SAFARI：用于 AI 分割图像的形状分析。
SAFARI: shape analysis for AI-segmented images.
机构信息
Department of Mathematical Sciences, The University of Texas at Dallas, Richardson, TX, USA.
Quantitative Biomedical Research Center, Department of Population and Data Sciences, The University of Texas Southwestern Medical Center, Dallas, TX, USA.
出版信息
BMC Med Imaging. 2022 Jul 22;22(1):129. doi: 10.1186/s12880-022-00849-8.
BACKGROUND
Recent developments to segment and characterize the regions of interest (ROI) within medical images have led to promising shape analysis studies. However, the procedures to analyze the ROI are arbitrary and vary by study. A tool to translate the ROI to analyzable shape representations and features is greatly needed.
RESULTS
We developed SAFARI (shape analysis for AI-segmented images), an open-source R package with a user-friendly online tool kit for ROI labelling and shape feature extraction of segmented maps, provided by AI-algorithms or manual segmentation. We demonstrated that half of the shape features extracted by SAFARI were significantly associated with survival outcomes in a case study on 143 consecutive patients with stage I-IV lung cancer and another case study on 61 glioblastoma patients.
CONCLUSIONS
SAFARI is an efficient and easy-to-use toolkit for segmenting and analyzing ROI in medical images. It can be downloaded from the comprehensive R archive network (CRAN) and accessed at https://lce.biohpc.swmed.edu/safari/ .
背景
最近在医学图像的感兴趣区域（ROI）分割和特征描述方面的进展，使得对形状的分析研究有了很大的发展。然而，分析 ROI 的过程是任意的，并且因研究而异。因此，非常需要一种工具来将 ROI 转换为可分析的形状表示和特征。
结果
我们开发了 SAFARI（用于 AI 分割图像的形状分析），这是一个开源的 R 包，带有一个用户友好的在线工具包，用于对 AI 算法或手动分割提供的分割图谱进行 ROI 标记和形状特征提取。我们的研究表明，在一项对 143 例 I-IV 期肺癌连续患者和另一项对 61 例胶质母细胞瘤患者的病例研究中，SAFARI 提取的一半形状特征与生存结果显著相关。
结论
SAFARI 是一种用于分割和分析医学图像 ROI 的高效、易用的工具包。它可以从综合 R 档案网络（CRAN）下载，并可在 https://lce.biohpc.swmed.edu/safari/ 访问。
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