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 [BibTeX] [Marc21]
Comparing Stability and Discriminatory Power of Hand-crafted Versus Deep Radiomics: A 3D-Printed Anthropomorphic Phantom Study
Type of publication: Conference paper
Citation: Jimenez-del-Toro_EUVIP2024_2024
Publication status: Accepted
Booktitle: Proceedings of the 12th European Workshop on Visual Information Processing
Year: 2024
Month: September
Abstract: Radiomics have the ability to comprehensively quantify human tissue characteristics in medical imaging studies. However, standard radiomic features are highly unstable due to their sensitivity to scanner and reconstruction settings. We present an evaluation framework for the extraction of 3D deep radiomics features using a pre-trained neural network on real computed tomography (CT) scans for tissue characterization. We compare both the stability and discriminative power of the proposed 3D deep learning radiomic features versus standard hand-crafted radiomic features using 8 image acquisition protocols with a 3D-printed anthropomorphic phantom containing 4 classes of liver lesions and normal tissue. Even when the deep learning model was trained on an external dataset and for a different tissue characterization task, the resulting generic deep radiomics are at least twice more stable on 8 CT parameter variations than any category of hand-crafted features. Moreover, the 3D deep radiomics were also discriminative for the tissue characterization between 4 classes of liver tissue and lesions, with an average discriminative power of 93.5%.
Keywords:
Projects Idiap
Authors Jimenez-del-Toro, Oscar
Aberle, Christoph
Schaer, Roger
Bach, Michael
Flouris, Kyriakos
Konukoglu, Ender
Stieltjes, Bram
Obmann, Markus M.
Anjos, André
Müller, Henning
Depeursinge, Adrien
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Total mark: 0
Attachments
  • Jimenez-del-Toro_EUVIP2024_2024.pdf
Notes