An improved 3D region detection network: automated detection of the 12th thoracic vertebra in image guided radiation therapy

Abstract. Image guidance has been widely used in radiation therapy. Correctly identifying anatomical landmarks, like the 12th thoracic vertebra (T12), is the key to success. Until recently, the detection of those landmarks still requires tedious manual inspections and annotations; and superior-inferior misalignment to the wrong vertebral body is still relatively common in image guided radiation therapy. It is necessary to develop an automated approach to detect those landmarks from images. There are three major challenges to identify T12 vertebra automatically: 1) subtle difference in the structures with high similarity, 2) limited annotated training data, and 3) high memory usage of 3D networks. Abstract. In this study, we propose a novel 3D full convolutional network (FCN) that is trained to detect anatomical structures from 3D volumetric data, requiring only a small amount of training data. Comparing with existing approaches, the network architecture, target generation and loss functions were significantly improved to address the challenges specific to medical images. In our experiments, the proposed network, which was trained from a small amount of annotated images, demonstrated the capability of accurately detecting structures with high similarity. Furthermore, the trained network showed the capability of cross-modality learning. This is meaningful in the situation where image annotations in one modality are easier to obtain than others. The cross-modality learning ability also indicated that the learned features were robust to noise in different image modalities. In summary, our approach has a great potential to be integrated into the clinical workflow to improve the safety of image guided radiation therapy.