  Medical image segmentation has been so far achieving promising results with
Convolutional Neural Networks (CNNs). However, it is arguable that in
traditional CNNs, its pooling layer tends to discard important information such
as positions. Moreover, CNNs are sensitive to rotation and affine
transformation. Capsule network is a data-efficient network design proposed to
overcome such limitations by replacing pooling layers with dynamic routing and
convolutional strides, which aims to preserve the part-whole relationships.
Capsule network has shown a great performance in image recognition and natural
language processing, but applications for medical image segmentation,
particularly volumetric image segmentation, has been limited. In this work, we
propose 3D-UCaps, a 3D voxel-based Capsule network for medical volumetric image
segmentation. We build the concept of capsules into a CNN by designing a
network with two pathways: the first pathway is encoded by 3D Capsule blocks,
whereas the second pathway is decoded by 3D CNNs blocks. 3D-UCaps, therefore
inherits the merits from both Capsule network to preserve the spatial
relationship and CNNs to learn visual representation. We conducted experiments
on various datasets to demonstrate the robustness of 3D-UCaps including
iSeg-2017, LUNA16, Hippocampus, and Cardiac, where our method outperforms
previous Capsule networks and 3D-Unets.

Image and Video Processing;Computer Vision and Pattern Recognition;Machine Learning