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Annika Liebgott

4 accepted papers

2021

Automated Multi-Organ Segmentation in Pet Images Using Cascaded Training of a 3d U-Net and Convolutional Autoencoder

ICASSP 2021accepted

PET imaging is an important tool in clinical diagnostics, especially in oncology as it is able to visualize ongoing metabolic processes, e.g. caused by a tumor. Due to the low spatial resolution, a corresponding CT or MRI scan is normally necessary to gain knowledge about the physiological structure…

Cited by 0SourceScholar
2018

Automated Detection of High FDG Uptake Regions in CT Images

ICASSP 2018accepted

Combined PET-CT scan is an important diagnostic tool in modern medicine, e.g. for staging or treatment planning in the field of oncology. Especially in small structures, like a tumour, textural variations visible in a PET image are not visually recognizable within a CT scan from the same region. Thu…

Cited by 0SourceScholar
2018

Automatic Motion Artifact Detection for Whole-Body Magnetic Resonance Imaging

ICASSP 2018accepted

Magnetic resonance (MR) plays an important role in medical imaging. It can be flexibly tuned towards different applications for deriving a meaningful diagnosis. However, its long acquisition times and flexible parametrization make it on the other hand prone to artifacts which obscure the underlying…

Cited by 0SourceScholar
2016

Active learning for magnetic resonance image quality assessment

ICASSP 2016accepted

In medical imaging, the acquired images are usually analyzed by a human observer and rated with respect to a diagnostic question. However, this procedure is time-demanding and expensive. Further more, the lack of a reference image makes this task challenging. In order to support the human observer i…

Cited by 0SourceScholar