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Stagger Network: Rethinking information loss in medical image segmentation with various-sized targets
Tianyi Liu1, Zhaorui Tan2, Haochuan Jiang3
1School of Robotics, XJTLU Entrepreneur College (Taicang), Xi'an Jiaotong-Liverpool University, 111 Taicang Road, Taicang, Suzhou, 215123, Jiangsu, China; Department of Computer Science, University of Liverpool, Brownlow Hill, Liverpool, L697ZX, United Kingdom.
This study introduces the Stagger Network (SNet) for medical image segmentation, effectively handling various target sizes by fusing Convolutional Neural Network (CNN) and Vision Transformer (ViT) features. SNet minimizes information loss, outperforming other methods on multiple datasets.
Area of Science:
- Medical image analysis
- Computer vision
- Deep learning for medical imaging
Background:
- Medical image segmentation requires models to capture both local and global information, especially for targets of varying sizes.
- Current CNN and ViT approaches struggle to balance multi-scale target detection, often leading to significant information loss due to divergent feature distributions.
Purpose of the Study:
- To introduce a novel Stagger Network (SNet) designed to mitigate information loss in medical image segmentation.
- To develop a fusion structure that effectively balances local and global feature extraction from CNNs and ViTs for improved multi-scale segmentation.
Main Methods:
- Proposed a novel Stagger Network (SNet) incorporating a Parallel Module to bridge semantic gaps between CNN and ViT features.
- Introduced a Stagger Module for fusing semantically similar features and an Information Recovery Module to recapture complementary information.
- Theoretically analyzed the parallel and stagger strategies to demonstrate reduced information loss.
Main Results:
- The proposed SNet demonstrated superior performance in segmenting various-sized targets on the Synapse dataset compared to state-of-the-art methods.
- SNet also showed superiority on the ACDC and MoNuSeg datasets, which feature targets with more consistent dimensions.
Conclusions:
- The SNet effectively addresses the challenge of multi-scale medical image segmentation by intelligently fusing CNN and ViT features.
- The proposed fusion strategies significantly reduce information loss, leading to enhanced segmentation accuracy across datasets with diverse target size variations.
