• Treffer 1 von 10
Zurück zur Trefferliste

Deep Learning methods for partial to whole object registration in mixed reality guided liver surgery - preliminary experiments

  • 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.

Volltext Dateien herunterladen

Metadaten exportieren

Metadaten
Verfasserangaben:Guido Reinfurt
URN:urn:nbn:de:bvb:860-opus4-3943
DOI:https://doi.org/10.57688/394
Gutachter/Betreuer:Stefanie Remmele
Dokumentart:Bachelorarbeit
Sprache:Englisch
Jahr der Fertigstellung:2024
Veröffentlichende Institution:Hochschule für Angewandte Wissenschaften Landshut
Titel verleihende Institution:Hochschule für Angewandte Wissenschaften Landshut
Datum der Freischaltung:27.05.2024
Freies Schlagwort / Tag:Artificial Intelligence; Augmented Reality; Liver Surgery; Point clouds; Registration
Seitenzahl:56
Fakultät / Institut:Fakultät Elektrotechnik und Wirtschaftsingenieurwesen
Lizenz (Deutsch):Creative Commons - CC BY - Namensnennung 4.0 International