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Text and multimedia material, such as images and videos, are released under the Creative Common Attribution-Sharealike 3.0 Unported license (CC-BY-SA) and the GNU Free Documentation License (GFDL) (unversioned, with no invariant sections, front-cover texts, or back-cover texts).

Text, multimedia and source code can be re-used and modified, given that the original sources are cited. Namely:

• Studierfenster.at
• Egger J, Wild D, Weber M, Bedoya CA, Karner F, Prutsch A, Schmied M, Dionysio C, Krobath D, Jin Y, Gsaxner C. Studierfenster: an Open Science Cloud-Based Medical Imaging Analysis Platform. Journal of Digital Imaging. 2022 Jan 21:1-6.

We have a new server, with better hardware! Please visit: http://studierfenster.icg.tugraz.at

Here you can take a look at three axial CTs of COVID-19 cases. Just click on one of the buttons and the respective case will load automatically in the Medical 3D Viewer. So this is also a great way to try out the viewer, even if you do not have a volume file at hand. The cases are part of a dataset that can be freely used and is available here.

Here you can take a look at two CTs of dogs. The first one shows a lung lobe torsion and the second one a lung hernia. Just click on one of the buttons and the respective case will load automatically in the Medical 3D Viewer. So this is also a great way to try out the viewer, even if you do not have a volume file at hand. We thank the University of Veterinary Medicine, Vienna for providing the data.

Image feature extraction for tumour characterization

The image segmentation pairs that are loaded onto the server in the course of this module are compared with a big amount of predefined, descriptive features. Therefore, for each image segmentation pair about 1600 relevant features are calculated and stored in a Csv file which can then be downloaded. The data obtained can then be used for data anaysis and association studies.

The “Dicom Browser” allows parsing a local folder with DICOM files. Afterwards, the whole folder can be converted to compressed .NRRD files and downloaded as a single .zip file. It is also possible to select specific Studies or Series, and only convert these.

We chose the compressed .NRRD format, because it contains only the image data. All patient DICOM tags will be removed. The compressed .NRRD files can then be opened with the “Medical 3D Viewer”, for visualization in 2D, 3D and further processing.

Note: The “Dicom Browser” is completely client-sided. No data is transferred to our server during the conversion!

Here the Dice Similarity Coefficient (DSC) and the Hausdorff Distance (HD) between two segmentation masks (in .nrrd format) can be calculated.

The File Converter, converts a medical volume file (e.g. a non-compressed NRRD) to a (compressed/binary) NRRD. The compressed NRRD can then be used for example with our Medical 3D Viewer.

The 3D Skull Reconstruction performs an automatic cranial defect reconstruction via our cloud platform with Deep Learning. The main steps are:

• Choosing a defected skull dataset
• Converting the image dataset (NRRD) to a mesh
• Choosing a defected skull dataset again
• Perform the skull reconstruction with Deep Learning on our sever
• Perform the implant generation (subtracting the defected skull from the reconstructed)

Afterwards, the implant can be downloaded and opened for example in MeshLab for further post-processing, like removing noise.

The Medical VR Viewer allows viewing medical data in Virtual Reality (VR), for example with the Google Cardboard or the HTC Vive (via the WebVR App). However, you need to convert your (medical) data (e.g., in NRRD format) to the VTI (.vti) format. This can be done with ParaView.

The 3D Face Reconstruction module allows registering two facial meshes.

The Medical 3D Viewer allows viewing of medical data (CT, MRI, etc.) in 2D and 3D (Volume Rendering) directly in a standard Web Browser. More enhanced functionalities of the 3D Viewer are the manual slice-by-slice outlining (segmentation) of structures in medical images (brush and contour modes available) and the manual placing of Landmarks in medical images. More sophisticated features of the 3D Viewer are the inpainting of Aortic Dissections with a Generative Adversarial Network (GAN) and the automatic aortic landmark detection with Deep Learning in CTA (Computed Tomography Angiography) scans.

The ITK/VTK interface allows the processing of medical data (CT, MRI, etc.) directly in a standard Web Browser. <\p>

Goal is to have an easy access tool for the popular libraries ITK (Insight Toolkit) and VTK (Visualization Toolkit) independent on the hardware of the user.

Important to mention is that all the hardware power is offered by the server hardware, as well as that the data is temporary saved on the server due to the implementation of the interface. Currently the Interface offers:

• MedianImageFilter
• GaussianBlurringFilter
• MarchingCubes
• resampleDataset
• ImageRegistration

Progress: