Abstract
Medical imaging archives are growing rapidly in both size and resolution, making efficient compression increasingly important for storage and data transfer. Most existing codecs compress full images/volumes(including non-diagnostic background) or apply differential ROI coding that still preserves background bits. We propose MedROI, a codec-agnostic, plug-and-play ROI-centric framework that discards background voxels prior to compression. MedROI extracts a tight tissue bounding box via lightweight intensity-based thresholding and stores a fixed 54byte meta data record to enable spatial restoration during decompression. The cropped ROI is then compressed using any existing 2D or 3D codec without architectural modifications or retraining. We evaluate MedROI on 200 T1-weighted brain MRI volumes from ADNI using 6 codec configurations spanning conventional codecs (JPEG2000 2D/3D, HEIF) and neural compressors (LIC_TCM, TCM+AuxT, BCM-Net, SirenMRI). MedROI yields statistically significant improvements in compression ratio and encoding/decoding time for most configurations (two-sided t-test with multiple-comparison correction), while maintaining comparable reconstruction quality when measured within the ROI; HEIF is the primary exception in compression-ratio gains. For example, on JPEG20002D (lv3), MedROI improves CR from 20.35 to 27.37 while reducing average compression time from 1.701s to 1.380s. Code is available at this https URL.
Abstract (translated)
医学影像档案的体量与分辨率正快速增长，使得高效压缩对存储与数据传输愈发重要。现有编解码器多对全图像/体数据（包含非诊断性背景区域）进行压缩，或采用仍保留背景比特的差分感兴趣区域（ROI）编码。我们提出MedROI——一个编解码器无关、即插即用的ROI中心化框架，可在压缩前剔除背景体素。MedROI通过轻量级基于强度的阈值法提取紧致的组织边界框，并存储固定54字节的元数据记录以支持解压时的空间还原。裁剪后的ROI随后可使用任意现有2D或3D编解码器进行压缩，无需修改架构或重新训练。 我们在ADNI数据集200例T1加权脑部MRI体数据上，使用涵盖传统编解码器（JPEG2000 2D/3D、HEIF）与神经压缩器（LIC_TCM、TCM+AuxT、BCM-Net、SirenMRI）的6种编解码配置评估MedROI。结果显示：在ROI内保持可比重建质量的前提下，MedROI在多数配置中显著提升压缩比并缩短编解码时间（经多重比较校正的双侧t检验）；HEIF在压缩比增益方面是主要例外。例如在JPEG2000 2D（lv3）配置中，MedROI将压缩比从20.35提升至27.37，同时将平均压缩时间从1.701秒降至1.380秒。 代码公开于此https链接。
URL
https://arxiv.org/abs/2604.04511
