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Computed Tomography Pulmonary Angiography (CTPA) is an essential imaging modality for diagnosing pulmonary embolism (PE). Although its primary focus is on the pulmonary arteries, the detailed images it provides can also offer valuable insights into cardiovascular structures. This thesis focuses on developing automated systems to detect, segment, and measure mediastinal structures and PE in CTPA scans, addressing challenges such as increasing radiology workloads and the need for high diagnostic accuracy.

Paper I presents a deterministic algorithm that automates segmentation and measurement of major thoracic vessels, including the ascending aorta (AAo), descending aorta (DAo), and pulmonary trunk (PT). This method eliminates the need for manual annotations, achieving segmentation success rates of up to 100% and demonstrating high correlations with radiologist measurements (Pearson’s r = 0.68–0.99) across varying image qualities. External validation further confirmed its robust performance, with a Dice score of 0.92, highlighting its clinical applicability.

Paper II builds on this by training a deep learning model (nnU-Net) using segmentation masks derived from the deterministic approach. This model achieved improved segmentation accuracy (Dice score: 0.95) and higher success rates for detecting AAo, DAo, and PT compared to traditional methods. Validation on external datasets further confirmed its reliability and potential for integration into clinical workflows.

Paper III focuses on detecting pulmonary embolism (PE) using a 3D U-Net generated by the nnU-Net framework, which was trained on an internal dataset of 149 CTPA scans containing PE. The model achieved exceptional classification performance, with sensitivity and specificity exceeding 96% in internal and external validations. Advanced post-processing strategies improved specificity and reduced false positives, outperforming state-of-the-art methods in sensitivity, specificity, and overall diagnostic accuracy.

Future work includes integrating these models into Picture Archiving and Communication Systems (PACS) for seamless clinical deployment, refining algorithms for PT measurement and right/left ventricle analysis, and exploring advanced architectures like vision transformers to further enhance performance. In conclusion, the proposed developments aim to elevate diagnostic precision and clinical outcomes, paving the way for routine deployment of automated CTPA analysis systems in healthcare.