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 labelled 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 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. From start to finish, FAMOUS provides full segmentation results within a week on previously unseen datasets. FAMOUS was showcased on a dataset acquired using a focused ion beam scanning electron microscope (FIBSEM), and on yeast cells acquired by transmission electron microscopy.