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

Institutions

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
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Using Photon-Counting CT Images for Lung Nodule Classification

Hoen‐oh Shin, Steffen Oeltze‐Jafra, Andrea Schenk, Leonie Basso et al.
Radiomics and Machine Learning in Medical Imaging
article

Using Photon-Counting CT Images for Lung Nodule Classification

Hoen‐oh Shin, Steffen Oeltze‐Jafra, Andrea Schenk, Leonie Basso, Zahra Ahmadi, Eike Petersen
article en

Abstract

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.

Medizinische Hochschule Hannover (DE), Fraunhofer Institute for Digital Medicine (DE)
Openalex Percentile: Top 95%
Radiomics and Machine Learning in Medical Imaging
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.