将2D超像素算法扩展至3D，提升医学图像分割效率与精度。
Extending SEEDS to a Supervoxel Algorithm for Medical Image Analysis
- 将SEEDS算法推广至3D体积数据，实现快速生成超体素。
- 速度提升10倍，Dice分数提高6.5%，过分割误差降低0.16%。
- 适用于多器官医学图像分析，开源代码可复现。
本文将2D超像素算法SEEDS扩展至3D体积数据，提出3D SEEDS，一种更快、更优且开源的超体素算法，专用于医学图像分析。在10个器官的13项分割任务中，3D SEEDS相比广泛使用的SLIC算法，加速超体素生成达10倍，Dice分数提升6.5%，过分割误差降低0.16%。代码已公开于https://github.com/Zch0414/3d_seeds。
原文摘要 · Abstract (English)
In this work, we extend the SEEDS superpixel algorithm from 2D images to 3D volumes, resulting in 3D SEEDS, a faster, better, and open-source supervoxel algorithm for medical image analysis. We compare 3D SEEDS with the widely used supervoxel algorithm SLIC on 13 segmentation tasks across 10 organs. 3D SEEDS accelerates supervoxel generation by a factor of 10, improves the achievable Dice score by +6.5%, and reduces the under-segmentation error by -0.16%. The code is available at https://github.com/Zch0414/3d_seeds
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