Multiphase difference enhanced fusion for tumor and pancreas segmentation

Multi-phase contrast-enhanced computed tomography (CECT) has been shown to effectively refine the segmentation accuracy for both tumors and organs. Nevertheless, enhancing the segmentation accuracy of the pancreas and pancreatic tumors using multi-phase CECT remains a challenge, which is crucial for subsequent clinical analysis and treatment. Current methods either simply concatenate multi-phase features along the channel direction or utilize attention mechanisms to perform weighted summation of features across phases, neglecting the contribution of phase-specific difference features to segmentation accuracy. In view of this, this paper proposes a multi-phase segmentation method for the pancreas and pancreatic tumors, improving segmentation accuracy by enhancing the phase-specific difference features. Additionally, we propose a training strategy to improve the segmentation accuracy of model when only single-phase CECT is available. The process achieved by distilling the learned knowledge of a segmentation model trained on multi-phase CECT. The proposed method and training strategy proved effective through experiments conducted on a privately collected multiphase CECT dataset of pancreatic tumors.