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Artificial Intelligence Approaches to Assessing Primary Cilia

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Published on: May 1, 2021

SAFARI: shape analysis for AI-segmented images.

Esteban Fernández¹, Shengjie Yang², Sy Han Chiou¹

¹Department of Mathematical Sciences, The University of Texas at Dallas, Richardson, TX, USA.

BMC Medical Imaging

|July 22, 2022

Summary

This summary is machine-generated.

A new R package, SAFARI (shape analysis for AI-segmented images), offers a user-friendly toolkit for medical image analysis. It extracts shape features from regions of interest, showing significant associations with survival outcomes in lung cancer and glioblastoma patients.

Keywords:

Machine learning Medical imaging Shape descriptors Shape representations

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Area of Science:

Medical imaging analysis
Computational pathology
Radiomics

Background:

Medical image analysis often involves segmenting regions of interest (ROI).
Current ROI analysis methods are inconsistent and vary significantly between studies.
A standardized tool is needed to convert ROIs into analyzable shape representations and features.

Purpose of the Study:

To develop an open-source R package and online toolkit for shape analysis of segmented medical images.
To provide a user-friendly platform for ROI labeling and shape feature extraction.

Main Methods:

Developed SAFARI (shape analysis for AI-segmented images), an R package and online toolkit.
Extracted shape features from segmented maps generated by AI or manual segmentation.
Applied SAFARI to case studies involving lung cancer and glioblastoma patients.

Main Results:

SAFARI facilitates efficient and user-friendly segmentation and analysis of ROIs in medical images.
Half of the shape features extracted by SAFARI demonstrated significant associations with survival outcomes.
Demonstrated utility in case studies of 143 lung cancer patients and 61 glioblastoma patients.

Conclusions:

SAFARI is an efficient, easy-to-use toolkit for medical image ROI analysis.
The package is available on CRAN and via an online portal.
SAFARI aids in extracting clinically relevant shape features for survival outcome prediction.