arXiv 2604.045112026-04-06cs.CV

MedROI: 一种通用的以感兴趣区域为中心的医学图像压缩方法

MedROI: Codec-Agnostic Region of Interest-Centric Compression for Medical Images

Division of Computer Engineering, Hankuk University of Foreign Studies（韩国外国语大学计算机工程系）；Department of Computer Science and Engineering, Hankuk University of Foreign Studies（韩国外国语大学计算机科学与工程系）

Jiwon Kim, Ikbeom Jang

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AI总结：

MedROI提出了一种无需特定编码器的感兴趣区域中心压缩框架，通过轻量级强度阈值提取紧致组织边界，并利用固定元数据记录实现空间恢复，有效提升压缩比和效率。

摘要：

医学影像档案在尺寸和分辨率上迅速增长，使高效压缩在存储和数据传输中变得越来越重要。现有编码器主要压缩完整图像/体积（包括非诊断背景）或应用差分ROI编码仍保留背景位。我们提出MedROI，一种通用、即插即用的ROI中心框架，在压缩前丢弃背景体素。MedROI通过轻量级强度阈值提取紧致组织边界，并存储固定54字节元数据记录以实现解压时的空间恢复。裁剪后的ROI然后使用任何现有2D或3D编码器进行压缩，无需架构修改或重新训练。我们在ADNI的200个T1加权脑部MRI体积上评估MedROI，使用6种编码器配置，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在大多数配置中实现了统计显著的压缩比和编码/解码时间提升（双侧t检验多重比较校正），在ROI内保持可比的重建质量；HEIF是压缩比提升的主要例外。例如，在JPEG2000 2D（lv3）中，MedROI将压缩比从20.35提升到27.37，同时将平均压缩时间从1.701秒减少到1.380秒。代码可在https://github.com/labhai/MedROI获取。

英文摘要：

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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