Maria Wimmer

Ph.D.

VRVis GmbH

Senior Researcher

NLST (Learn more about this study)

NLST-1465

Sep 24, 2025

Enhancing lesion segmentation algorithm to include lung nodules

Accurate lesion segmentation is a foundational component in effective lung cancer screening, diagnosis, and longitudinal management. AGFA HealthCare’s Enterprise Imaging platform supports clinicians with advanced tools for visualizing and assessing lesions in medical imaging. This project proposes to further develop the AGFA HealthCare lesion segmentation algorithm, with a particular focus on improving its performance on complex and clinically significant lesion types to include lung nodules.

Lung nodules, especially in early or subtle forms, are challenging to delineate and track over time. However, they are critical indicators of potential malignancy and require precise segmentation to support volume assessment, doubling time calculation, and treatment planning. The extension and validation of the algorithm on lung nodule lesions will significantly improve its clinical applicability. Utilizing the National Lung Screening Trial (NLST) dataset, which offers a rich and diverse collection of CT scans with associated radiological and pathological data, the project aims to train and evaluate a machine learning model for robust and reproducible segmentation across lesion subtypes. The long-term goal is to provide clinicians with reliable, efficient tools that support early detection, diagnosis, and consistent longitudinal follow-up of lung abnormalities.