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. 2026 Jan 15;17:1573. doi: 10.1038/s41467-026-68292-9

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© The Author(s) 2026

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

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Fig. 4 — a, b The encoding (a) and decoding (b) speed of DeepSTD when implemented with different numbers of GPUs, calculated on the CTSpine1K and Amos datasets with 256 × and 128 × compression, respectively. Here we plot the statistics on the datasets and the figure on a specific example with dashed lines. c The time breakdown of DeepSTD's training stage, among three subtasks---selecting reference shape, aligning shape, and training encoder/decoder. The time for CT (left) and MRI (right) data are respectively calculated on the CTSpine1K training set with 256 × compression and the Amos training set with 128 × compression, using 4 RTX 3090 GPUs. d The error bar (mean ± standard deviation) of DeepSTD's performance in terms of SSIM after different training durations, averaged over three runs with different random seeds and on the same dataset and compression ratio with c. The vertical dashed line indicates the training time adopted in this paper. For better readability, two horizontal axes are provided: one for the number of iterations and the other for training time. e DeepSTD's performance and training time in cases with limited training data, with “Sufficient” and “Limited” denoting the full Amos training dataset and a small training set consisting of 50 randomly selected samples, respectively. Additionally, we tested the contribution of two proposed countermeasures---data augmentation and knowledge distillation, as well as their combination, denoted as “Data Aug.'', “Know. Dis.” and “Data Aug. + Know. Dis.''. All experiments were conducted with a compression ratio of 128 × . f DeepSTD's stable and flexible control over the compression ratio. Left: The boxplot displaying the achieved compression ratios of different compressors for the given 256 × compression ratio, tested on 50 random data from the Amos test set. Right: DeepSTD's final achieved compression ratios at 20 different target compression ratios ranging from 32 × to 512 × , tested on 50 random data from the Amos test set. The left panel uses the same legend as Figs. 2 and 3, while the error bars in the right panel indicate the mean ± standard deviation.