Iterative 3D feature enhancement network for pancreas segmentation from CT images
Juan Mo, Lei Zhang, Yan Wang & Haiying Huang
Automatic and accurate pancreas segmentation from 3D computed tomography volumes is a crucial prerequisite for computer-aided diagnosis, intraoperative planning and guidance. However, this is a challenging task because of the high inter-subject variability in the shape and location of the pancreas, as well as the existence of the surrounding organs. In order to address the above challenges, we propose a novel iterative 3D feature enhancement network to segment pancreas accurately. Specifically, the multi-level integrated features and the individual features at different levels can be progressively enhanced in an iterative manner by leveraging the complementary information encoded in different features. Therefore, the non-pancreas information at lower layers can be suppressed, and the fine details of pancreas at higher layers can be increased. In addition, because the pancreas region occupies only a small part of the scan, in order to prevent the final predictions from being biased toward the background class, we design the Dice similarity coefficients loss function in the training phase to mitigate this issue. Meanwhile, deep supervision with auxiliary classifier is incorporated in the intermediate layers at each iteration to guide the back-propagation of gradient flows and boost the discriminative capability at lower layers. Finally, in order to verify the effectiveness of the proposed method, we evaluated our approach on the publicly available NIH pancreas segmentation dataset. Extensive experiments illustrate that the proposed method achieves better performance than the state-of-the-art algorithms, and it can be easily applied to other volumetric image segmentation tasks.
Read Full Article Here: https://doi.org/10.1007/s00521-020-04710-3