LuTuFlow: a deep learning-based pipeline for quantitative characterization of mouse lung tumors in CT imaging

Abstract

Computed tomography (CT) is a widely used, non-invasive imaging tool to monitor tumor growth. Tumor identification and volumetric quantification are crucial for assessing tumor burden, whereas tracking individual tumors by aligning scans from multiple time points reveals disease progression. However, these processes remain largely dependent on manual analysis, limiting the accuracy, reproducibility, objectivity, and scalability of tumor characterization. Several deep learning models have enabled automated segmentation and object identification. However, open-source, integrated software platforms that cover the entire workflow - including processing raw CT images, annotating and tracking tumors through time, and generating data reports and visualizations - are still needed. Here, we present LuTuFlow, a computer vision pipeline incorporating a nnU-Net-based deep learning model, configured for murine lung segmentation and tumor analysis. We integrated these modules into a Napari plugin, allowing researchers to apply the entire automated workflow to transform raw CT scan datasets into quantitative readouts and interactive visualizations in a simple graphical user interface. The resulting model, iteratively refined using expert-corrected annotations, generated reliable quantitative estimates of tumor burden.