A web-based radiomics module for image feature extraction for tumor characterization

Theresa Huebner; Aaron Berger; Daniel Wild; Jianning Li; Antonio Pepe; Christina Gsaxner; Yuan Jin; Gijs Luijten; Jens Kleesiek; Jan Egger

doi:10.1117/12.2663100

10 April 2023 A web-based radiomics module for image feature extraction for tumor characterization

Theresa Huebner, Aaron Berger, Daniel Wild, Jianning Li, Antonio Pepe, Christina Gsaxner, Yuan Jin, Gijs Luijten, Jens Kleesiek, Jan Egger

Author Affiliations +

Proceedings Volume 12469, Medical Imaging 2023: Imaging Informatics for Healthcare, Research, and Applications; 124690W (2023) https://doi.org/10.1117/12.2663100
Event: SPIE Medical Imaging, 2023, San Diego, California, United States

Conference Poster

Abstract

The number of digital medical images is growing constantly over the years. This opens new possibilities of extracting information from them using computer-assisted methods, such as artificial intelligence. In this context, the application of radiomics has received increasing attention since 2012. In radiomics, medical image data is exploited by extracting numerous features from them that are not directly visible to the human eye. These features provide valuable information for diagnosis, prognosis and therapy, especially in cancer research. In this paper, we introduce a web-based radiomics module for end users under StudierFenster (www.studierfenster.at), which can extract image features for tumor characterization. StudierFenster is an online, open science medical image processing framework, where multiple clinically relevant modules and applications have been integrated since its initiation in 2018/2019, such as a medical VR viewer and automatic cranial implant design. The newly integrated Radiomics module allows the upload of medical images and segmentations of a region of interest to StudierFenster, where predefined radiomic features are calculated from them using the ‘pyRadiomic’ Python package. The radiomics module is able to calculate not only the basic first-order statistics of the images, but also more advanced features that capture the 2D/3D shape and gray level characteristics. The design of the radiomics module follows the architecture of StudierFenster, where computation-intensive procedures, such as preprocessing of the data and calculating the features for each image-segmentation pair, are executed on a server. The results are stored in a CSV file, which can afterwards be downloaded in a web-based user interface.

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Theresa Huebner, Aaron Berger, Daniel Wild, Jianning Li, Antonio Pepe, Christina Gsaxner, Yuan Jin, Gijs Luijten, Jens Kleesiek, and Jan Egger "A web-based radiomics module for image feature extraction for tumor characterization", Proc. SPIE 12469, Medical Imaging 2023: Imaging Informatics for Healthcare, Research, and Applications, 124690W (10 April 2023); https://doi.org/10.1117/12.2663100

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