Automated Synthetic Lung Tumor Generation for Training a U-Net Model on Lung CT Slices

Abstract

This thesis presents an automated pipeline for generating synthetic lung tumor CT images and corresponding segmentation masks to improve deep learning–based tumor segmentation in low-data settings. Real tumor regions are extracted from annotated CT scans and inserted into healthy lung slices using a 2D Tukey window and Poisson image blending to preserve realistic texture and boundaries. Ground truth masks are generated automatically using the Segment Anything Model and refined through morphological operations. The synthetic and real images are used to train a 2D U-Net segmentation model, which is evaluated across multiple experimental trials on an external dataset composed entirely of real pathological CT scans. Results show that models trained with carefully curated synthetic data match or outperform models trained on real data alone, demonstrating improved generalization and robustness. This work highlights the potential of automated synthetic data generation to reduce reliance on large, manually annotated medical imaging datasets.