Mr. Rory Gilliland Profile

Rory Gilliland

at 1512 Western Rd

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Proceedings Article | 4 April 2022 Poster + Paper

A semi-automatic threshold-based segmentation algorithm for lung cancer delineation

Jaryd Christie, Omar Daher, Hannah van Dongen, Rory Gilliland, Mohamed Abdelrazek, Sarah Mattonen

Proceedings Volume 12036, 120361T (2022) https://doi.org/10.1117/12.2611501

KEYWORDS: Image segmentation, Tumors, Computed tomography, Feature extraction, Algorithm development, Lung cancer, Lung, Medical imaging

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Radiomic studies utilize AI and quantitative features from medical images to create models that can predict patient outcomes. An integral step in these radiomic studies is the delineation of the regions of interest where the features are extracted. Manual segmentation is labor intensive and time-consuming for large studies. Semi-automatic segmentation tools have been used in recent radiomic studies to achieve more reproducible segmentations and robust radiomics features. However, for the segmentation of lung tumors on CT images, tools in the literature are difficult to find publicly and require extensive user interaction. Therefore, we aimed to build a semi-automatic segmentation tool which was intuitive, fast, and required minimal user interaction. We used one dataset to develop the segmentation algorithm on (n=49), and another to test its performance (n=144). All 144 tumors were segmented on the CT images using the semiautomatic tool by three separate users. A gold standard tumor delineation was determined by a trained radiologist. The segmentation robustness was assessed using the Dice, mean absolute boundary distance (MAD) and volume difference (VD). A total of 408 radiomic features were extracted and feature robustness was determined using an intra-class correlation coefficient (ICC) greater than 0.8. The developed tool achieved an average Dice of 0.90, MAD of 0.62 mm and a VD of 0.97 ml between the three users. A total of 181 (76%) of the extracted features displayed excellent reliability. This tool has the potential to augment the reliability of radiomic studies by making segmentations and feature sets more reproducible.

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