School of Optical and Electronic Information-Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, 430074, China.
Department of Bioengineering, Henry Samueli School of Engineering and Applied Science, University of California, Los Angeles, Los Angeles, 90095, USA.
Commun Biol. 2024 Sep 3;7(1):1081. doi: 10.1038/s42003-024-06788-0.
The surge in advanced imaging techniques has generated vast biomedical image data with diverse dimensions in space, time and spectrum, posing big challenges to conventional compression techniques in image storage, transmission, and sharing. Here, we propose an intelligent image compression approach with the first-proved semantic redundancy of biomedical data in the implicit neural function domain. This Semantic redundancy based Implicit Neural Compression guided with Saliency map (SINCS) can notably improve the compression efficiency for arbitrary-dimensional image data in terms of compression ratio and fidelity. Moreover, with weight transfer and residual entropy coding strategies, it shows improved compression speed while maintaining high quality. SINCS yields high quality compression with over 2000-fold compression ratio on 2D, 2D-T, 3D, 4D biomedical images of diverse targets ranging from single virus to entire human organs, and ensures reliable downstream tasks, such as object segmentation and quantitative analyses, to be conducted at high efficiency.
高级成像技术的兴起产生了具有不同空间、时间和光谱维度的大量生物医学图像数据，这对传统的图像存储、传输和共享压缩技术提出了巨大挑战。在这里，我们提出了一种基于智能图像压缩的方法，该方法在隐式神经函数域中首次证明了生物医学数据的语义冗余。这种基于语义冗余的隐式神经压缩引导的显著图（SINCS）可以显著提高任意维图像数据的压缩效率，提高压缩比和保真度。此外，通过权重转移和残差熵编码策略，它在保持高质量的同时提高了压缩速度。SINCS 可以在 2D、2D-T、3D、4D 生物医学图像上实现超过 2000 倍的压缩比，这些图像的目标从单个病毒到整个人体器官各不相同，并且可以确保高效地进行下游任务，如对象分割和定量分析。
