TY - THES A1 - Reinfurt, Guido T1 - Deep Learning methods for partial to whole object registration in mixed reality guided liver surgery - preliminary experiments N2 - This work primarily focuses on improving registration procedures within augmented reality (AR) applications, by analyzing the reliability of point cloud reconstruction as a preprocessing step. Therefore, a state-of-the-art, transformer base network is utilized. For the training process a comprehensive dataset specifically designed for training deep learning models is created. This dataset will be tailored to the task of reconstructing partial liver views for augmented reality guidance in medical applications. The validation process for this dataset will be two-fold: • Deep Learning Network Training: The created dataset will be used to train a state-of-the-art deep learning network, specifically a transformer-based model, the “PoinTr”-network. This training will evaluate the dataset's effectiveness in facilitating accurate point cloud reconstruction. • Testing with Real-World Scenarios: A separate testing dataset, meticulously designed to reflect various real-world conditions encountered in AR guidance, such as variations in point cloud density, occlusions, and sensor noise, will be used to assess the performance of the trained model. This evaluation will analyze the model's ability to generate accurate reconstructions under diverse and potentially challenging scenarios. KW - Registration KW - Augmented Reality KW - Artificial Intelligence KW - Liver Surgery KW - Point clouds Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:860-opus4-3943 ER -