Applications of Synthetic Data Integration for Deep Learning for Volumetric Analysis and Segmentation in Thoracic CT Imaging

This study presents a framework for processing Digital Imaging and Communications in Medicine (DICOM) medical imaging data by integrating synthetic objects for volumetric analysis and simulation for applications in assessment of computed tomography (CT) imaging used in thoracic surgery. Functions are designed to generate synthetic objects including geometric shapes such as spheres, cubes, rectangular prisms, cylinders, and blobs with known volumes. Validation is performed through test functions to ensure accuracy and consistency. Additionally, the use of UNet models for segmenting various chest pathologies, such as hemothorax and pneumothorax, as well as organs, is demonstrated. The created framework is used to generate synthetic data to address the scarcity of publicly available hemothorax CT imaging data. Models achieved high performance, assessed by various metrics. The framework and models provide a robust tool for data augmentation and analysis in medical imaging, potentially enhancing clinical decision-making and supporting research in thoracic surgery and related fields.