Triplet-constraint Transformer with Multi-scale Refinement for Dose
  Prediction in Radiotherapy
Lu Wen Qihun Zhang Zhenghao Feng Yuanyuan Xu Xiao Chen Jiliu Zhou Yan Wang
Radiotherapy is a primary treatment for cancers with the aim of applying
sufficient radiation dose to the planning target volume (PTV) while minimizing
dose hazards to the organs at risk (OARs). Convolutional neural networks (CNNs)
have automated the radiotherapy plan-making by predicting the dose maps.
However, current CNN-based methods ignore the remarkable dose difference in the
dose map, i.e., high dose value in the interior PTV while low value in the
exterior PTV, leading to a suboptimal prediction. In this paper, we propose a
triplet-constraint transformer (TCtrans) with multi-scale refinement to predict
the high-quality dose distribution. Concretely, a novel PTV-guided triplet
constraint is designed to refine dose feature representations in the interior
and exterior PTV by utilizing the explicit geometry of PTV. Furthermore, we
introduce a multi-scale refinement (MSR) module to effectively fulfill the
triplet constraint in different decoding layers with multiple scales. Besides,
a transformer encoder is devised to learn the important global dosimetric
knowledge. Experiments on a clinical cervical cancer dataset demonstrate the
superiority of our method.