Automatic organ segmentation based on novel imaging techniques and 3D deep convolutional neural networks
Reduce organ segmentation time and improve accuracy of organ at risk segmentation in a radiotherapy treatment workflow.
In 2016 around 108.400 patients were diagnosed with cancer in The Netherlands, of which 50% is treated with radiotherapy. Before any radiotherapy treatment, a complex treatment plan is made to calculate radiation dose to be delivered to the tumor and to spare healthy surrounding organs at risk. The dose in the organs at risk is currently evaluated by manually segmenting every organ at risk on every CT (computed tomography) slice, which the most time consuming task in radiotherapy, and is also very prone to user-variation. The tumors can have very complex shapes and are usually delineated manually by experienced oncologists. This is for now a task that cannot easily be automated.
In our research proposal we would like to investigate the combinational use of our in-house developed multi-atlas based contouring algorithm and a 3D-Unet automatic contouring algorithm to perform automatic segmentations of organs at risk on different CT datasets. Also the use of novel imaging techniques such as dual-energy CT will be investigated. This imaging technique provides a level of anatomical detail surpassing the current imaging standard: single-energy CT. The resulting contours originating from the automatic contouring algorithm will be compared with the clinical standard by an experienced radiation oncologist.
Composition research team
Brent van der Heyden (MSC - Applied medical-nuclear engineering)
Frank Verhaegen (PhD - Medical Physics engineering)
Daniele Eekers (MD, Radiation Oncology)
Cecile Wolfs (MSc, Knowledge engineering, Operations research)