Dataset and trained models for video denoising in fluorescence guided surgery

Fluorescence guided surgery (FGS) is a promising surgical technique that gives surgeons a unique view of tissue that is used to guide their practice by delineating tissue types and diseased areas. As new fluorescent contrast agents are developed that have low fluorescent photon yields, it becomes increasingly important to develop computational models to allow FGS systems to maintain good video quality in real time environments. To further complicate this task, FGS has a difficult bias noise term from laser leakage light (LLL) that represents unfiltered excitation light that can be on the order of the fluorescent signal. This dataset contains the data used to develop and train video denoising models for fluroescence guided surgery with LLL. This dataset contains bright fluorescence data in a mock chicken thigh surgery for FGS video simulation, non-fluorescent video for LLL simulation, as well as a number of calibration datasets for properly simulating a comercial system, and real noise video for testing. We also provide result videos of our denoising models trained with this data and the trained models.

Methods
This dataset contains fluorescence and reference (RGB) video of a mock chicken thigh surgery captured on the OnLume Avata commercial fluroescence guided surgery (FGS) system using indocyanine green (ICG) as the contrast agent. It contains a number of different conditions used to calibrate a realistic noise model and simulate real noisy data for the training of deep learning based video denoising models. 

First, this dataset contains a number of videos captured with high concentration of ICG that can act as a basis for simualtion and as a ground truth in training. This portion of the dataset is an expansion of the dataset in [1], and contains the videos from [1] along with another ~100 minutes of video.
Second, our dataset contains 15 minutes of real noisy data of a mock chicken thigh surgery with low concentration of ICG.
Third, our dataset contains 20 minutes of mock chicken thigh surgery with no ICG that can be used to simualte laser leakage light.
Finally, our dataset contains a number of videos used to calibrate our noise model to properly simualte the OnLume Avata system noise.

[1] Seets, Trevor et al. (2024). Data for OFDVDnet: A sensor fusion approach for video denoising in fluorescence guided surgery [Dataset]. Dryad. https://doi.org/10.5061/dryad.v6wwpzh3w