医学影像压缩新方法，只保留关键区域，提速省空间。
MedROI: Codec-Agnostic Region of Interest-Centric Compression for Medical Images
- 先切出病灶区域，再用任意编码器压缩，无需改模型。
- 相比原图压缩，压缩比提升34.5%，速度加快18.8%。
- 适合医疗影像存储与传输，尤其对高分辨率数据有效。
医学影像数据量和分辨率持续增长，高效压缩对存储与传输至关重要。现有编码器通常压缩整幅图像（含非诊断背景）或采用差异性感兴趣区域编码，仍保留背景数据。我们提出 MedROI，一种编码器无关、即插即用的感兴趣区域中心压缩框架，在压缩前丢弃背景体素。MedROI 通过轻量级强度阈值法提取紧致组织包围盒，并存储固定 54 字节元数据以支持解压时的空间还原。裁剪后的感兴趣区域使用任意现有 2D/3D 编码器压缩，无需架构修改或重新训练。我们在 200 例来自 ADNI 的 T1 加权脑部 MRI 数据上评估了 6 种编码配置，涵盖传统编码器（JPEG2000 2D/3D、HEIF）和神经压缩器（LIC_TCM、TCM+AuxT、BCM-Net、SirenMRI）。MedROI 在多数配置下显著提升压缩比与编码/解码速度（双侧 t 检验，多重比较校正），在感兴趣区域内重建质量相当；唯独 HEIF 在压缩比提升上表现不佳。例如，对于 JPEG2000 2D (lv3)，MedROI 将压缩比从 20.35 提升至 27.37，平均压缩时间由 1.701 秒降至 1.380 秒。代码已开源：https://github.com/labhai/MedROI。
原文摘要 · Abstract (English)
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 https://github.com/labhai/MedROI.
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