A Novel Implicit Neural Representation for Volume Data: Architecture and Performance Comparison

核心概念

Efficient compression of volumetric medical images using a novel implicit neural representation architecture.

要約

The article introduces a novel implicit neural representation (INR) architecture for compressing volumetric medical images. It addresses the challenges of high-resolution image storage and manipulation in the medical imaging field. The proposed architecture combines Lanczos downsampling, SIREN deep network, and SRDenseNet upsampling to reduce training time, increase compression rate, and save GPU memory. Experimental results show higher quality reconstruction and faster training speed compared to existing techniques. The study uses CT scan slices from the Visible Human project dataset for evaluation.

統計

Best PSNR with 2 layers: 34.670 dB
Training time with 3 layers: 74.80 seconds/50,000 iterations
Compression rate with 4 layers: 1.28
GPU memory consumption with 2 layers: 1038 KB

引用

"The experiments show that our proposed architecture is a novel implicit neural representation of medical volume data."
"Our architecture significantly outperforms other INR-based techniques without our architecture."
"The quality of our reconstructed high-resolution images with a small version of SIREN is considerably higher than direct SIREN with the same size."

抽出されたキーインサイト

A Novel Implicit Neural Representation for Volume Data

by Armin Sheiba... 場所 arxiv.org 03-14-2024

https://arxiv.org/pdf/2403.08566.pdf

深掘り質問

How can the proposed architecture be adapted for different types of medical imaging beyond CT scans

The proposed architecture can be adapted for different types of medical imaging beyond CT scans by adjusting the network structure and training data. For example, for MRI images, the input data format may need to be modified to accommodate the specific characteristics of MRI scans. Additionally, the training dataset would need to include a diverse range of MRI images to ensure that the model learns effectively from various scenarios. The downsampling and upsampling techniques used in the architecture can also be customized based on the resolution and quality requirements of different modalities.

What are the potential limitations or drawbacks of using implicit neural representations for image compression

While implicit neural representations offer promising results for image compression, there are potential limitations and drawbacks to consider. One limitation is related to interpretability - since INRs operate as black-box models, understanding how they compress and reconstruct images may be challenging. Another drawback is computational complexity; training deep neural networks like SIREN can require significant computational resources and time. Additionally, achieving high compression rates without compromising image quality remains a challenge with INR-based techniques.

How might advancements in deep learning impact the future development of medical image compression techniques

Advancements in deep learning are likely to have a profound impact on the future development of medical image compression techniques. Improved architectures such as transformers or attention mechanisms could enhance feature extraction capabilities and optimize reconstruction processes for better compression performance. Furthermore, incorporating reinforcement learning or generative adversarial networks (GANs) could lead to more efficient optimization strategies and higher-quality reconstructions in medical imaging applications. Overall, advancements in deep learning will continue to drive innovation in medical image compression towards more effective and efficient solutions.