Learned Lossy Image Compression for Volumetric Medical Data

Abstract

This work addresses the problem of lossy compression of volumetric images consisting of individual slices such as those produced by CT scans and MRI machines in medical imaging. We propose an extension of a single-image lossy compression method with an autoregressive context module to a sequential encoding of the volumetric slices. In particular, we remove the intra-slice autoregressive relation and instead condition the entropy model of the latent on the previous slice in the sequence. This modification alleviates the typical disadvantages of autoregressive contexts and leads to a significant increase in performance compared to encoding each slice independently. We test the proposed method on a dataset of diverse CT scan images in a setting with an emphasis on high-fidelity reconstruction required in medical imaging and show that it compares favorably against several established state-of-the-art codecs in both performance and runtime.