  Medical image segmentation (MIS) aims to finely segment various organs. It
requires grasping global information from both parts and the entire image for
better segmenting, and clinically there are often certain requirements for
segmentation efficiency. Convolutional neural networks (CNNs) have made
considerable achievements in MIS. However, they are difficult to fully collect
global context information and their pooling layer may cause information loss.
Capsule networks, which combine the benefits of CNNs while taking into account
additional information such as relative location that CNNs do not, have lately
demonstrated some advantages in MIS. Vision Transformer (ViT) employs
transformers in visual tasks. Transformer based on attention mechanism has
excellent global inductive modeling capabilities and is expected to capture
longrange information. Moreover, there have been resent studies on making ViT
more lightweight to minimize model complexity and increase efficiency. In this
paper, we propose a U-shaped 3D encoder-decoder network named 3D-EffiViTCaps,
which combines 3D capsule blocks with 3D EfficientViT blocks for MIS. Our
encoder uses capsule blocks and EfficientViT blocks to jointly capture local
and global semantic information more effectively and efficiently with less
information loss, while the decoder employs CNN blocks and EfficientViT blocks
to catch ffner details for segmentation. We conduct experiments on various
datasets, including iSeg-2017, Hippocampus and Cardiac to verify the
performance and efficiency of 3D-EffiViTCaps, which performs better than
previous 3D CNN-based, 3D Capsule-based and 3D Transformer-based models. We
further implement a series of ablation experiments on the main blocks. Our code
is available at: https://github.com/HidNeuron/3D-EffiViTCaps.
