TexLiverNet: Leveraging Medical Knowledge and Spatial-Frequency Perception for Enhanced Liver Tumor Segmentation
This work addresses the challenge of fine-grained liver tumor segmentation for medical diagnosis, though it appears incremental as it builds on existing multi-modal approaches with specific enhancements.
The paper tackled the problem of liver tumor segmentation by integrating lesion-specific text annotations with imaging, resulting in the TexLiverNet model that achieved superior performance compared to state-of-the-art methods on public and private datasets.
Integrating textual data with imaging in liver tumor segmentation is essential for enhancing diagnostic accuracy. However, current multi-modal medical datasets offer only general text annotations, lacking lesion-specific details critical for extracting nuanced features, especially for fine-grained segmentation of tumor boundaries and small lesions. To address these limitations, we developed datasets with lesion-specific text annotations for liver tumors and introduced the TexLiverNet model. TexLiverNet employs an agent-based cross-attention module that integrates text features efficiently with visual features, significantly reducing computational costs. Additionally, enhanced spatial and adaptive frequency domain perception is proposed to precisely delineate lesion boundaries, reduce background interference, and recover fine details in small lesions. Comprehensive evaluations on public and private datasets demonstrate that TexLiverNet achieves superior performance compared to current state-of-the-art methods.