Automatic kidney segmentation, reconstruction, preoperative planning and 3D printing

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Ζάγκου, Σπυριδούλα
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Renal cancer is the seventh most prevalent cancer among men and the tenth most frequent cancer among women, accounting for 5% and 3% of all adult malignancies, respectively. Κidney cancer is increasing dramatically in developing countries due to inadequate living conditions but and in developed countries due to bad lifestyles, smoking, obesity, and hypertension. For decades, radical nephrectomy (RN) was the standard method to address the problem of the high incidence of kidney cancer. However, the utilization of minimally invasive partial nephrectomy (PN), for the treatment of localized small renal masses has increased with the advent of laparoscopic and robotic-assisted procedures. In this framework, certain factors must be considered in surgical planning and decision-making of partial nephrectomies, such as the morphology and location of the tumor. Advanced technologies such as automatic image segmentation, image and surface reconstruction, and 3D printing have been developed to assess the tumor anatomy before surgery and its relationship to surrounding structures, such as the arteriovenous system, with the aim of preventing damage. Overall, it is obvious that 3D printed anatomical kidney models are very useful to urologists, surgeons, and researchers as a reference point for preoperative planning and intraoperative visualization for a more efficient treatment and a high standard of care. Furthermore, they can provide a lot of degrees of comfort in education, in patient counseling, and in delivering therapeutic methods customized to the needs of each individual patient. To this context, the fundamental objective of this thesis is to provide an analytical and general pipeline for the generation of a renal 3D printed model from CT images. In addition, there are proposed methods to enhance preoperative planning and help surgeons to prepare with increased accuracy the surgical procedure so that improve their performance.
Computed tomography, Semantic segmentation, Convolutional neural networks(CNNs), Surface reconstruction, 3D printing, Operative assistance, Mesh processing, Delineation of tumor-normal tissue interface