Generalizable Cross-modality Medical Image Segmentation via Style Augmentation and Dual Normalization
The paper presented explores an innovative framework for tackling the challenging problem of generalizable cross-modality medical image segmentation. This task involves training segmentation models on images from one modality (e.g., MRI) and effectively applying them to another modality (e.g., CT) without re-training. The motivation arises from the substantial distribution shift between various imaging modalities, which significantly degrades the performance of standard segmentation models.
Methodology
The authors introduce a novel model that integrates both style augmentation and dual normalization techniques to enhance generalization capabilities. The primary contributions are as follows:
- Style Augmentation: The approach begins with augmenting the single source domain using B
ezier Curves to generate images with varying styles. These transformations aim to simulate possible appearance changes in unseen target domains. Specifically, the transformed images are separated into source-similar and source-dissimilar categories, based on their similarity to the original source images in terms of grayscale distribution.
- Dual-normalization: A dual-normalization module is proposed, where independent batch normalization layers are employed to process source-similar and source-dissimilar images separately. This design helps the model capture different domain-specific biases during training, thus maintaining crucial domain-specific information.
- Style-Based Path Selection: During inference, a novel style-based selection mechanism is used to choose the optimal normalization path for target domain images. This involves calculating the similarity between feature statistics of the target image and those stored from the source-similar and source-dissimilar domains.
Experimental Results
Extensive experiments demonstrate the efficacy of the proposed method. Evaluations were conducted on several datasets, including the BraTS, Cross-Modality Cardiac, and Abdominal Multi-Organ datasets. The results consistently showed that this dual-normalization method outperformed other state-of-the-art domain generalization techniques. For instance, on the BraTS dataset, the proposed method achieved a Dice coefficient of 54.44% on the T2 source domain, compared to 35.17% for the baseline DeepAll method.
Implications and Future Directions
This work highlights the potential for achieving robust medical image segmentation across different modalities without the need for accessing target domain data during training. The practical implications are significant, particularly in medical scenarios where cross-modality consistency is crucial, such as multi-modal imaging studies and healthcare settings where data privacy restricts direct access to images from different domains.
Theoretically, this framework can be expanded to explore more diverse transformations and domain representations. Future work might investigate more sophisticated style augmentation techniques or alternative normalization strategies. Additionally, exploring the application of this method to other types of domain shifts (e.g., cross-institution or cross-scanner) could provide broader insights into its generalization strengths and limitations.
In conclusion, the paper provides a substantial contribution to the domain of medical image analysis by addressing a tough problem with a practical solution, paving the way for more generalizable and robust segmentation models applicable across a wide array of modalities.