Semantic hyperspectral image synthesis for cross-modality knowledge transfer in surgical data science.

Area of Science:

• Medical Imaging
• Computer Vision
• Machine Learning

Background:

• Hyperspectral imaging (HSI) offers significant potential for intraoperative applications like tissue classification and cancer detection.
• A major limitation in surgical HSI is the scarcity of comprehensive datasets, impeding the development of data-driven algorithms.
• Knowledge transfer across different optical imaging modalities is crucial for advancing HSI applications.

Purpose of the Study:

• To address the critical bottleneck of limited surgical HSI datasets.
• To develop a novel approach for knowledge transfer across imaging modalities using generative modeling.
• To enable the creation of realistic HSI data from other imaging sources.

Main Methods:

• Proposed a latent diffusion model (LDM) for converting semantic segmentation masks into realistic hyperspectral images.
• Leveraged generative modeling to facilitate knowledge transfer across optical imaging modalities.
• Assessed the approach's effectiveness using surgical scene segmentation as a downstream task.

Main Results:

• LDMs demonstrated suitability for synthesizing high-resolution, realistic HSI, even with limited training data or cross-modality annotations.
• The generative augmentation approach improved semantic HSI segmentation performance, achieving up to a 35% increase in Dice similarity coefficient.
• The method proved effective with diverse annotations, including geometric out-of-distribution data.

Conclusions:

• The proposed LDM-based method effectively augments HSI datasets in a modality-agnostic manner.
• This approach provides a blueprint for overcoming data limitations for novel imaging modalities.
• It facilitates the development of more robust data-driven algorithms in surgical HSI.