将SEEDS扩展为用于医学图像分析的超体素算法
Extending SEEDS to a Supervoxel Algorithm for Medical Image Analysis
AI总结：
本文将SEEDS超像素算法扩展到三维，提出3D SEEDS超体素算法，在10个器官的13个分割任务上相比SLIC速度提升10倍、Dice分数提高6.5%，并开源代码。
摘要：
在这项工作中，我们将SEEDS超像素算法从二维图像扩展到三维体数据，提出了3D SEEDS——一种更快、更好且开源的超体素算法，用于医学图像分析。我们将3D SEEDS与广泛使用的超体素算法SLIC在10个器官的13个分割任务上进行了比较。3D SEEDS将超体素生成速度提高了10倍，将可达到的Dice分数提升了+6.5%，并将欠分割误差降低了-0.16%。代码可在https://github.com/Zch0414/3d_seeds获取。
英文摘要：
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
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
