Improving the slice interaction of 2.5D CNN for automatic pancreas segmentation.

PURPOSE: Volumetric pancreas segmentation can be used in the diagnosis of pancreatic diseases, the research about diabetes and surgical planning. Since manual delineation is time-consuming and laborious, we develop a deep learning-based framework for automatic pancreas segmentation in three dimensional (3D) medical images. METHODS: A two-stage framework is designed for automatic pancreas delineation. In the localization stage, a Square Root Dice loss is developed to handle the trade-off between sensitivity and specificity. In refinement stage, a novel 2.5D slice interaction network with slice correlation module is proposed to capture the non-local cross-slice information at multiple feature levels. Also a self-supervised learning-based pre-training method, slice shuffle, is designed to encourage the inter-slice communication. To further improve the accuracy and robustness, ensemble learning and a recurrent refinement process are adopted in the segmentation flow. RESULTS: The segmentation technique is validated in a public dataset (NIH Pancreas-CT) with 82 abdominal contrast-enhanced 3D CT scans. Fourfold cross-validation is performed to assess the capability and robustness of our method. The dice similarity coefficient, sensitivity, and specificity of our results are 86.21 ± 4.37%, 87.49 ± 6.38% and 85.11 ± 6.49% respectively, which is the state-of-the-art performance in this dataset. CONCLUSIONS: We proposed an automatic pancreas segmentation framework and validate in an open dataset. It is found that 2.5D network benefits from multi-level slice interaction and suitable self-supervised learning method for pre-training can boost the performance of neural network. This technique could provide new image findings for the routine diagnosis of pancreatic disease.