Methods Inf Med 2009; 48(04): 336-339
DOI: 10.3414/ME9232
Original Articles
Schattauer GmbH

A Quality-refinement Process for Medical Imaging Applications

J. Neuhaus*
1   Division of Medical and Biological Informatics, German Cancer Research Center, Heidelberg, Germany
,
D. Maleike*
1   Division of Medical and Biological Informatics, German Cancer Research Center, Heidelberg, Germany
,
M. Nolden
1   Division of Medical and Biological Informatics, German Cancer Research Center, Heidelberg, Germany
,
H.-G. Kenngott
1   Division of Medical and Biological Informatics, German Cancer Research Center, Heidelberg, Germany
,
H.-P. Meinzer
1   Division of Medical and Biological Informatics, German Cancer Research Center, Heidelberg, Germany
,
I. Wolf
1   Division of Medical and Biological Informatics, German Cancer Research Center, Heidelberg, Germany
› Author Affiliations
Further Information

Publication History

05 June 2009

Publication Date:
17 January 2018 (online)

Summary

Objectives: To introduce and evaluate a process for refinement of software quality that is suitable to research groups. In order to avoid constraining researchers too much, the quality improvement process has to be designed carefully. The scope of this paper is to present and evaluate a process to advance quality aspects of existing research prototypes in order to make them ready for initial clinical studies. The proposed process is tailored for research environments and therefore more lightweight than traditional quality management processes.

Methods: Focus on quality criteria that are important at the given stage of the software life cycle. Usage of tools that automate aspects of the process is emphasized. To evaluate the additional effort that comes along with the process, it was exemplarily applied for eight prototypical software modules for medical image processing.

Results: The introduced process has been applied to improve the quality of all prototypes so that they could be successfully used in clinical studies. The quality refinement yielded an average of 13 person days of additional effort per project. Overall, 107 bugs were found and resolved by applying the process.

Conclusions: Careful selection of quality criteria and the usage of automated process tools lead to a lightweight quality refinement process suitable for scientific research groups that can be applied to ensure a successful transfer of technical software prototypes into clinical research workflows.

* First two authors contributed equally to this work.


 
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