We have managed to upload most of the slides as far as they are available. We are very happy about the great response and thank all contributors for making the meeting the success it was. June 2016: Thank you for joining the MITK Users Meeting 2016.January 2017: MITK Users Meeting 2017: We reschedule the MITK Users Meeting.MITK 2016.11 Release Workbench and Toolkit 2016.11 The binary installers with MITK Diffusion 2017.07 application for Windows and Linux can be found here: The full release notes and further information about the application can be found here: Many new features have been added since the last release in 2014 and many bugs have been fixed. New MITK Diffusion installers with tons of new features including deep learning based modules such as TractSeg and a new interface to Dipy! Always stay fresh with our nightly installers and check out the full feature list īased on the current master branch of MITK we have released a beta version of the MITK Diffusion application. You can download an installer containing the MITK Workbench application for Windows, Linux, and macOS. Read the full release notes and a list of highlights. Highlights include extended DICOM support (RTSTRUCT, RTDOSE, RTPLAN, SEG), improved macOS support, and a dark theme. The plugin also continues to support interactive mode annotation where the AI models are constantly learning from user inputs.įor more information and guidance, please visit the Nvidia Developer Blog. Auto Segmentation along with Annotation Server in Clara Train SDK provides capabilities for single step organ segmentation. The Nvidia AI-Assisted Annotation plugin has been updated to include the brand new Auto Segmentation feature. The Nvidia AI-Assisted Annotation plugin has been updated to version v1.0.2, including the brand new DeepGrow feature of the Nvidia Clara Train Application Framework v3.0.įor more information, please visit the Nvidia Clara Train Application Framework documentation. Second update on Nvidia AI-Assisted Annotation Read the changelog for a selected short list of highlights since the last release MITK v2018.04.2.ĭownload MITK v2021.02 for Windows, Linux, or macOS. In the past two years we resolved a whopping 740 tasks to improve and extend MITK. We are happy to announce the release of MITK v2021.02 ( download here). Read the changelog for a selected short list of highlights since the last release MITK v2021.02.ĭownload MITK v2021.10 for Windows, Linux, or macOS. In the past eight months we resolved about 180 tasks to improve and extend MITK. We are happy to announce the release of MITK v2021.10 ( download here). Read the changelog for a selected short list of highlights since the last release MITK v2021.10.ĭownload MITK v2022.04 for Windows, Linux, or macOS. In the past six months we resolved about 130 tasks to improve and extend MITK. We are happy to announce the release of MITK v2022.04 ( download here). Read the changelog for a selected short list of highlights since the last release MITK v2022.04.ĭownload MITK v2022.10 for Windows, Linux, or macOS. In the past six months we resolved about 150 tasks to improve and extend MITK. We are happy to announce the release of MITK v2022.10 ( download here). Read the changelog for a selected short list of highlights since the last release MITK v2022.10.ĭownload MITK v2023.04 for Windows, Linux, or macOS. In the past six months we resolved about 140 tasks and pushed nearly 500 commits into our Git repository to improve and extend MITK. We are happy to announce the release of MITK v2023.04 ( download here). Then requires you to reboot, and re-run the installer tool.Download MITK v2023.04 News MITK v2023.04 Release And installs packages from snap repositoriesĪlso currently the install appears to be hung, and finally continued after about 10 minutes.It installs packages from git repositories. This package installs deb packages - so it requires a user to have root.(So it does not mess up other applications usage). I was going to install this in the python venv environment with system-defaults. It looks like it uses spyder which brings in other issues, but some like spyder. I will need to reboot and again re-run the installer script as it just finished the first stage of the install. (nvidia tends to use their own version of the deprecated nvidia-docker2 versus docker.io). I think I can make it work with Lambda Stack by adding other repositories. However, if you are going to move to use ‘NVIDIA Data Science Workbench’. The way it is written it is not directly compatible with any normal install, and can break usage not just for the user installing but for other users on system, also it does not isolate to a virtual environment, so it may break other packages you are working on. NVIDIA Data Science Workbench is writes all over the system replacing packages.
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