DSA-Former: A Network of Hybrid Variable Structures for Liver and Liver Tumour Segmentation

Abstract

Background

Accurately annotated CT images of liver tumours can effectively assist doctors in diagnosing and treating liver cancer. However, due to the relatively low density of the liver, its tumours, and surrounding tissues, as well as the existence of multi-scale problems, accurate automatic segmentation still faces challenges.

Methods

We propose a segmentation network DSA-Former that combines convolutional kernels and attention. By combining the morphological and edge features of liver tumour images, capture global/local features and key inter-layer information, and integrate attention mechanisms obtaining detailed information to improve segmentation accuracy.

Results

Compared to other methods, our approach demonstrates significant advantages in evaluation metrics such as the Dice coefficient, IOU, VOE, and HD95. Specifically, we achieve Dice coefficients of 96.8% for liver segmentation and 72.2% for liver tumour segmentation.

Conclusion

Our method offers enhanced precision in segmenting liver and liver tumour images, laying a robust foundation for liver cancer diagnosis and treatment.