MiShape: 3D Shape Modelling of Mitochondria in Microscopy
This addresses a domain-specific problem in microscopy for biologists, offering a tool for realistic 3D modeling and simulation, but it is incremental as it builds on existing generative and implicit representation methods.
The paper tackles the problem of extracting 3D shapes of mitochondria from fluorescence microscopy images, which is challenging due to complex shapes and poor resolution, by proposing MiShape, a generative model that learns a shape prior from high-resolution electron microscopy data to enable 3D shape reconstruction from limited 2D or 3D inputs.
Fluorescence microscopy is a quintessential tool for observing cells and understanding the underlying mechanisms of life-sustaining processes of all living organisms. The problem of extracting 3D shape of mitochondria from fluorescence microscopy images remains unsolved due to the complex and varied shapes expressed by mitochondria and the poor resolving capacity of these microscopes. We propose an approach to bridge this gap by learning a shape prior for mitochondria termed as MiShape, by leveraging high-resolution electron microscopy data. MiShape is a generative model learned using implicit representations of mitochondrial shapes. It provides a shape distribution that can be used to generate infinite realistic mitochondrial shapes. We demonstrate the representation power of MiShape and its utility for 3D shape reconstruction given a single 2D fluorescence image or a small 3D stack of 2D slices. We also showcase applications of our method by deriving simulated fluorescence microscope datasets that have realistic 3D ground truths for the problem of 2D segmentation and microscope-to-microscope transformation.