Browsing by Author "Lamecker, Hans"

Now showing 1 - 2 of 2
  • No Thumbnail Available
    Item
    AutoImplant 2020-First MICCAI Challenge on Automatic Cranial Implant Design
    (Institute of Electrical and Electronics Engineers Inc., 2021) Li, Jianning; Pimentel, Pedro; Szengel, Angelika; Ehlke, Moritz; Lamecker, Hans; Zachow, Stefan; Estacio, Laura; Doenitz, Christian; Ramm, Heiko; Shi, Haochen; Chen, Xiaojun; Matzkin, Franco; Newcombe, Virginia; Ferrante, Enzo; Jin, Yuan; Ellis, David G.; Aizenberg, Michele R.; Kodym, Oldrich; Spanel, Michal; Herout, Adam; Mainprize, James G; Fishman, Zachary; Hardisty, Michael R.; Bayat, Amirhossein; Shit, Suprosanna; Wang, Bomin; Liu, Zhi; Eder, Matthias; Pepe, Antonio; Gsaxner, Christina; Alves, Victor; Zefferer, Ulrike; von Campe, Gord; Pistracher, Karin; Schafer, Ute; Schmalstieg, Dieter; Menze, Bjoern H.; Glocker, Ben; Egger, Jan
    The aim of this paper is to provide a comprehensive overview of the MICCAI 2020 AutoImplant Challenge. The approaches and publications submitted and accepted within the challenge will be summarized and reported, highlighting common algorithmic trends and algorithmic diversity. Furthermore, the evaluation results will be presented, compared and discussed in regard to the challenge aim: seeking for low cost, fast and fully automated solutions for cranial implant design. Based on feedback from collaborating neurosurgeons, this paper concludes by stating open issues and post-challenge requirements for intra-operative use. The codes can be found at https://github.com/Jianningli/tmi. © 1982-2012 IEEE
  • No Thumbnail Available
    Item
    Unsupervised detection of disturbances in 2D radiographs
    (IEEE Computer Society, 2021) Estacio, Laura; Mora, Rensso; Moritz, Ehlke; Lamecker, Hans; Tack, Alexander; Zachow, Stefan; Castro, Eveling
    "We present a method based on a generative model for detection of disturbances such as prosthesis, screws, zippers, and metals in 2D radiographs. The generative model is trained in an unsupervised fashion using clinical radiographs as well as simulated data, none of which contain disturbances. Our approach employs a latent space consistency loss which has the benefit of identifying similarities, and is enforced to reconstruct X-rays without disturbances. In order to detect images with disturbances, an anomaly score is computed also employing the Frechet distance between the input X-ray and the reconstructed one using our generative model. Validation was performed using clinical pelvis radiographs. We achieved an AUC of 0.77 and 0.83 with clinical and synthetic data, respectively. The results demonstrated a good accuracy of our method for detecting outliers as well as the advantage of utilizing synthetic data. © 2021 IEEE"