Streaming Lossless Volumetric Compression of Medical Images Using Gated Recurrent Convolutional Neural Network
This work addresses the need for efficient and practical lossless compression of medical images, which is crucial for storage and transmission in healthcare, though it appears incremental as it builds on existing deep learning approaches with hardware optimizations.
The paper tackles the problem of compressing medical volumetric images by introducing a hardware-friendly streaming lossless compression framework that uses a gated recurrent convolutional neural network to capture inter-slice dependencies, achieving superior compression performance compared to traditional and state-of-the-art learning-based methods across various benchmarks.
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