Using Photon-Counting CT Images for Lung Nodule Classification
An automatic classification of the malignancy of lung nodules in computed tomography (CT) scans can support early detection of lung cancer, which is crucial for the treatment success. The novel photon-counting CT (PCCT) technology enables high image quality with a low radiation dose and provides additional spectral information. This research focuses on whether PCCT scans offer a benefit in the automatic classification of lung nodules. Establishing a dataset of PCCT images poses several challenges, such as the extraction of annotations or the data imbalance.
Authors
- Hoen‐oh Shin (ORCID: https://orcid.org/0000-0002-3176-0075)
- Steffen Oeltze‐Jafra (ORCID: https://orcid.org/0000-0002-6962-9080)
- Andrea Schenk (ORCID: https://orcid.org/0000-0003-1806-8380)
- Leonie Basso (ORCID: https://orcid.org/0000-0002-8691-3219)
- Zahra Ahmadi (ORCID: https://orcid.org/0000-0003-1110-4756)
- Eike Petersen (ORCID: https://orcid.org/0000-0003-0097-3868)
Institutions
- Medizinische Hochschule Hannover (DE)
- Fraunhofer Institute for Digital Medicine (DE)
Publication Details
- Published
- 2025-02-26
- DOI
- https://doi.org/10.1145/3701551.3708810
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00