The web-based Go-Smart environment is a scalable system that allows the prediction of minimally invasive cancer treatment. Interventional radiologists create a patient-specific 3D model by semi-automatic segmentation and registration of pre-interventional CT (Computed Tomography) and/or MRI (Magnetic Resonance Imaging) images in a 2D/3D browser environment. This model is used to compare patient-specific treatment plans and device performance via built-in simulation tools. Go-Smart includes evaluation techniques for comparing simulated treatment with real ablation lesions segmented from follow-up scans. The framework is highly extensible, allowing manufacturers and researchers to incorporate new ablation devices, mathematical models and physical parameters

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In teleradiology doctors view the image data of a healthcare institution from a remote site. To do so they typically connect to an image repository, download the necessary data and finally view it in a locally installed DICOM viewer. Recent research activities in teleradiology [1, 2, 3, 4] focus on the implementation of ubiquitously accessible web-based DICOM viewers which simplify the access and the collaboration among radiologists. However, these solutions either have a weak performance, they run on the client side only, or they are not as feature rich as their desktop counterpart. In this contribution we present an architecture of a web based DICOM viewer which has both, good performance as well as advanced features such as the visualization of anatomical structures. On the client side only native web technologies are used. The whole architecture has been implemented and evaluated in the GoSmart environment, a planning tool for minimally invasive cancer treatment

In teleradiology a vast amount of medical images is sent from one location to another location. If the network infrastructure between the locations is poor, users experience long download times or, if a client application is used, application lags. To solve this issue lossless compression algorithms can be used as a first option. Unfortunately these algorithms can only compress the data to a certain degree which is most of the time not enough for the heavy requirements in teleradiology. As a second option the image data can be compressed lossily by reducing the image quality. This however can have an impact on the work of the user and also on image processing tools, when the images are post-processed. In this contribution we give a first impression of frame rate and resolution effects on the work of both, humans and machines, using the example of tumor diagnosis

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