PN-Quant: An Automated Pulmonary Nodule Quantification Method for Full-Size CT Scans

Pulmonary nodule quantification is essential in forecasting and diagnosing potential malignant nodules, providing critical information for early intervention and treatment planning. However, most existing assistant diagnosis techniques primarily focus on the localization of lung nodules without comprehensive quantitative analysis, limiting their utility in clinical practice. To address this significant limitation, we present a novel and robust pulmonary nodule quantification framework named PN-Quant. It integrates a detection module, a segmentation module, and a quantification module to enable automated identification and precise measurement of lung nodules in full-size Computer Tomography (CT) scans, which facilitates the extraction of geometric characteristics, including volume, surface area, mass, sphericity, compactness, and elongation, offering valuable quantitative data for accurate nodule assessment. This study evaluates multiple PN-Quant pipelines with diverse configurations using datasets LIDC-IDRI, LNDb-19, and MSD-lung. Notably, the pipeline combining SANet and 3D UX-Net demonstrated superior performance, yielding low relative errors of 16.8%, 39.5%, and 24.4% on the respective datasets. These results underscore the effectiveness of the automated pipeline based on PN-Quant in efficiently and accurately quantifying pulmonary nodules across diverse datasets. The findings from this research highlight the potential of PN-Quant as a valuable tool for enhancing the precision and reliability of pulmonary nodule analysis in clinical settings, ultimately contributing to improved patient outcomes and clinical decision-making. Our source code is available at https://github.com/Xinkai-Tang/PN-Quant.