Radiomics as a measure superior to common similarity metrics for tumor segmentation performance evaluation.

ABSTRACT:

Purpose

To propose radiomics features as a superior measure for evaluating the segmentation ability of physicians and auto-segmentation tools and to compare its performance with the most commonly used metrics: Dice similarity coefficient (DSC), surface Dice similarity coefficient (sDSC), and Hausdorff distance (HD).

Materials/methods

The data of 10 lung cancer patients' CT images with nine tumor segmentations per tumor were downloaded from the RIDER (Reference Database to Evaluate Response) database. Radiomics features of 90 segmented tumors were extracted using the PyRadiomics program. The intraclass correlation coefficient (ICC) of radiomics features were used to evaluate the segmentation similarity and compare their performance with DSC, sDSC, and HD. We calculated one IC

SUBMITTER: Akramova R 

PROVIDER: S-EPMC11302798 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature