Automatic brain tumour segmentation in 18F-FDOPA PET using PET/MRI fusion

PET-MRI fusion is widely used in oncology for early tumour diagnosis, localisation and monitoring of therapy effects. Automatic extraction of the lesions on PET images is desirable, but remains problematic. Manual segmentation of PET images is time consuming, and restricts the definition of the tumour extent to some arbitrary threshold. This can be sub-optimal in brain tumour for instance, where tumour is diffused by nature. Moreover, when the tracer uptake is not limited to the invaded regions, it becomes more difficult for an expert to define a precise contour. In this work, we propose a soft segmentation approach to automatically segment brain tumours in 18F-FDOPA PET images using a tumour growth model. This is based on extrapolating the tumour extent starting from tumour boundaries extracted from T1W MRI. A reaction-diffusion model is utilised for the extrapolation task to obtain tumour probability density. We evaluate our method on patient's PET/MRI images. The advantage of this method is that it is completely automatic and offers a soft segmentation of tumours in PET images