This dataset contains individual 3D mitochondria extracted from 3D electron microscopy images in different 3D shape representation formats of meshes, point clouds and implicit shapes. The dataset also includes synthetic microscope images of these shapes.

With more deep learning solutions being developed for fluorescence microscopy image analysis, there is increasing demand for annotated ground truth datasets for training supervised methods. However, obtaining these annotations is a laborious and expensive endeavor. To address this problem for microscope analysis of cell organelles, we release 3DOSL , a database of 3D shapes of mitochondria. 3DOSL utilizes high-resolution Electron Microscopy data as the source for creating the extensive database. Utilizing a physics-based simulator, 3DOSL allows the creation of large fluorescence microscope image datasets with 3D ground truths that can be used to train deep earning models for 3D shape reconstruction, microscope-microscope style transfer, 2D and 3D segmentation etc. We demonstrate this using a variety of example application in this paper. 3DOSL contains more than 27K instances of diverse mitochondria shapes in different 3D shape representation formats of , meshes, point clouds and implicit shapes.