Voltage imaging is an emerging microscopy technique that can make neuroscientific research very prominent. The images obtained with this imaging method exhibit a substantial amount of noise.
Currently, the new methods are developed and tested to computationally denoise voltage imaging videos with high efficiency and preservation of the video structure.
This research attempted to investigate how well traditional denoising algorithms, such as different types of blur or diffusion, perform in denoising such videos. Specifically, five well-established algorithms commonly used in biomedical imaging were evaluated: Gaussian filter, bilateral filter, anisotropic diffusion, wavelet filter, and total variation minimization.
The methods were applied to both real brain recordings (HPC2 dataset) and synthetic videos (Broad DSP CellMincer).
Performance was assessed by measuring the structural similarity index (SSIM) and signal-to-noise ratio (SNR).
Results suggest a trade-off between noise removal and structural preservation, with total variation minimization and anisotropic filtering performing particularly well in terms of noise suppression. These classical methods remain relevant for data exploration and visualization.
For the methods to be used in a medical context, a more in-depth research should be carried out on medical data. The deep learning methods remain relevant for the high-precision applications. Code developed for the research is available online at https://github.com/Rpplctns/denoising-voltage-imaging-videos.git.

Cardiovascular diseases (CVDs) are a group of disorders of the heart and blood vessels.
CVDs are the leading cause of death worldwide. To diagnose and treat CVDs, clinicians and cardiologists use multiple noninvasive imaging techniques. These scans are used to segment certain structures of the heart. Deep learning-based cardiac segmentation on short-axis cardiac magnetic resonance images (CMRI) has gained popularity over the past few years because of its generalisability and accuracy. This has exponentially reduced contouring times for clinicians. The development of such deep learning techniques has seen a common trend. In order to accommodate learning for larger cardiac datasets, the depth and size of segmentation networks have been increased. Unfortunately, the environmental impact of exploding such networks is not taken into account. One solution to mitigate having computationally expensive networks is to incorporate anatomical knowledge in the form of shape priors. The Gridnet and UNet with a shape prior are computationally efficient networks that are used to evaluate segmentation performance on a large and varied cardiac dataset (Combination of the Automated Cardiac Diagnosis Challenge - ACDC and Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Segmentation challenge - M&M datasets). On average, these networks segment CMRIs with an average dice score of 0.87 and a Hausdorff distance of 11.7mm. In parallel, one of the major issues in cardiac technology is the under-representation of women in cardiac datasets. Purposefully curated cardiac datasets such as ACDC and M&M try and maintain equal representation. In real-world scenarios, this might not always be the case. Clinical trials to collect such data often report female representation as low as 25%. Evaluation of segmentation performance between a balanced and skewed dataset is conducted. This is to address if bias in such cardiac training datasets affects the performance of segmentation networks between male and female test patients.