Leveraging Multi-axis Frequency Domain Representation for Improved Medical Image Segmentation

Temel Kavramlar

Proposing a novel Multi-axis External Weights (MEW) block that effectively captures comprehensive global and local information in the frequency domain to enhance medical image segmentation performance.

Özet

The paper proposes a novel Multi-axis External Weights (MEW) block that leverages the frequency domain information to improve medical image segmentation. The key highlights are:

The MEW block performs 2D Discrete Fourier Transform (DFT) along three different axes (Height-Width, Channel-Width, and Channel-Height) of the input feature map to obtain global frequency domain information. Learnable external weights are then applied to the frequency domain features before inverse DFT is performed to obtain the output.

In addition to the multi-axis frequency domain processing, a depthwise separable convolution is also applied to capture local information. The final output is obtained by concatenating the features from the four branches.

The MEW block is integrated into a U-shaped architecture, replacing the self-attention module in Vision Transformers, resulting in the proposed MEW-UNet model.

Extensive experiments on four medical image segmentation datasets (Synapse, ACDC, ISIC17, ISIC18) demonstrate that the proposed MEW-UNet outperforms state-of-the-art methods, highlighting the effectiveness of the multi-axis frequency domain representation.

Ablation studies confirm the importance of the multi-axis frequency domain processing and the learnable external weights generator in achieving the superior performance.

İstatistikler

The paper reports the following key metrics:

On the ISIC17 dataset, the proposed MEW-UNet achieves 81.38% mIoU and 89.73% DSC.
On the ISIC18 dataset, the proposed MEW-UNet achieves 81.90% mIoU and 90.05% DSC.
On the Synapse dataset, the proposed MEW-UNet achieves 78.92% DSC and 16.44mm HD95.
On the ACDC dataset, the proposed MEW-UNet achieves 91.00% DSC and 1.19mm HD95.

Alıntılar

"Frequency domain technology has improved these problems."
"By considering the frequency domain signal strength of the three axes together, it becomes evident that there is no signal strength intersection among the three curves."
"Motivated by this observation, we propose to extract and fuse features using a multi-axis approach."

Önemli Bilgiler Şuradan Elde Edildi

Learning Multi-axis Representation in Frequency Domain for Medical Image Segmentation

by Jiacheng Rua... : arxiv.org 09-25-2024

https://arxiv.org/pdf/2312.17030.pdf

Daha Derin Sorular

How can the proposed multi-axis frequency domain representation be extended to other medical imaging tasks beyond segmentation, such as classification or registration?

The proposed multi-axis frequency domain representation can be effectively extended to other medical imaging tasks, including classification and registration, by leveraging its ability to capture comprehensive global and local features. For classification tasks, the multi-axis approach can enhance feature extraction by applying the same frequency domain transformations to the feature maps before classification layers. This would allow the model to learn more discriminative features that are invariant to spatial transformations, improving classification accuracy across various medical imaging modalities.
In the context of registration, the multi-axis frequency domain representation can be utilized to align images from different modalities or time points. By transforming both images into the frequency domain, the model can identify and minimize discrepancies in frequency components, which often correspond to structural differences in the images. This approach can be particularly beneficial in scenarios where traditional spatial domain methods struggle due to noise or variations in image quality. Future work could explore the integration of multi-axis frequency domain features with existing registration algorithms, enhancing their robustness and accuracy.

What are the potential limitations of the frequency domain approach, and how can they be addressed in future work?

Despite its advantages, the frequency domain approach has potential limitations that need to be addressed in future work. One significant limitation is the computational complexity associated with performing Fourier transforms, especially for high-resolution medical images. This can lead to increased processing time and resource consumption, which may not be feasible in real-time clinical settings. To mitigate this, future research could focus on optimizing the Fourier transform operations, possibly through the use of fast algorithms or approximations that maintain accuracy while reducing computational load.
Another limitation is the potential loss of spatial information during the transformation process. While the frequency domain can highlight certain features, it may obscure others that are critical for accurate interpretation. To address this, hybrid models that combine frequency domain representations with spatial domain features could be developed. This would allow the model to leverage the strengths of both domains, ensuring that important spatial details are preserved while still benefiting from the global context provided by the frequency domain.

Given the importance of local information captured by the depthwise separable convolution, how can the interaction between global frequency domain features and local spatial features be further improved?

To enhance the interaction between global frequency domain features and local spatial features, several strategies can be employed. One approach is to implement attention mechanisms that specifically focus on the relationships between frequency domain representations and local spatial features. By incorporating attention layers that weigh the importance of different features based on their relevance to the task at hand, the model can dynamically adjust its focus, ensuring that both global and local information are effectively integrated.
Additionally, multi-scale feature fusion techniques can be utilized to combine features extracted from different resolutions. This would allow the model to capture both fine-grained local details and broader contextual information simultaneously. For instance, features from the depthwise separable convolution can be concatenated with frequency domain features at various stages of the network, enabling richer representations that enhance segmentation or classification performance.
Finally, exploring the use of residual connections between frequency domain and spatial domain branches can facilitate better information flow. By allowing gradients to propagate more effectively between these branches, the model can learn to balance the contributions of both feature types, leading to improved overall performance in medical imaging tasks.