Prior Attention Enhanced Convolutional Neural Network Based Automatic Segmentation of Organs at Risk for Head and Neck Cancer Radiotherapy

Aimed to automate the segmentation of organs at risk (OARs) in head and neck (H&N) cancer radiotherapy, we develop a novel Prior Attention enhanced convolutional neural Network (PANet) based Stepwise Refinement Segmentation Framework (SRSF) on full-size computed tomography (CT) images. The SRSF is built with a multiscale segmentation concept, in which OARs are segmented from coarse to fine. PANet is a pyramidal architecture with elements of inception block and prior attention. In this study, the developed PANet based SRSF is applied for OARs segmentation in H&N radiotherapy. 139 CT series and manually delineated contours of twenty-two OARs by experienced oncologists are collected from 139 H&N patients for training and evaluating the proposed PANet based SRSF. The mean testing Dice similarity coefficients (DSC) on 39 CT series range from 76.1± 8.3% (left middle ear) to 91.9± 1.4% (right mandible) for large volume OARs(mean volume >1cc) while the corresponding ranges are 63.4± 12.3%(chiasm) to 81.0± 14.1% (right lens) for small and challenging OARs(mean volume ≤1cc). Furthermore, the proposed method also achieved superior segmentations over reference methods on the MICCAI 2015 H&N dataset with mean DSC of 95.6± 0.7%, 81.3± 4.0%, 77.6± 4.5%, 77.5± 4.6%, and 69.2± 7.6%, on the mandible, left submandibular, left and right optical nerve, and chiasm, respectively. The accurate segmentation of OARs is obtained on both the self-collected testing data and public testing dataset, which implies that the proposed method can be used as a practicable and efficient tool for automated OARs contouring in the H&N cancer radiotherapy.