Deep Probability Contour Framework for Tumour Segmentation and Dose Painting in PET Images

Abstract

The use of functional imaging such as PET in radiotherapy (RT) is rapidly expanding with new cancer treatment techniques. A fundamental step in RT planning is the accurate segmentation of tumours based on clinical diagnosis. Furthermore, recent tumour control techniques such as intensity modulated radiation therapy (IMRT) dose painting requires the accurate calculation of multiple nested contours of intensity values to optimise dose distribution across the tumour. Recently, convolutional neural networks (CNNs) have achieved tremendous success in image segmentation tasks, most of which present the output map at a pixel-wise level. However, its ability to accurately recognize precise object boundaries is limited by the loss of information in the successive downsampling layers. In addition, for the dose painting strategy, there is a need to develop image segmentation approaches that reproducibly and accurately identify the high recurrent-risk contours. To address these issues, we propose a novel hybrid-CNN that integrates a kernel smoothing-based probability contour approach (KsPC) to produce contour-based segmentation maps, which mimic expert behaviours and provide accurate probability contours designed to optimise dose painting/IMRT strategies. Instead of user-supplied tuning parameters, our final model, named KsPC-Net, applies a CNN backbone to automatically learn the parameters and leverages the advantage of KsPC to simultaneously identify object boundaries and provide probability contour accordingly. The proposed model demonstrated promising performance in comparison to state-of-the-art models on the MICCAI 2021 challenge dataset (HECKTOR).