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Updated: Mar 27, 2026

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

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

Published on: April 13, 2013

A GPU accelerated moving mesh correspondence algorithm with applications to RV segmentation.

Kumaradevan Punithakumar, Michelle Noga, Pierre Boulanger

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

|January 7, 2016

Summary

This summary is machine-generated.

This study accelerates nonrigid image registration using Graphics Processing Units (GPUs) for faster organ segmentation. The parallel algorithm significantly improves performance for medical imaging tasks like cardiac right ventricle delineation.

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Area of Science:

Medical Imaging
Computer Vision
High-Performance Computing

Background:

Point correspondence is crucial for organ delineation in image sequences.
Nonrigid registration algorithms are computationally intensive.
Accelerating these algorithms is vital for clinical applications.

Purpose of the Study:

To develop a parallel nonrigid registration algorithm for accelerated point correspondence.
To leverage Graphics Processing Unit (GPU) computing for performance enhancement.
To improve the efficiency of organ segmentation from medical image sequences.

Main Methods:

Implemented a parallel Compute Unified Device Architecture (CUDA) version of the nonrigid registration algorithm.
Utilized an image concatenation approach for further parallelization.
Evaluated the algorithm on a dataset of 16 subjects with 19 magnetic resonance (MR) images per sequence.

Main Results:

Achieved a significant performance improvement over serial image registration.
The parallel algorithm processed a sequence of 19 MR images in an average of 4.36 seconds.
Demonstrated the effectiveness of GPU computing for accelerating nonrigid registration.

Conclusions:

The proposed parallel nonrigid registration algorithm effectively accelerates image processing.
GPU computing offers a substantial performance gain for medical image analysis.
This approach enhances the feasibility of using point correspondence for organ segmentation in clinical settings.