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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 0Source ↗Scholar ↗
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 2Source ↗Scholar ↗
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 0Source ↗Scholar ↗
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 0Source ↗Scholar ↗