Recently, the advent of vision Transformer (ViT) has brought substantial
advancements in 3D dataset benchmarks, particularly in 3D volumetric medical
image segmentation (Vol-MedSeg). Concurrently, multi-layer perceptron (MLP)
network has regained popularity among researchers due to their comparable
results to ViT, albeit with the exclusion of the resource-intensive
self-attention module. In this work, we propose a novel permutable hybrid
network for Vol-MedSeg, named PHNet, which capitalizes on the strengths of both
convolution neural networks (CNNs) and MLP. PHNet addresses the intrinsic
isotropy problem of 3D volumetric data by employing a combination of 2D and 3D
CNNs to extract local features. Besides, we propose an efficient multi-layer
permute perceptron (MLPP) module that captures long-range dependence while
preserving positional information. This is achieved through an axis
decomposition operation that permutes the input tensor along different axes,
thereby enabling the separate encoding of the positional information.
Furthermore, MLPP tackles the resolution sensitivity issue of MLP in Vol-MedSeg
with a token segmentation operation, which divides the feature into smaller
tokens and processes them individually. Extensive experimental results validate
that PHNet outperforms the state-of-the-art methods with lower computational
costs on the widely-used yet challenging COVID-19-20 and Synapse benchmarks.
The ablation study also demonstrates the effectiveness of PHNet in harnessing
the strengths of both CNNs and MLP