MISSFormer: An Effective Medical Image Segmentation Transformer (2109.07162v2)

Abstract: The CNN-based methods have achieved impressive results in medical image segmentation, but they failed to capture the long-range dependencies due to the inherent locality of the convolution operation. Transformer-based methods are recently popular in vision tasks because of their capacity for long-range dependencies and promising performance. However, it lacks in modeling local context. In this paper, taking medical image segmentation as an example, we present MISSFormer, an effective and powerful Medical Image Segmentation tranSFormer. MISSFormer is a hierarchical encoder-decoder network with two appealing designs: 1) A feed-forward network is redesigned with the proposed Enhanced Transformer Block, which enhances the long-range dependencies and supplements the local context, making the feature more discriminative. 2) We proposed Enhanced Transformer Context Bridge, different from previous methods of modeling only global information, the proposed context bridge with the enhanced transformer block extracts the long-range dependencies and local context of multi-scale features generated by our hierarchical transformer encoder. Driven by these two designs, the MISSFormer shows a solid capacity to capture more discriminative dependencies and context in medical image segmentation. The experiments on multi-organ and cardiac segmentation tasks demonstrate the superiority, effectiveness and robustness of our MISSFormer, the experimental results of MISSFormer trained from scratch even outperform state-of-the-art methods pre-trained on ImageNet. The core designs can be generalized to other visual segmentation tasks. The code has been released on Github: https://github.com/ZhifangDeng/MISSFormer

Overview of MISSFormer: An Effective Medical Image Segmentation Transformer

The paper introduces MISSFormer, a novel transformer-based model specifically designed for medical image segmentation. The research addresses the inherent limitations of Convolutional Neural Network (CNN)-based methods in capturing long-range dependencies due to their localized receptive fields. The proposed model leverages the strengths of transformers in long-term dependency modeling while simultaneously addressing the shortfall in local context modeling, which is often a limitation of pure transformer architectures when applied to vision tasks.

Key Contributions

1. Enhanced Transformer Block: The MISSFormer architecture introduces a redesigned feed-forward network, named Enhanced Transformer Block, which bolsters feature representation by effectively capturing both long-range dependencies and local contextual information. This is achieved by embedding convolutions within the transformer architecture in a manner that aligns feature distribution, thereby ensuring more discriminative feature representations.
2. Enhanced Transformer Context Bridge: Unlike conventional methods that focus primarily on global context modeling, this paper proposes a context bridge that effectively integrates both local and global contexts. This bridge enables the extraction and utilization of multi-scale features, which is crucial for enhancing the segmentation capability.
3. Hierarchical Encoder-Decoder Architecture: The proposed model employs a U-shaped hierarchical structure, which includes an encoder for feature extraction and a decoder for pixel-wise segmentation. The architecture is noted for its capacity to enhance segmentation precision through the use of skip connections between corresponding encoder and decoder layers.

Experimental Validation

The experiments were conducted on the Synapse multi-organ segmentation dataset and the Automated Cardiac Diagnosis Challenge (ACDC) dataset. The results reveal that MISSFormer not only outperforms several state-of-the-art models, including those pretrained on large datasets like ImageNet, but also demonstrates significant robustness and effectiveness when trained from scratch.

1. On the Synapse dataset, MISSFormer achieved a Dice-Sørensen Coefficient (DSC) score that surpassed previous models, including the popular Transunet and Swin-Unet, by demonstrating superior segmentational accuracy across multiple organs.
2. On the ACDC dataset, MISSFormer again demonstrated superior performance with strong DSC scores across cardiac substructures.

Implications and Future Directions

The findings from this research not only showcase the potential of transformers in medical imaging tasks but also highlight the importance of marrying local and global context for improved feature discrimination. This could pave the way for more generalized applications in other visual segmentation tasks beyond the medical domain.

Future research might delve into further optimizing the integration of multi-scale contextual information and exploring more lightweight transformer configurations without compromising accuracy. Additionally, the Enhanced Transformer Blocks could be adapted and investigated for other computer vision applications where balanced feature discrimination across different scales is paramount.

In conclusion, MISSFormer represents a significant stride in the intersection of transformer architectures and medical image segmentation, offering a balanced approach to context modeling that addresses both local and global demands of the task.