3D END-TO-END BOUNDARY-AWARE NETWORKS FOR PANCREAS SEGMENTATION

Accurate pancreas segmentation is crucial for computer aided pancreas diagnosis and surgery. It still remains challenging to precisely extract the pancreas due to its small size, unclear boundary, and shape variations on CT images. This work proposes a new 3D end-to-end boundary-aware network architecture for automatic accurate pancreas segmentation from CT images. Specifically, this architecture introduces four hybrid blocks for feature extraction in accordance with 3D fully convolutional neural networks so that it can successfully extract and perceive spatial and contextual information from 3D CT data. Simultaneously, a reverse attention block and a boundary enhancement block are embedded into this architecture to enhance the ability to learn and extract feature maps with more context and boundary information. We evaluate our proposed method on publicly available pancreas data using 4-fold cross-validation, with the experimental results showing that our network model can obtain more accurate or comparable segmentation than other existing methods.