Radiomics study of lung tumor volume segmentation technique in contrast-enhanced Computed Tomography (CT) thorax images: A comparative study

Medical image segmentation is crucial in extracting information regarding tumour characteristics including lung cancer. To obtain the information of macroscopic (tumour volume) and microscopic features (radiomics study), image segmentation process is required. Various kind of advance segmentation algorithms are available nowadays yet there is no so-called ‘the best segmentation technique’ that can be used in medical imaging modalities. This study compared manual slice by slice segmentation and semi-automated segmentation of lung tumour volume measurement with radiomics features of shape analysis and first-order statistical measures of texture analysis. Manual slice by slice delineation and region-growing semi-automated segmentation using 3D slicer software was performed on 45 sets of contrast-enhanced Computed Tomography (CT) Thorax images downloaded from The Cancer Imaging Archive (TCIA). The results showed that the manually and semi-automated segmentation has high similarity with volume Hausdorff distance (AHD) measured as 1.02 ± 0.71mm, high Dice similarity coefficient (DSC) value is 0.83 ± 0.05 and p value is 0.997; p > 0.05. Overall, 84.62% of the features under shape analysis and 33.33% of first-order statistical measures of texture analysis are no significant difference between these two segmentation methods. In conclusion, semiautomated segmentation can be perform as good as manual segmentation in lung tumour volume measurement, especially in terms of the ability to extract the shape order features of the lung tumour radiomics analysis.