Streaming Lossless Volumetric Compression of Medical Images Using Gated Recurrent Convolutional Neural Network

• URL: http://arxiv.org/abs/2311.16200v1
• Date: Mon, 27 Nov 2023 07:19:09 GMT
• Title: Streaming Lossless Volumetric Compression of Medical Images Using Gated Recurrent Convolutional Neural Network
• Authors: Qianhao Chen, Jietao Chen
• Abstract summary: This paper introduces a hardware-friendly streaming lossless volumetric compression framework. We propose a gated recurrent convolutional neural network that combines diverse convolutional structures and fusion gate mechanisms. Our method exhibits robust generalization ability and competitive compression speed.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep learning-based lossless compression methods offer substantial advantages in compressing medical volumetric images. Nevertheless, many learning-based algorithms encounter a trade-off between practicality and compression performance. This paper introduces a hardware-friendly streaming lossless volumetric compression framework, utilizing merely one-thousandth of the model weights compared to other learning-based compression frameworks. We propose a gated recurrent convolutional neural network that combines diverse convolutional structures and fusion gate mechanisms to capture the inter-slice dependencies in volumetric images. Based on such contextual information, we can predict the pixel-by-pixel distribution for entropy coding. Guided by hardware/software co-design principles, we implement the proposed framework on Field Programmable Gate Array to achieve enhanced real-time performance. Extensive experimental results indicate that our method outperforms traditional lossless volumetric compressors and state-of-the-art learning-based lossless compression methods across various medical image benchmarks. Additionally, our method exhibits robust generalization ability and competitive compression speed

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