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Medical imaging produces many images every day in clinical routine. Keeping up with the
daily image analysis task and this vast amount of data is quite a challenge for radiologists.
However, these analysis tasks can be automated with well-proven automatic segmentation
methods. Segmentation reviewing of an expert is necessary because learningbased
automatic segmentation methods may not perform well on exceptional image
data. Creating valid segmentations by reviewing them also improve the learning-based
methods.
Combining established standards with modern technologies creates a flexible environment
to efficiently evaluate multiple segmentation algorithm outputs based on different metrics
and visualizations and report these analysis results back to a clinical system environment.
The presented software system can inspect such quantitative results in a fast and intuitive
way, potentially improving the daily repetitive segmentation review and rework of a
research radiologist. The presented system is designed to be integrated into a virtual
distributed computing environment with other systems and analysis methods. Critical
factors for this particular environment are the handling of many patient data and routine
automated analysis with state of the art technology.
First experiments show that the time to review automatic segmentation results can be
roughly divided in half while the confidence of the radiologist is enhanced. The system
is also able to highlight individual slices which are essential for the expert’s review
decision. For this highlighting, different metric scores are compared and evaluated.