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Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

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Published on: April 13, 2013

Intracranial aneurysm detection: an object detection perspective.

Youssef Assis¹, Liang Liao^2,3,4, Fabien Pierre²

¹Université de Lorraine, CNRS, Inria, LORIA, 54000, Nancy, France. youssef.assis@loria.fr.

International Journal of Computer Assisted Radiology and Surgery

|April 17, 2024

Summary

This summary is machine-generated.

A new 3D object detection method improves intracranial aneurysm detection from 3D Time-Of-Flight Magnetic Resonance Angiography (3D TOF MRA) images. This approach offers advantages over segmentation-based methods for clinical use.

Keywords:

Computer-aided detection Deep learning Intracranial aneurysms Magnetic resonance angiography

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A Volumetric Method for Quantification of Cerebral Vasospasm in a Murine Model of Subarachnoid Hemorrhage

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Published on: July 28, 2018

Area of Science:

Medical Imaging
Artificial Intelligence
Radiology

Background:

Intracranial aneurysm detection is crucial in clinical practice.
Segmentation-based neural networks show promise but may not align with diagnostic needs.
Object detection is more relevant for clinical decision-making.

Purpose of the Study:

To introduce a 3D single-stage object detection method for small object detection, specifically intracranial aneurysms.
To address limitations of segmentation-based approaches in clinical settings.
To improve the efficiency and accuracy of aneurysm detection.

Main Methods:

Developed a 3D single-stage, anchor-free object detection method.
Incorporated fast data annotation and adapted data sampling/generation to handle class imbalance.
Utilized spherical representations for enhanced detection of small objects like aneurysms.

Main Results:

Evaluated against state-of-the-art methods (SCPM-Net, nnDetection, nnUNet) on two datasets (402 subjects).
Achieved comparable or superior performance using adapted object detection metrics.
Reported an average precision of 78.96%, sensitivity of 86.78%, and 0.53 false positives per case.

Conclusions:

The proposed object detection method simplifies aneurysm detection complexity.
Demonstrates the superiority of object detection over segmentation for this task.
Shows potential for detecting other small objects in medical imaging.