Medical image segmentation takes an important position in various clinical
applications. Deep learning has emerged as the predominant solution for
automated segmentation of volumetric medical images. 2.5D-based segmentation
models bridge computational efficiency of 2D-based models and spatial
perception capabilities of 3D-based models. However, prevailing 2.5D-based
models often treat each slice equally, failing to effectively learn and exploit
inter-slice information, resulting in suboptimal segmentation performances. In
this paper, a novel Momentum encoder-based inter-slice fusion transformer
(MOSformer) is proposed to overcome this issue by leveraging inter-slice
information at multi-scale feature maps extracted by different encoders.
Specifically, dual encoders are employed to enhance feature distinguishability
among different slices. One of the encoders is moving-averaged to maintain the
consistency of slice representations. Moreover, an IF-Swin transformer module
is developed to fuse inter-slice multi-scale features. The MOSformer is
evaluated on three benchmark datasets (Synapse, ACDC, and AMOS), establishing a
new state-of-the-art with 85.63%, 92.19%, and 85.43% of DSC, respectively.
These promising results indicate its competitiveness in medical image
segmentation. Codes and models of MOSformer will be made publicly available
upon acceptance.

提出了一种基于动量编码器的切片间融合变压器（MOSformer），通过利用不同编码器提取的多尺度特征图中的切片间信息，克服了普遍的 2.5D 模型在处理每个切片时等价视为，未能有效学习和利用切片间信息，从而导致分割性能不佳。该方法在三个基准数据集上进行了评估，分别建立了新的最先进水平，达到 85.63％，92.19％和 85.43％的 DSC。这些有希望的结果表明其在医学图像分割方面的竞争力。MOSformer 的代码和模型将在接受后公开发布。

MOSformer：基于动量编码的切片融合变形器用于医学图像分割

MOSformer: Momentum encoder-based inter-slice fusion transformer for  medical image segmentation

The nature of thick-slice scanning causes severe inter-slice discontinuities
of 3D medical images, and the vanilla 2D/3D convolutional neural networks
(CNNs) fail to represent sparse inter-slice information and dense intra-slice
information in a balanced way, leading to severe underfitting to inter-slice
features (for vanilla 2D CNNs) and overfitting to noise from long-range slices
(for vanilla 3D CNNs). In this work, a novel mesh network (MNet) is proposed to
balance the spatial representation inter axes via learning. 1) Our MNet
latently fuses plenty of representation processes by embedding
multi-dimensional convolutions deeply into basic modules, making the selections
of representation processes flexible, thus balancing representation for sparse
inter-slice information and dense intra-slice information adaptively. 2) Our
MNet latently fuses multi-dimensional features inside each basic module,
simultaneously taking the advantages of 2D (high segmentation accuracy of the
easily recognized regions in 2D view) and 3D (high smoothness of 3D organ
contour) representations, thus obtaining more accurate modeling for target
regions. Comprehensive experiments are performed on four public datasets
(CT\&MR), the results consistently demonstrate the proposed MNet outperforms
the other methods. The code and datasets are available at:
this https URL

该论文提出了一种新的 Mesh 网络（MNet）用于平衡 2D / 3D 卷积神经网络中稀疏的层间信息和密集的层内信息，并在四个公共数据集上进行了综合实验，结果显示 MNet 具有优越性能。