End-to-End Neural Network for Medical Volume Data Compression
Author Information
Author(s): Sheibanifard Armin, Yu Hongchuan, Ruan Zongcai, Zhang Jian J.
Primary Institution: NCCA, Bournemouth University, Poole, United Kingdom
Hypothesis
Can an end-to-end architecture improve compression rates and reduce GPU memory usage for medical volume data?
Conclusion
The proposed architecture achieves a high compression rate of up to 97.5% while maintaining superior reconstruction accuracy.
Supporting Evidence
- The architecture reduces GPU memory requirements and processing time.
- Experimental results show a Peak Signal-to-Noise Ratio (PSNR) of 40.05 dB and Structural Similarity Index (SSIM) of 0.96.
- The method is applicable to various medical imaging tasks.
Takeaway
This study created a smart system that makes big medical images smaller without losing important details, making it easier to store and use them.
Methodology
The study used a three-module architecture consisting of downsampling, implicit neural representation, and super-resolution to compress medical volume data.
Limitations
The architecture requires significant retraining for different datasets and the trade-off point method is time-consuming.
Participant Demographics
Patients diagnosed with glioblastoma or lower-grade glioma.
Digital Object Identifier (DOI)
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