Can Deep Learning Relax Endomicroscopy Hardware Miniaturization Requirements?
This work addresses the challenge of maintaining image quality in miniaturized medical imaging devices for clinical and research applications, though it is incremental as it applies an existing deep learning approach to a specific hardware limitation.
The paper tackles the problem of reduced image quality in miniaturized confocal laser endomicroscopy (CLE) hardware by using a densely connected convolutional neural network to reconstruct high-resolution images from low-resolution inputs, achieving superior results compared to traditional and learning-based methods on a dataset of 181 images.
Confocal laser endomicroscopy (CLE) is a novel imaging modality that provides in vivo histological cross-sections of examined tissue. Recently, attempts have been made to develop miniaturized in vivo imaging devices, specifically confocal laser microscopes, for both clinical and research applications. However, current implementations of miniature CLE components, such as confocal lenses, compromise image resolution, signal-to-noise ratio, or both, which negatively impacts the utility of in vivo imaging. In this work, we demonstrate that software-based techniques can be used to recover lost information due to endomicroscopy hardware miniaturization and reconstruct images of higher resolution. Particularly, a densely connected convolutional neural network is used to reconstruct a high-resolution CLE image from a low-resolution input. In the proposed network, each layer is directly connected to all subsequent layers, which results in an effective combination of low-level and high-level features and efficient information flow throughout the network. To train and evaluate our network, we use a dataset of 181 high-resolution CLE images. Both quantitative and qualitative results indicate superiority of the proposed network compared to traditional interpolation techniques and competing learning-based methods. This work demonstrates that software-based super-resolution is a viable approach to compensate for loss of resolution due to endoscopic hardware miniaturization.