One of the major challenges when imaging thick live samples is balancing image quality with tissue integrity. Low incident light doses over long periods of image acquisition can lead to low signal levels, causing low contrast images and difficulties with segmentation and analysis. The problem only gets worse when high dosage imaging or high temporal resolution imaging techniques are required to boost signal intensity. So often, the question is - do I image hard and fast, hoping for a one-take shot, or will it cause too much photobleaching of a sample or cell death?
However, new AI systems are making this challenge a thing of the past. Two new technologies – THUNDER and Aivia – can be used to return high-quality images and analysis of widefield images without compromising the longevity of samples. THUNDER uses computational methods to significantly increase image contrast during imaging with low light dosages. In combination, Aivia’s machine learning-based object detection segments and accurately analyzes these higher-contrast images to produce reliable reconstructions of highly complex samples.