Deep Learning Techniques to Support Endovascular Surgical Procedures

Clinical Problem. Medical image analysis plays a crucial role in all the stages included in endovascular surgery, from screening to follow-up monitoring. Given the growing availability of clinical images, automatic tools that can process data in a quick and effective way are essential for clinical support. Methods. In this thesis, deep learning (DL) methodologies are designed to support clinicians in three different phases of endovascular surgery: the preoperative phase, the intraoperative phase, and the postoperative phase. In the preoperative phase, deep learning is exploited to perform automatic segmentation of aortic lumen and thrombus dealing with spatial coherence. Then, geometric measurements are extracted from the segmentation, allowing geometric evaluation and aneurysm screening. For the intraoperative phase, a deep learning model is used as a surrogate of finite-element analysis to predict the intraoperative aortic deformations induced by tools-tissue interaction. Finally, for the postoperative phase, deep learning is exploited to perform aortic lumen segmentation and geometric analysis is performed on multiple follow-up patient acquisitions. Results. For the preoperative stage, the developed segmentation pipelines provided better results compared to state-of-the art approaches. Automated geometric measurements showed comparable results to manual ones, and aneurysm screening provided promising results. For the intraoperative stage, the deep learning model showed good accuracy in predicting intraoperative aortic deformations. For the postoperative stage, the preliminary longitudinal analysis of aortic geometry showed that landing zone diameters tend to change over the follow-up acquisitions. Conclusions. This work presents a platform for the automatic analysis of CTA scans of patients affected by aortic diseases. The developed methodologies allow to rapidly process large image databases; the results of such analysis (e.g., thrombus and lumen segmentation, geometric measurements) can be useful in the research field as well as in clinical practice.