : an International Journal Devoted to the Applications of Physics to Medicine and Biology : Official Journal of the Italian Association of Biomedical Physics (AIFB)

|August 8, 2025

Summary

AI contouring accuracy improved with software updates, demonstrating robustness to minor image variations. However, AI performance significantly decreased with increased image noise and spatial resolution degradation, highlighting the need for continuous quality control.

Keywords:

Artificial intelligence Auto segmentation Image quality Quality control Software updates

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Area of Science:

Medical Physics
Radiotherapy Technology
Artificial Intelligence in Medicine

Background:

AI-based automatic contouring offers efficiency gains in radiotherapy planning.
Rigorous validation and daily monitoring are crucial for AI contouring tools.
Understanding AI performance under varying conditions is essential for clinical adoption.

Purpose of the Study:

To evaluate the impact of AI software updates on contouring accuracy.
To assess how image quality variations influence AI contouring performance.
To validate AI contouring for radiotherapy applications.

Main Methods:

Analysis of two patient cohorts: software updates (40 CT scans) and image quality (20 patients).
Comparison of six Limbus AI software versions and three reconstruction algorithms (Standard, iDose, IMR).
Assessment of AI performance using Volumetric Dice Similarity Coefficient (vDSC) and 95% Hausdorff Distance (HD95%), with simulated noise and spatial resolution degradations.

Main Results:

AI contouring accuracy (vDSC) generally improved with software updates (mean DSC ≥ 0.75).
AI contouring showed robustness to minor reconstruction variations; median HD95% remained within acceptable limits (<12 mm).
Significant performance degradation observed with increased image noise (vDSC dropped from 89% to 30%) and spatial resolution reduction (vDSC from 87% to 70%).

Conclusions:

AI contouring accuracy is enhanced by software updates and robust to minor reconstruction variations.
AI performance is highly sensitive to image noise and spatial resolution degradation.
Continuous validation and quality control are essential for reliable AI contouring in radiotherapy.