Mobile Augmented Reality and 3D Printing to Involve Patients in Treatment Decisions for Prostate Cancer. - Sebastian Weiß; Alena Schnurr; André Mewes; Thomas Hoffmann 002; Daniel Schindele; Martin Schostak; Christian Hansen 001 (CURAC, 2016)

Automatic high resolution segmentation of the prostate from multi-planar MRI. - Anneke Meyer; Alireza Mehrtash; Marko Rak; Daniel Schindele; Martin Schostak; Clare M. Tempany; Tina Kapur; Purang Abolmaesumi; Andriy Fedorov; Christian Hansen 001 (ISBI, 2018)

Towards Patient-Individual PI-Rads v2 Sector Map: Cnn for Automatic Segmentation of Prostatic Zones From T2-Weighted MRI. - Anneke Meyer; Marko Rak; Daniel Schindele; Simon Blaschke; Martin Schostak; Andriy Fedorov; Christian Hansen 001 (ISBI, 2019)

Learning Multi-Modal Volumetric Prostate Registration With Weak Inter-Subject Spatial Correspondence. - Oleksii Bashkanov; Anneke Meyer; Daniel Schindele; Martin Schostak; Klaus-Dietz Tönnies; Christian Hansen 001; Marko Rak (ISBI, 2021)

Uncertainty-aware temporal self-learning (UATS): Semi-supervised learning for segmentation of prostate zones and beyond. - Anneke Meyer; Suhita Ghosh; Daniel Schindele; Martin Schostak; Sebastian Stober; Christian Hansen 001; Marko Rak (Artif. Intell. Medicine, 2021)

Anisotropic 3D Multi-Stream CNN for Accurate Prostate Segmentation from Multi-Planar MRI. - Anneke Meyer; Grzegorz Chlebus; Marko Rak; Daniel Schindele; Martin Schostak; Bram van Ginneken; Andrea Schenk; Hans Meine; Horst K. Hahn; Andreas Schreiber 005; Christian Hansen 001 (Comput. Methods Programs Biomed., 2021)