Segmentation of Organs at Risk in thoracic CT images using a SharpMask architecture and Conditional Random Fields

Cancer is one of the leading causes of death worldwide. Radiotherapy is a standard treatment for this condition and the first step of the radiotherapy process is to identify the target volumes to be targeted and the healthy organs at risk (OAR) to be protected. Unlike previous methods for automatic segmentation of OAR that typically use local information and individually segment each OAR, in this paper, we propose a deep learning framework for the joint segmentation of OAR in CT images of the thorax, specifically the heart, esophagus, trachea and the aorta. Making use of Fully Convolutional Networks (FCN), we present several extensions that improve the performance, including a new architecture that allows to use low level features with high level information, effectively combining local and global information for improving the localization accuracy. Finally, by using Conditional Random Fields (specifically the CRF as Recurrent Neural Network model), we are able to account for relationships between the organs to further improve the segmentation results. Experiments demonstrate competitive performance on a dataset of 30 CT scans.