Fig. 3. DeepSTD achieved high efficiency MRI data compression, demonstrated on the Amos dataset⁵⁰.

a The reduction in file size and minimum transmission time with 500 KB/s bandwidth by applying our compression algorithm for 128 × compression to the preprocessed Amos dataset. b The encoding quality of DeepSTD on 229 samples from the Amos dataset at 128 × compression ratio, in terms of intensity fidelity---an average Structural Similarity Index Metrics (SSIM) of 0.993, and the organ segmentation precision---an average Dice Similarity Coefficient (DSC) of 87.8 and 95% Hausdorff Distance (HD95) of 9.1. Here the plots share the same legend with Fig. 2b, and the organ segmentation was performed using the UNETR++⁴³ trained on the labeled Amos dataset for the Liver, Stomach, and Spleen. c, d Visual comparison of DeepSTD's intensity fidelity on 128  × compression of two axial cross-sectional images at depth 225 (c) and depth 270 (d) of data sample #7298, with the zoomed-in comparisons of three regions of interest (ROIs) and corresponding line profiles alongside for clear demonstration. e Visual comparison of the downstream organ segmentation before and after applying DeepSTD for 128  × compression. We present the axial cross-sectional images at depth 29 of data sample #7200, with color-coded segmentation results overlaid on top, and place the differences between segmentation on the original and compressed data shown alongside. Additionally, we show zoomed-in comparisons of three ROIs for a clearer view. f The encoding/decoding time breakdown of DeepSTD between shape and texture information, recorded at 128 × compression and using a single RTX 3090 GPU. g–i Comparison between DeepSTD against baselines in terms of intensity fidelity---SSIM (g), segmentation precision---DSC and HD95 (h), and encoding speed (i) at four different compression ratios.