A Novel Implicit Neural Representation for Volume Data
Authors: Sheibanifard, A. and Yu, H.
Journal: Applied Sciences (Switzerland)
Volume: 13
Issue: 5
eISSN: 2076-3417
DOI: 10.3390/app13053242
Abstract:The storage of medical images is one of the challenges in the medical imaging field. There are variable works that use implicit neural representation (INR) to compress volumetric medical images. However, there is room to improve the compression rate for volumetric medical images. Most of the INR techniques need a huge amount of GPU memory and a long training time for high-quality medical volume rendering. In this paper, we present a novel implicit neural representation to compress volume data using our proposed architecture, that is, the Lanczos downsampling scheme, SIREN deep network, and SRDenseNet high-resolution scheme. Our architecture can effectively reduce training time, and gain a high compression rate while retaining the final rendering quality. Moreover, it can save GPU memory in comparison with the existing works. The experiments show that the quality of reconstructed images and training speed using our architecture is higher than current works which use the SIREN only. Besides, the GPU memory cost is evidently decreased.
https://eprints.bournemouth.ac.uk/38355/
Source: Scopus
A Novel Implicit Neural Representation for Volume Data
Authors: Sheibanifard, A. and Yu, H.
Journal: Journal of Applied Sciences
Publisher: Asian Network for Scientific Information
ISSN: 1607-8926
DOI: 10.3390/app13053242
https://eprints.bournemouth.ac.uk/38355/
Source: Manual
A Novel Implicit Neural Representation for Volume Data
Authors: Sheibanifard, A. and Yu, H.
Journal: Journal of Applied Sciences
Volume: 13
Publisher: Asian Network for Scientific Information
ISSN: 1607-8926
Abstract:The storage of medical images is one of the challenges in the medical imaging field. There are variable works that use implicit neural representation (INR) to compress volumetric medical images. However, there is room to improve the compression rate for volumetric medical images. Most of the INR techniques need a huge amount of GPU memory and a long training time for highquality medical volume rendering. In this paper, we present a novel implicit neural representation to compress volume data using our proposed architecture, that is, the Lanczos downsampling scheme, SIREN deep network, and SRDenseNet high-resolution scheme. Our architecture can effectively reduce training time, and gain a high compression rate while retaining the final rendering quality. Moreover, it can save GPU memory in comparison with the existing works. The experiments show that the quality of reconstructed images and training speed using our architecture is higher than current works which use the SIREN only. Besides, the GPU memory cost is evidently decreased.
https://eprints.bournemouth.ac.uk/38355/
Source: BURO EPrints