Deep Learning-based Tumour Delineation on Photon-counting CT Images

Treatment planning for stereotactic radiosurgery is based on manual delineation of tumour volumes and image registration of the CT and MRI patient scan, which can lead to uncertainties in the clinical workflow. These could be alleviated by implementing deep learning-based tumour contouring and a single modality imaging technique. This study investigates the performance of the deep learning models DeepMedic and nnU-Net for automatic tumour segmentation on two different data sets. In a first experiment, both models are trained on MRI data for vestibular schwannoma segmentation. A second experiment assesses the performance of a 2D nnU-Net model on a glioma data set including MRI and CT data. Additionally, the simulation framework GATE is used to simulate and reconstruct energy-integrating and photon-counting images of a simple brain phantom including a tumour volume. The 2D nnU-Net trained on the glioma CT data is applied to these images.All of the investigated networks achieve a high performance for vestibular schwannoma segmentation on MRI data. However, the performance achieved of the 2D nnU-Net model on the glioma data is low. A possible reason could be the heterogeneity of the data set. Investigation of this is part of ongoing work.The photon-counting image with a 50 keV threshold shows the best contrast to differentiate between tumour and brain tissue for our phantom. However, the 2D nnU-Net is not able to delineate the tumour on any of the reconstructed images which can be explained by the lack of higher-level brain features in the phantom.The results of this work show the need of incorporating a more realistic brain anatomy into the system and improvement of the network training and image reconstruction pipeline.