Self-Supervised Rotation Learning for 3D Segmentation on Nasopharyngeal Carcinoma MRI Images

Segmenting the tumors of nasopharyngeal carcinoma (NPC) in Magnetic Resonance Imaging (MRI) images is critical for its diagnosis and treatment. In medical image segmentation, it is vital to exploit the rich information contained in 3D sliced images. However, the precise identification and location of lesion regions often require plenty of labels. To alleviate this situation, in this article, we propose 3DRotNPC, a framework based on a tailored Self-Supervised Learning (SSL) strategy, to accurately segment tumor regions for NPC under the circumstance of label limitation. To learn the rich 3D spatial and geometric nature of MRI images in a Self-Supervised way, the proxy task of randomly selecting and rotating images in consecutively sliced data is designed. After the SSL pre-training stage, the learned parameters of the Convolutional Neural Networks (CNNs) based model are transferred to adapt the downstream segmentation task. We verify the capability of 3DRotNPC on the NPC tumor dataset which is collected and curated from the clinical treatment in the representative hospital. Extensive experiments demonstrate that our approach delivers considerable gains in downstream 3D voxel segmentation, especially with a 10% to 50% number of labels.