["The Journal of Physiology, Volume 604, Issue 13, Page 5561-5584, 1 July 2026. ", "\nAbstract figure legend 3D cardiomyocyte segmentation enables comprehensive analyses of myocardial microstructure in health and disease; however, it is technically demanding. We present an open‐source toolkit for this task, which reduces challenges associated with sample preparation, image restoration, segmentation and proofreading. Alongside, we release a unique dataset containing 73 confocal microscopy image volumes with 3D cardiomyocyte annotations from seven species (including humans), acquired using different microscopes across several institutions. The datasets sample ventricular myocardium from control, post‐myocardial infarction, and ex vivo slice culture conditions, capturing a broad biological scope. On test data, our automatic workflow achieves an adapted Rand error of 0.063 ± 0.034, which can be quickly corrected using our proofreading tool, enabling high‐quality 3D reconstructions of cardiomyocyte microstructure. \n\n\n\n\n\n\n\n\n\nAbstract\nSegmentation of cardiomyocytes in microscopic 3D volumes is key to our understanding of cardiac (patho‐)physiology; however, it poses substantial experimental and analytical challenges. Therefore, researchers often resort to inferring 3D information from 2D segmentations, which can lead to biased and incorrect conclusions. Deep learning‐based methods are showing promise with respect to robustly segmenting objects in volumes acquired using various imaging modalities; yet, they have not been applied to high‐resolution 3D cardiomyocyte segmentations, and suitable open‐source tools and datasets are lacking. Here, we present a deep learning‐enabled toolkit for segmentation of individual cardiomyocytes in 3D confocal microscopy volumes. We include a dataset of 73 volumes with expert annotations, covering seven species, including mouse, human, and elephant, and containing samples generated under different experimental conditions, such as post‐myocardial infarction and ex vivo slice cultures. The toolkit additionally contains an image restoration workflow to address imaging‐related artefacts, such as spatially varying blur. Our automatic cardiomyocyte segmentation workflow achieved an adapted Rand error of 0.063 ± 0.034 (∼94% voxel‐pair agreement) on the test set. Our semi‐automatic workflow reached a throughput of 3 cells min−1 on a challenging, previously unseen dataset. The toolkit and data are open‐source and accessible through a dedicated graphical user interface. In summary, we provide an accessible toolkit enabling researchers to extract quantitative data on cardiomyocyte microstructure from 3D confocal image stacks of cardiac tissue. Given the size and diversity of our dataset, we expect our methods to perform well across species and experimental conditions, facilitating high‐quality 3D reconstructions of large numbers of individual cardiomyocytes.\n\n\n\n\n\n\n\n\n\nKey points\n\n3D cardiomyocyte microstructure is a key determinant of cardiac function in health and disease. However, reliable extraction and quantification of 3D cardiomyocyte cytoarchitecture pose significant experimental and computational challenges.\nWe present an effective experimental protocol and a deep learning‐enabled toolkit for sample preparation and 3D analysis of cardiomyocyte morphology in ventricular myocardium.\nOur method is validated across seven species (mouse to human) and in samples prepared in diverse experimental conditions from a range of models, including myocardial infarction and ex vivo tissue culture, highlighting the robustness and versatility of our workflow.\nOur open‐source dataset and toolkit enable large‐scale analyses and extraction of realistic 3D geometries of ventricular microstructure. These can be used to explore a host of research questions and provide a new resource for modelling cardiac function at the cellular level.\n\n\n"]