Volumetric End-to-End Optimized Compression for Brain Images

The amount of volumetric brain image increases rapidly, which requires a vast amount of resources for storage and transmission, so it's urgent to explore an efficient volumetric compression method. Recent years have witnessed the progress of deep learning-based approaches for two-dimensional (2D) natural image compression, but the field of learned volumetric image compression still remains unexplored. In this paper, we propose the first end-to-end learning framework for volumetric image compression by extending the advanced techniques of 2D image compression to volumetric images. Specifically, a convolutional autoencoder is used to compress 3D image cubes, and the non-local attention models are embedded in the convolutional autoencoder to jointly capture local and global correlations. Both hyperprior and autoregressive models are used to perform the conditional probability estimation in entropy coding. To reduce model complexity, we introduce a convolutional long short-term memory network for the autoregressive model based on channel-wise prediction. Experimental results on volumetric mouse brain images show that the proposed method outperforms JPEG2000-3D, HEVC and state-of-the-art 2D methods.