Annika Gerken
Orcid: 0000-0002-4327-6352
According to our database1,
Annika Gerken
authored at least 15 papers
between 2018 and 2024.
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Bibliography
2024
Improving assessment of lesions in longitudinal CT scans: a bi-institutional reader study on an AI-assisted registration and volumetric segmentation workflow.
Int. J. Comput. Assist. Radiol. Surg., September, 2024
2023
Combining arterial and venous CT scans in a multi-encoder network for improved hepatic vessel segmentation.
Proceedings of the Medical Imaging 2023: Image Processing, 2023
Abstract: Liver Tumor Segmentation in Late-phase MRI using Multi-model Training and an Anisotropic U-Net.
Proceedings of the Bildverarbeitung für die Medizin 2023, 2023
CNN-based Whole Breast Segmentation in Longitudinal High-risk MRI Study - Initial Findings on Quality vs. Quantity of Training Annotations.
Proceedings of the Bildverarbeitung für die Medizin 2023, 2023
2022
AI-Based Detection of Aspiration for Video-Endoscopy with Visual Aids in Meaningful Frames to Interpret the Model Outcome.
Sensors, 2022
Improving deep learning based liver vessel segmentation using automated connectivity analysis.
Proceedings of the Medical Imaging 2022: Image Processing, 2022
Proceedings of the Medical Imaging 2022: Image-Guided Procedures, 2022
Proceedings of the Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, 2022
Proceedings of the Bildverarbeitung für die Medizin 2022, 2022
2021
Implications of Dataset Heterogeneity on Deep Learning Performance in Medical Image Segmentation
PhD thesis, 2021
2020
Proceedings of the 17th IEEE International Symposium on Biomedical Imaging, 2020
2019
Quantitative and qualitative methods for efficient evaluation of multiple 3D organ segmentations.
Proceedings of the Medical Imaging 2019: Image Processing, 2019
Data Pooling and Sampling of Heterogeneous Image Data for White Matter Hyperintensity Segmentation.
Proceedings of the OR 2.0 Context-Aware Operating Theaters and Machine Learning in Clinical Neuroimaging, 2019
Artifact-driven sampling schemes for robust female pelvis CBCT segmentation using deep learning.
Proceedings of the Medical Imaging 2019: Computer-Aided Diagnosis, San Diego, 2019
2018
Comparison of different deep learning approaches for parotid gland segmentation from CT images.
Proceedings of the Medical Imaging 2018: Computer-Aided Diagnosis, 2018