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Automated Deep Learning-based Segmentation of Brain, SEEG and DBS Electrodes on CT Images.

, , , , , , and . Bildverarbeitung für die Medizin, page 92-97. Springer, (2021)

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Lead-DBS v2: Towards a comprehensive pipeline for deep brain stimulation imaging., , , , , , , , , and 9 other author(s). NeuroImage, (2019)Assessment of Electrode Displacement and Deformation with Respect to Pre-Operative Planning in Deep Brain Stimulation., , , , and . Bildverarbeitung für die Medizin, page 77-82. Springer, (2015)FastField: An open-source toolbox for efficient approximation of deep brain stimulation electric fields., , , , and . NeuroImage, (2020)Unified Retrieval for Streamlining Biomedical Image Dataset Aggregation and Standardization., , , and . Bildverarbeitung für die Medizin, page 328-333. Springer, (2024)Abstract: The Importance of Dataset Choice Lessons Learned from COVID-19 X-ray Imaging Models., , , , and . Bildverarbeitung für die Medizin, page 114. Springer, (2022)The effect of dataset confounding on predictions of deep neural networks for medical imaging., , and . NLDL, Septentrio Academic Publishing, (2022)Automated Deep Learning-based Segmentation of Brain, SEEG and DBS Electrodes on CT Images., , , , , , and . Bildverarbeitung für die Medizin, page 92-97. Springer, (2021)Rapid artificial intelligence solutions in a pandemic - The COVID-19-20 Lung CT Lesion Segmentation Challenge., , , , , , , , , and 26 other author(s). Medical Image Anal., (2022)Initialisation of Deep Brain Stimulation Parameters with Multi-objective Optimisation Using Imaging Data., , , , , and . Bildverarbeitung für die Medizin, page 297-302. Springer, (2022)Lead-DBS v3.0: Mapping deep brain stimulation effects to local anatomy and global networks., , , , , , , , , and 13 other author(s). NeuroImage, (March 2023)