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

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Chegraoui, Hamza | Rebei, Amine | Philippe, Cathy | Frouin, Vincent

Edité par CCSD -

ISBI 2021 moves to a fully virtual conference. International audience. 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.

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