A quality-refinement process for medical imaging applications.

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.

Interactive segmentation framework of the Medical Imaging Interaction Toolkit.

Interactive methods are indispensable for real world applications of segmentation in medicine, at least to allow for convenient and fast verification and correction of automated techniques. Besides traditional interactive tasks such as adding or removing parts of a segmentation, adjustment of contours or the placement of seed points, the relatively recent Graph Cut and Random Walker segmentation methods demonstrate an interest in advanced interactive strategies for segmentation. Though the value of toolkits and extensible applications is generally accepted for the development of new segmentation algorithms, the topic of interactive segmentation applications is rarely addressed by current toolkits and applications. In this paper, we present the extension of the Medical Imaging Interaction Toolkit (MITK) with a framework for the development of interactive applications for image segmentation. The framework provides a clear structure for the development of new applications and offers a plugin mechanism to easily extend existing applications with additional segmentation tools. In addition, the framework supports shape-based interpolation and multi-level undo/redo of modifications to binary images. To demonstrate the value of the framework, we also present a free, open-source application named InteractiveSegmentation for manual segmentation of medical images (including 3D+t), which is built based on the extended MITK framework. The application includes several features to effectively support manual segmentation, which are not found in comparable freely available applications. InteractiveSegmentation is fully developed and successfully and regularly used in several projects. Using the plugin mechanism, the application enables developers of new algorithms to begin algorithmic work more quickly.

Simulation and visualization of dose uncertainties due to interfractional organ motion.

In this paper, we deal with the effects of interfractional organ motion during radiation therapy. We consider two problems: first, treatment plan evaluation in the presence of motion, and second, the incorporation of organ motion into IMRT optimization. Concerning treatment plan evaluation, we face the problem that the delivered dose cannot be predicted with certainty at the time of treatment planning but is associated with uncertainties. We present a method to simulate stochastic properties of the dose distribution. This provides the treatment planner with information about motion-related risks of different plans and may support the decision for or against a treatment plan. This information includes the display of probabilities of individual voxels to receive doses from a therapeutical interval or above critical levels, as well as a diagram that shows the variability of the dose volume histogram. Concerning the incorporation of organ motion into IMRT planning, we further analyse the approach of inverse planning based on probability distributions of possible patient geometries. We consider three different sources of uncertainty, namely uncertainty about the amplitude of motion, a systematic error and a random error. We analyse the impact of these sources of uncertainty on the optimized treatment plans for prostate cancer.