Nebojša Nešić, Xavier Heiligenstein, Lydia Zopf, Valentin Blüml, Katharina S Keuenhof, Michael Wagner, Johanna L Höög, Heng Qi, Zhiyang Li, Georgios Tsaramirsis, Christopher J Peddie, Miloš Stojmenović, Andreas Walter
Recent advances in computing power triggered the use of artificial intelligence in image analysis in life sciences. To train these algorithms, a large enough set of certified labeled data is required. The trained neural network is then capable of producing accurate instance segmentation results that will then need to be re-assembled into the original dataset: the entire process requires substantial expertise and time to achieve quantifiable results. To speed-up the process, from cell organelle detection to quantification across electron microscopy modalities, we propose a deep-learning based approach for fast automatic outline segmentation (FAMOUS), that involves organelle detection combined with image morphology, and 3D meshing to automatically segment, visualize and quantify cell organelles within volume electron microscopy datasets...
March 19, 2024: Microscopy Research and Technique