MOSformer: Momentum encoder-based inter-slice fusion transformer for medical image segmentation
CoRR(2024)
摘要
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
These promising results indicate its competitiveness in medical image
segmentation. Codes and models of MOSformer will be made publicly available
upon acceptance.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要