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Efficient web-based review for automatic segmentation results of volumetric DICOM images

  • 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.

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Author:Tobias Stein
Referee:Rolf Bendl, Klaus Maier-Hein
Advisor:Marco Nolden
Document Type:Master's Thesis
Year of Completion:2018
Publishing Institution:Hochschule Heilbronn
Granting Institution:Hochschule Heilbronn
Date of final exam:2018/12/17
Release Date:2018/12/21
Tag:Software; bildgebende Verfahren; medizinische Bildgebung
GND Keyword:Bildgebung
Pagenumber:77 Seiten
DDC classes:000 Allgemeines, Informatik, Informationswissenschaft
Access Right:Frei zugänglich
Licence (German):License LogoCreative Commons - Namensnennung, Nicht kommerziell, Keine Bearbeitung