SAFARI: shape analysis for AI-segmented images

Esteban Fernández, Shengjie Yang, Sy Han Chiou, Chul Moon, Cong Zhang, Bo Yao, Guanghua Xiao, Qiwei Li

Research output: Contribution to journalArticlepeer-review

Abstract

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/.

Original languageEnglish (US)
Article number129
JournalBMC Medical Imaging
Volume22
Issue number1
DOIs
StatePublished - Dec 2022

Keywords

  • Machine learning
  • Medical imaging
  • Shape descriptors
  • Shape representations

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging

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