Abstract
Advances in electron microscopy (EM) such as electron tomography and focused ion-beam scanning electron microscopy provide unprecedented, three-dimensional views of cardiac ultrastructures within sample volumes ranging from hundreds of nanometres to hundreds of micrometres. The datasets from these samples are typically large, with file sizes ranging from gigabytes to terabytes and the number of image slices within the three-dimensional stack in the hundreds. A significant bottleneck with these large datasets is the time taken to extract and statistically analyse three-dimensional changes in cardiac ultrastructures. This is because of the inherently low contrast and the significant amount of structural detail that is present in EM images. These datasets often require manual annotation, which needs substantial person-hours and may result in only partial segmentation that makes quantitative analysis of the three-dimensional volumes infeasible. We present CardioVinci, a deep learning workflow to automatically segment and statistically quantify the morphologies and spatial assembly of mitochondria, myofibrils and Z-discs with minimal manual annotation. The workflow encodes a probabilistic model of the three-dimensional cardiomyocyte using a generative adversarial network. This generative model can be used to create new models of cardiomyocyte architecture that reflect variations in morphologies and cell architecture found in EM datasets. This article is part of the theme issue 'The cardiomyocyte: new revelations on the interplay between architecture and function in growth, health, and disease'.