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Updated: May 10, 2026

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

An end-to-end implicit neural representation architecture for medical volume data.

Armin Sheibanifard¹, Hongchuan Yu¹, Zongcai Ruan²

¹NCCA, Bournemouth University, Poole, United Kingdom.

|January 3, 2025

Summary

Deep learning enhances medical data compression, achieving 97.5% reduction while preserving image quality. This AI-driven approach optimizes storage and processing for large medical datasets.

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Area of Science:

Medical Imaging
Artificial Intelligence
Data Science

Background:

Medical volume data is rapidly increasing, posing challenges for organization, storage, transmission, and rendering.
Current methods struggle to efficiently manage petabyte-scale medical datasets.

Purpose of the Study:

To develop an end-to-end deep learning architecture for efficient medical data compression.
To balance high compression rates with superior reconstruction quality for medical imaging data.

Main Methods:

An architecture combining downsampling, implicit neural representation (INR), and super-resolution (SR) modules was proposed.
A trade-off point method was used to optimize module performance for compression and reconstruction.
The method was experimentally validated on multi-parametric MRI data.

Main Results:

Achieved a high compression rate of up to 97.5%.
Maintained superior reconstruction accuracy with a Peak Signal-to-Noise Ratio (PSNR) of 40.05 dB and Structural Similarity Index (SSIM) of 0.96.
Significantly reduced GPU memory requirements and processing time.

Conclusions:

The proposed deep learning architecture offers a practical solution for handling large medical datasets.
This approach effectively addresses the challenges of medical data volume growth.
The method enables efficient storage, transmission, and manipulation of medical imaging data.