Prediction performance of radiomic features when obtained using an object detection framework

Hamza Chegraoui (1) , Amine Rebei (1) , Cathy Philippe (1) , Vincent Frouin (1)

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Radiomic features analysis is a non invasive method for disease profiling. In the case of brain tumour studies, the quality of these features depends on the quality of tumour segmentation. However, these segmentations are not available for most cohorts. One way to address this issue is using object detection frameworks to automatically extract the area where the tumour is located in. The purpose of this study is to compare the quality of bounding-boxes based radiomics with manual segmentation, with regards to their performance in patient stratification and survival prediction.

Domains: Mathematics [math]; Computer Science [cs]; Life Sciences [q-bio]

Hamza Chegraoui, Amine Rebei, Cathy Philippe, Vincent Frouin. Prediction performance of radiomic features when obtained using an object detection framework. IEEE ISBI 2021 - International Symposium on Biomedical Imaging, Apr 2021, Nice, France. ⟨cea-03162316⟩