  The shapes and morphology of the organs and tissues are important prior
knowledge in medical imaging recognition and segmentation. The morphological
operation is a well-known method for morphological feature extraction. As the
morphological operation is performed well in hand-crafted image segmentation
techniques, it is also promising to design an approach to approximate
morphological operation in the convolutional networks. However, using the
traditional convolutional neural network as a black-box is usually hard to
specify the morphological operation action. Here, we introduced a 3D
morphological operation residual block to extract morphological features in
end-to-end deep learning models for semantic segmentation. This study proposed
a novel network block architecture that embedded the morphological operation as
an infinitely strong prior in the convolutional neural network. Several 3D deep
learning models with the proposed morphological operation block were built and
compared in different medical imaging segmentation tasks. Experimental results
showed the proposed network achieved a relatively higher performance in the
segmentation tasks comparing with the conventional approach. In conclusion, the
novel network block could be easily embedded in traditional networks and
efficiently reinforce the deep learning models for medical imaging
segmentation.
