[image] When creating a plan for radiation treatment of cancer, it is critical to carefully define the tumor volume. In order to avoid unwanted side-effects, it is also necessary to identify different organs in the tumor's environment, so-called organs at risk. This process is called contouring and is usually performed using manual or semi-automatic tools.

The deep learning contouring feature in RayStation uses machine learning models that have been trained and evaluated on previous clinical cases to create contours of the patient's organs automatically and quickly. Healthcare staff can review and, if necessary, adjust the contours. The final result is reached much faster than with other methods.

Andreas Johansson, physicist at Region Sörmland, which runs Mälar Hospital, says: "We used deep learning to contour the first patient on May 26 and the treatment was performed on June 9. From taking 45-60 minutes per patient, the contouring now only takes 10-15 minutes, which means a huge time saving."

Johan Löf, founder and CEO, RaySearch, says: "Mälar Hospital was very quick to implement RayStation in 2015 and now it has shown again how quickly new technology can be adopted and brought into clinical use. The fact that this helps to resolve a situation where hospital resources are unusually strained is of course also very positive."