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Micro-CT imaging enables noninvasive and longitudinal assessment of mouse lung pathology in genetically engineered lung cancer models, which is crucial for evaluating the effectiveness of potential therapeutics. However, manual lung analysis is time-consuming, and an automated workflow is needed. We present a strategy to optimize a deep learning-based workflow for lung tumor analysis using limited annotations. A 2D UNet model (M1) was trained for chest cavity segmentation using an existing dataset with lung, heart, and vasculature segmentations from wild-type mice (n = 10) and chest cavity segmentations of mice with lung tumors (n = 5). M1 then generated chest cavity segmentations for 20 additional lung tumor burdened mice. Next, non-rigid registration aligned wild-type segmentations with tumor burdened lung scans (n = 25) using the chest cavity mask predicted by M1. Subsequently, M1 was fine-tuned, and a heart segmentation model (M2) was trained with 10 wild-type and 25 tumor burdened lung scans. Heart segmentation was then subtracted from the chest segmentation, and a threshold-based algorithm (-1000 to -300 a.u.) was applied to reveal functional lung volume. Finally, tumor segmentation was estimated by subtracting functional lung and heart volumes from chest cavity volume in a cohort of lung tumor burdened mice. The resulting workflow provides “chest”, “heart”, “functional lung” and “tumor plus vasculature” segmentations for quantification and visualization. The models generate segmentations in approximately 13 seconds per mouse, with high accuracy (Dice ratios: 0.96 for chest cavity, 0.90 for heart). This workflow enables longitudinal monitoring of tumor progression, supporting applications in oncology drug discovery.
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