ArticleBMC medical imaging2022
SAFARI: shape analysis for AI-segmented images.
Article in BMC medical imaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Morphometry-based detection of deep learning faults in glomerular segmentation.bioRxiv : the preprint server for biology · 2025Article
- Bayesian Landmark-based Shape Analysis of Tumor Pathology Images.Journal of the American Statistical Association · 2024Article
- Reconstructing Spatial Transcriptomics at the Single-cell Resolution with BayesDeep.bioRxiv : the preprint server for biology · 2023Article
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Authors and funding
8 authors.
Funding
Abstract
backgroundRecent 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.
resultsWe 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.
conclusionsSAFARI 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/ .
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