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Text Embedded Swin-UMamba for DeepLesion Segmentation.

Ruida Cheng¹, Tejas Sudharshan Mathai², Pritam Mukherjee²

¹Scientific Application Services, Center of Information Technology, NIH.

|October 1, 2025

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This study integrates large language models (LLMs) with medical imaging for lesion segmentation. The new Text-Swin-UMamba model shows improved accuracy in segmenting lesions on CT scans.

Area of Science:

Medical imaging analysis
Artificial intelligence in healthcare
Radiology report interpretation

Background:

Lesion segmentation on CT scans is crucial for assessing chronic diseases like lymphoma.
Integrating large language models (LLMs) with imaging data can enhance segmentation by incorporating radiology report details.
Current segmentation methods may not fully leverage the combined potential of imaging and textual data.

Purpose of the Study:

To investigate the feasibility of integrating text descriptions into the Swin-UMamba architecture for improved lesion segmentation.
To develop and evaluate a novel Text-Swin-UMamba model for combining imaging features and radiology report information.
To assess the performance of the proposed model against existing lesion segmentation approaches.

Main Methods:

Keywords:

DeepLesion Universal Lesion Segmentation

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Utilized the ULS23 DeepLesion dataset for training and testing.
Integrated short-form descriptions from radiology reports into the Swin-UMamba architecture.
Developed the Text-Swin-UMamba model for lesion segmentation.
Compared the model's performance using Dice Score and Hausdorff distance metrics.

Main Results:

Achieved a high Dice Score of 82 ± 18% and a low Hausdorff distance of 6.58 ± 10.64 pixels for lesion segmentation.
The Text-Swin-UMamba model demonstrated a 37% improvement over the LLM-driven LanGuideMedSeg model (p < 0.001).
Outperformed purely image-based models, xLSTM-UNet by 1.74% and nnUNet by 0.22%.

Conclusions:

Integrating text information into the Swin-UMamba architecture is feasible and significantly improves lesion segmentation accuracy.
The Text-Swin-UMamba model represents a state-of-the-art approach for lesion segmentation, effectively combining imaging and textual data.
The developed model shows promise for enhancing clinical assessment of chronic diseases through more accurate automated lesion measurement.