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Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

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Automated Audit and Self-Correction Algorithm for Seg-Hallucination Using MeshCNN-Based On-Demand Generative AI.

Sihwan Kim^1,2, Changmin Park^1,2, Gwanghyeon Jeon²

¹Department of Applied Bioengineering, Graduate School of Convergence Science and Technology, Seoul National University, Seoul 08826, Republic of Korea.

Bioengineering (Basel, Switzerland)

|January 24, 2025

Summary

We developed an automated algorithm to detect and fix Seg-Hallucinations in medical images, improving AI segmentation accuracy without needing ground truth data.

Keywords:

AI audit Seg-Hallucination anomaly screening segmentation uncertainty

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Area of Science:

Medical Imaging
Artificial Intelligence
Deep Learning

Background:

Deep learning models excel at medical image segmentation but struggle with generalization and can produce unrealistic Seg-Hallucinations.
Seg-Hallucinations lead to inaccurate quantitative analysis and loss of critical imaging biomarker information.
Existing methods for auditing or correcting Seg-Hallucinations are limited.

Purpose of the Study:

To propose an automated Seg-Hallucination surveillance and correction (ASHSC) algorithm.
To address Seg-Hallucinations using only 3D organ mask information from CT images, without ground truth.
To enhance the reliability and efficiency of deep learning-based medical image segmentation.

Main Methods:

Developed a two-stage, on-demand ASHSC algorithm using mesh-based convolutional neural networks and generative AI.
Utilized 3D organ mask information from CT scans for training and evaluation on publicly available datasets.
Employed segmentation quality level (SQ-level)-based surveillance and on-demand correction strategies.

Main Results:

The surveillance stage achieved high performance with an AUROC of 0.94 ± 0.01, sensitivity of 0.82 ± 0.03, specificity of 0.90 ± 0.01, and PPV of 0.92 ± 0.01.
The on-demand correction stage significantly improved all similarity metrics (Dice score, volume error, surface distance, Hausdorff distance) compared to AI-segmentation alone.
The ASHSC algorithm demonstrated effective Seg-Hallucination handling without ground truth, offering 3D guidance for uncertainty regions.

Conclusions:

The ASHSC algorithm effectively audits and corrects Seg-Hallucinations in deep learning-based medical image segmentation.
This approach eliminates the need for ground truth data, enhancing practicality and efficiency.
The ASHSC algorithm advances automated auditing and correction methodologies, improving the reliability of medical imaging analysis.