Comparison of Semi-Automated and AI-Based Segmentation for AAA Biomechanical Analysis

Główne pojęcia

The study compares semi-automated and AI-based segmentation methods for AAA biomechanical analysis, revealing slightly higher stress values with AI segmentation due to larger lumen surface areas.

Streszczenie

The study compared stress calculations in abdominal aortic aneurysms (AAAs) using semi-automated and AI-based segmentations. The AI method led to slightly higher peak and 99th percentile maximum principal stress values due to larger lumen surfaces. Despite differences in stress magnitudes, the distribution patterns remained similar across both segmentation methods. The study highlights the importance of accurate lumen boundary determination, often underestimated in literature.

Statystyki

Ten different AAA contrast-enhanced CT images were segmented semi-automatically by an analyst.
Automated neural network-based segmentation required only 1-2 minutes per patient.
Eight out of ten cases showed higher maximum principal stress values with automatic segmentation.
Tetrahedral finite element meshes varied between 520,000 - 920,000 elements depending on the patient-specific aneurysm.
Relative difference in number of elements between semi-automatic and automatic segmentations ranged from -13% to +15%.

Cytaty

Kluczowe wnioski z

Towards Full Automation of Geometry Extraction for Biomechanical Analysis of Abdominal Aortic Aneurysm; Neural Network-Based versus Classical Methodologies

by Farah Alkhat... o arxiv.org 03-13-2024

https://arxiv.org/pdf/2403.07238.pdf

Głębsze pytania

How can the findings of this study impact the clinical workflow for AAA biomechanical analysis?

The findings of this study have significant implications for the clinical workflow in AAA biomechanical analysis. The comparison between semi-automated and AI-based segmentation methods shows that fully automated pipelines can be reliable and efficient in generating patient-specific geometries for stress calculations. This suggests that AI algorithms can streamline the process, reducing human effort and time required for segmentation tasks. Clinically, this means faster turnaround times from CT scans to stress assessment, enabling quicker decision-making regarding treatment strategies for patients with AAAs.

What are the implications of relying on normalized measures of stress rather than peak values?

Relying on normalized measures of stress rather than peak values has important implications in ensuring a more consistent and standardized approach to assessing AAA biomechanics. Normalized measures provide a relative comparison across different patients or models by scaling stresses based on an average value or another reference point. This approach helps mitigate variations caused by differences in segmentations or mesh quality, leading to more robust and comparable results. By focusing on normalized measures, clinicians can better assess rupture risk across a population consistently.

How can advancements in AI algorithms further improve accuracy in lumen boundary determination?

Advancements in AI algorithms offer promising opportunities to enhance accuracy in lumen boundary determination for AAA biomechanical analysis. Improved deep learning techniques can enable AI systems to learn complex patterns within medical images, leading to more precise segmentation results. By training AI models on diverse datasets with annotated ground truth data, these algorithms can become adept at identifying subtle features like lumen boundaries accurately.
Additionally, incorporating feedback mechanisms into AI systems allows continuous learning and refinement based on user input or corrections made during post-processing steps.
Overall, advancements in AI algorithms hold great potential for improving the efficiency and accuracy of lumen boundary determination processes essential for accurate biomechanical analyses of AAAs.