Automated Aneurysm Boundary Detection and Volume Estimation Using Deep Learning

TL;DR

This paper introduces an automated deep learning method to detect aneurysm boundaries and estimate their volume using medical imaging, reducing the need for manual analysis.

Contribution

The novel approach combines pre-trained neural networks and expert systems to estimate aneurysm volume without requiring pixel-wise aorta segmentations.

Findings

The method achieved an R2 score of 71% for predicting the aneurysm start point.

It achieved an R2 score of 92% for estimating aneurysm volume.

The approach reduces reliance on annotated datasets for clinical aneurysm analysis.

Abstract

Background/Objective: Precise aneurysm volume measurement offers a transformative edge for risk assessment and treatment planning in clinical settings. Currently, clinical assessments rely heavily on manual review of medical imaging, a process that is time-consuming and prone to inter-observer variability. The widely accepted standard of care primarily focuses on measuring aneurysm diameter at its widest point, providing a limited perspective on aneurysm morphology and lacking efficient methods to measure aneurysm volumes. Yet, volume measurement can offer deeper insight into aneurysm progression and severity. In this study, we propose an automated approach that leverages the strengths of pre-trained neural networks and expert systems to delineate aneurysm boundaries and compute volumes on an unannotated dataset from 60 patients. The dataset includes slice-level start/end annotations for aneurysm but no pixel-wise aorta segmentations. Method: Our method utilizes a pre-trained UNet to automatically locate the aorta, employs SAM2 to track the aorta through vascular irregularities such as aneurysms down to the iliac bifurcation, and finally uses a Long Short-Term Memory (LSTM) network or expert system to identify the beginning and end points of the aneurysm within the aorta. Results: Despite no manual aorta segmentation, our approach achieves promising accuracy, predicting the aneurysm start point with an R2 score of 71%, the end point with an R2 score of 76%, and the volume with an R2 score of 92%. Conclusions: This technique has the potential to facilitate large-scale aneurysm analysis and improve clinical decision-making by reducing dependence on annotated datasets.

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