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.

翻译：近期，视觉Transformer（ViT）的出现为三维数据集基准带来了重大进展，尤其在三维体素医学图像分割（Vol-MedSeg）领域。与此同时，多层感知器（MLP）网络因在排除高资源消耗的自注意力模块后仍能取得与ViT相当的结果，重新获得研究者青睐。本文提出一种名为PHNet的新型可置换混合网络用于Vol-MedSeg，该网络融合了卷积神经网络（CNN）与MLP的双重优势。PHNet通过结合2D和3D CNN提取局部特征，解决了三维体素数据固有的各向同性难题。此外，我们提出高效多层置换感知器（MLPP）模块，在保留位置信息的同时捕获长程依赖关系。该模块通过沿不同轴置换输入张量的轴分解操作实现位置信息的独立编码。进一步地，MLPP采用令牌分割操作解决Vol-MedSeg中MLP的分辨率敏感性问题，将特征分割为更小的令牌并分别处理。大量实验结果表明，在广泛使用且具挑战性的COVID-19-20与Synapse基准数据集上，PHNet以更低的计算成本超越现有最优方法。消融研究也证实了PHNet在融合CNN与MLP优势方面的有效性。