Improving the Slice Interaction of 2.5D CNN for Automatic Pancreas Segmentation.

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Abstract

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 3D medical images.
A two-stage framework is designed for automatic pancreas delineation. In 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.
The segmentation technique is validated in a public dataset (NIH Pancreas-CT) with 82 abdominal contrast enhanced 3D CT scans. Four-fold 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.
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.
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