In MICE we have found a high-end framework to register and analyze multi-modal medical image information. Rigid or non-rigid registration of CT, MR and PET images of cancer patients is linked through an intuitive flowchart. Importantly, the environment can also include advanced custom-made routines from MatLab or Python. MICE therefore allows us to assess potential new image diagnostic tools in a professional environment where both the simplicity of an idea and the complexity of its implementation needs to be considered.
Eirik Malinen, Professor in medical physics, University of Oslo, Oslo, Norway
MICE has proved an invaluable tool for image analysis – combining flexibility with efficiency. It has an intuitive user-interface which allows people without extensive programming skills to harness a powerful image processing environment. Before MICE, we would have either used a number of radiotherapy applications to piece together the required approach, or utilised other, less intuitive proprietary programming environments to perform image analysis. MICE provides a single interface which, so far, has solved all our image and data analysis requirements.
Hazel McCallum, Consultant Clinical Scientist, Northern Centre for Cancer Care, Newcastle upon Tyne, UK
Together with Prof. Tufve Nyholm we were working on a publication about the different textural behavior of tumor and healthy tissue of patients suffering from prostate cancer. These investigations were mostly performed using an in-house Matlab code to extract the textures. A cumbersome and time-consuming task. After successfully finishing this study, we continued the texture analysis of radiotherapy patients, but used MICE as a tool of choice. By doing so, it was possible to automate the workflow and thus increase the efficiency of data evaluation, guaranteeing a reproducible analysis of data. Besides the evaluation of prostate cancer patients also cervix cancer moved into our focus. Not only did we investigate the difference between malign and healthy tissue, but also between hypoxic and normoxic tumor sub-volumes and the treatment response based on histogram information from different time points of treatment. Furthermore, voxel by voxel analysis was performed in between neighboring timepoints witch conveniently replaced a previous Matlab workflow from a similar predecessor study.
A PhD student started to use MICE to create synthetic CTs from field MR images, where also registration of images plays an important role.
Besides that, MICE is also used in other studies. For example, to calculate the equivalent uniform dose, normal tissue complication probability, and tumor control probability. Features that were implemented in MICE after our request.
In general, using MICE for data evaluation is highly appealing to students and researchers, as it involves little start-up difficulties. The graphical way of programming the analysis comes rather natural for most users and the developers are keen to help if some functions remained secret.
Peter Kuess, Phd, Department of Radiotherapy, Medical University of Vienna.