ZFP压缩可大幅减小脑血管3D影像体积，同时保持分割精度
Efficiency vs. Efficacy: Assessing the Compression Ratio-Dice Score Relationship through a Simple Benchmarking Framework for Cerebrovascular 3D Segmentation
- 用ZFP压缩3D脑血管医学影像，分误差容忍和固定率两种模式
- 最高压缩比达22.89:1，分割Dice系数仍保持在0.87656
- 适合需要高效共享大尺度医学影像的研究者
医学影像数据量持续增长，尤其是3D格式，给协作研究与模型迁移带来挑战。本研究评估了ZFP压缩技术是否能在不损害自动脑血管分割性能的前提下缓解这些问题，该任务是颅内动脉瘤检测的关键步骤。我们对大规模、最新文献中的3D医学数据集（含真实血管分割标注）应用ZFP的误差容忍与固定率模式。将压缩后的图像分割质量与未压缩基线（Dice≈0.8774）进行严格对比。结果表明，ZFP可在误差容忍模式下实现高达22.89:1的压缩比，同时保持较高保真度，平均Dice系数为0.87656。这证明ZFP是提升大规模医学数据研究效率与可及性的有效工具，有助于促进学术界更广泛的合作。
原文摘要 · Abstract (English)
The increasing size and complexity of medical imaging datasets, particularly in 3D formats, present significant barriers to collaborative research and transferability. This study investigates whether the ZFP compression technique can mitigate these challenges without compromising the performance of automated cerebrovascular segmentation, a critical first step in intracranial aneurysm detection. We apply ZFP in both its error tolerance and fixed-rate modes to a large scale, and one of the most recent, datasets in the literature, 3D medical dataset containing ground-truth vascular segmentations. The segmentation quality on the compressed volumes is rigorously compared to the uncompressed baseline (Dice approximately equals 0.8774). Our findings reveal that ZFP can achieve substantial data reduction--up to a 22.89:1 ratio in error tolerance mode--while maintaining a high degree of fidelity, with the mean Dice coefficient remaining high at 0.87656. These results demonstrate that ZFP is a viable and powerful tool for enabling more efficient and accessible research on large-scale medical datasets, fostering broader collaboration across the community.
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