"Esophageal Gross Tumor Volume Segmentation using a 3D Convolutional Neural Network"
Sahar Yousefi, Hessam Sokooti, Mohamed S. Elmahdy, Femke P. Peters, Mohammad T. Manzuri Shalmani, Roel T. Zinkstok and Marius Staring
Accurate gross tumor volume (GTV) segmentation in esophagus CT images is a critical task in computer aided diagnosis (CAD) systems. However, because of the difficulties raised by the contrast similarity between esophageal GTV and its neighbouring tissues in CT scans, this problem has been addressed weakly. In this paper we present a 3D end-to-end method based on a convolutional neural network (CNN) for this purpose. We leverages design elements from DenseNet in a typical U-shape. The proposed architecture consists of a contractile path and an extending path that includes dense blocks for extracting contextual features and retrieves the lost resolution respectively. Using dense blocks leads to deep supervision, feature re-usability, and parameter reduction while aiding the network to be more accurate. The proposed architecture was trained and tested on a dataset containing 553 scans from 49 distinct patients. The proposed network achieved a Dice value of 0:73 ± 0:20, and a 95% mean surface distance of 3:07 ± 1:86 mm for 85 test scans. The experimental results indicates the effectiveness of the proposed method for clinical diagnosis and treatment systems.