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优化存储和计算效率:一个有效的算法,用于整张幻灯片的图像大小缩小.

Shahriar Faghani1, D Chamil Codipilly2, Mana Moassefi1

  • 1Artificial Intelligence Laboratory, Department of Radiology, Mayo Clinic, Rochester, MN.

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概括
此摘要是机器生成的。

一个新的算法通过删除背景,帮助存储和分析,有效地减少整个幻灯片成像 (WSI) 文件大小. 这种WSI压缩保持了图像分辨率和深度学习模型性能,以检测生殖不良.

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科学领域:

  • 数字病理学数字病理学
  • 计算成像技术的成像
  • 生物信息学是一种生物信息学.

背景情况:

  • 整个幻灯片成像 (WSI) 生成大型文件,对存储,传输和分析提出了挑战.
  • 对WSI的高效处理对于推进数字病理学工作流程和研究至关重要.

研究的目的:

  • 开发和验证一个用于减少WSI文件大小的图像处理算法.
  • 评估WSI尺寸缩小对深度学习模型用于发育不良检测的性能的影响.

主要方法:

  • 该算法将彩色WSIs转换为灰度,对图像进行二元化,并处理前景面具.
  • 连接的组件被确定,并计算出边界框来提取含有组织的区域.
  • 一个矩形包装包被用来找到包装所有组织部件的最小边界矩形.

主要成果:

  • 该算法实现了7.11×的平均WSI尺寸缩小.
  • 在缩小尺寸的WSIs上对巴雷特食道失生症分级的深度学习模型性能与原始WSIs相比.

结论:

  • 开发的算法为WSI大小缩小提供了有效的解决方案.
  • 这种方法有助于在研究和临床环境中有效地存储,转移和分析WSIs.