A new label-free optical imaging method for the lymphatic system enhanced by deep learning.

Area of Science:

• Biomedical Optics
• Vascular Biology
• Medical Imaging

Background:

• Current understanding of the lymphatic system is limited by imaging technology constraints.
• Developing advanced imaging methods is crucial for studying lymphatic functions and diseases.
• High-contrast visualization is needed for accurate assessment of lymphatic structures and their role in pathology.

Approach:

• Developed a label-free orthogonal polarization imaging (OPI) technique operating in the shortwave infrared (SWIR) range.
• Enabled in vivo imaging of lymphatic vessels and lymph nodes in rodents through intact skin.
• Integrated SWIR-OPI with U-Net deep learning for automated lymph node size measurement.

Key Points:

• Successfully imaged mouse and rat lymphatic systems in vivo and human mesenteric lymph nodes from colectomy specimens.
• Monitored lymph node changes in response to cancer progression in mouse models, providing insights into pre-metastatic niches.
• Demonstrated real-time detection of human lymph nodes in clinical colectomy specimens, highlighting diagnostic potential.

Conclusions:

• SWIR-OPI offers a high-contrast, label-free method for visualizing the lymphatic vascular system.
• This technique facilitates real-time monitoring of tumor microenvironments and anatomical pathology gross examination.
• The combined approach provides valuable insights into cancer progression and enhances lymph node assessment in clinical settings.