Temperature guided network for 3D joint segmentation of the pancreas and tumors

Accurate and automatic segmentation of pancreatic tumors and organs from medical images is important for clinical diagnoses and making treatment plans for patients with pancreatic cancer. Although deep learning methods have been widely adopted for this task, the segmentation accuracy, especially for pancreatic tumors, still needs to be further improved because (1) phenotypic differences, such as volumes, tend to make the models focus on pancreatic learning, resulting in insufficient tumor feature selection; (2) deep learning models may fall into local optima, leading to unsatisfactory segmentation results for tumors and pancreas. To alleviate the above issues, in this paper, we propose a 3D fully convolutional neural network with three temperature guided modules, namely, balance temperature loss, rigid temperature optimizer and soft temperature indictor, to realize joint segmentation of the pancreas and tumors. Specifically, balance temperature loss is designed to dynamically adjust the learning points between tumors and the pancreas to balance the selected features, and it is aimed at improving the accuracy of tumor segmentation without losing pancreas information. Rigid temperature optimizer is proposed to accept nonimproving moves probabilistically to adaptively avoid local optima. To further refine the segmentation results, we propose the soft temperature indictor to guide the network into a fine-tuning state automatically when the model tends to stability. Our experimental results are more accurate than the fourteen top-ranking methods in pancreas and tumors segmentation on the MSD pancreas dataset and six top-ranking methods in brain tumors segmentation. Ablation studies verify the effectiveness of the three temperature guided modules.