DeepCristae, a CNN for the restoration of mitochondria cristae in live microscopy images

• DOI: 10.1038/s42003-025-07684-x
• Publication Date: 2025-02-26T23:31:08Z
• Authors: Salomé Papereux; Ludovic Leconte; Cesar Augusto Val...; Tianyan Liu; Julien Dumont; Zhixing Chen; Jean Salamero; Charles Kervrann; Anaïs Badoual
• Keywords: mitochondria cristae; 0303 health sciences; QH301-705.5; [SDV]Life Sciences [q-bio]; live imaging; super resolution; [INFO] Computer Science [cs]; image restoration; fluorescence microscopy; [SDV] Life Sciences [q-bio]; 03 medical and health sciences; [INFO]Computer Science [cs]; Biology (General); Convolution neural network

Abstract Mitochondria play an essential role in the life cycle of eukaryotic cells. However, we still don’t know how their ultrastructure, like the cristae of the inner membrane, dynamically evolves to regulate these fundamental functions, in response to external conditions or during interaction with other cell components. Although high-resolution fluorescent microscopy coupled with recently developed innovative probes can reveal this structural organization, their long-term, fast and live 3D imaging remains challenging. To address this problem, we have developed a CNN, called DeepCristae, to restore mitochondria cristae in low spatial resolution microscopy images. Our network is trained from 2D STED images using a novel loss specifically designed for cristae restoration. To efficiently increase the size of the training set, we also developed a random image patch sampling centered on mitochondrial areas. To evaluate DeepCristae, quantitative assessments are carried out using metrics we derived by focusing on the mitochondria and cristae pixels rather than on the whole image as usual. Depending on the conditions of use indicated, DeepCristae works well on broad microscopy modalities (Stimulated Emission Depletion (STED), Live-SR, AiryScan and LLSM). It is ultimately applied in the context of mitochondrial network dynamics during interaction with endo/lysosome membranes.