Advances in tissue engineering for cardiac regenerative medicine require cellular-level understanding of the mechanism of cardiac muscle growth during embryonic developmental stage. Computational methods to automatize cell segmentation in 3D and deliver accurate, quantitative morphology of cardiomyocytes, are imperative to provide insight into cell behavior underlying cardiac tissue growth. Detecting individual cells from volumetric images of dense tissue, poised with low signal-to-noise ratio and severe intensity in homogeneity, is a challenging task. In this article, we develop a robust segmentation tool capable of extracting cellular morphological parameters from 3D multifluorescence images of murine heart, captured via light-sheet microscopy. The proposed pipeline incorporates a neural network for 2D detection of nuclei and cell membranes. A graph-based global association employs the 2D nuclei detections to reconstruct 3D nuclei. A novel optimization embedding the network flow algorithm in an alternating direction method of multipliers is proposed to solve the global object association problem. The associated 3D nuclei serve as the initialization of an active mesh model to obtain the 3D segmentation of individual myocardial cells. The efficiency of our method over the state-of-the-art methods is observed via various qualitative and quantitative evaluation.

Biological imaging tools continue to increase in speed, scale, and resolution, often resulting in the collection of gigabytes or even terabytes of data in a single experiment. In comparison, the ability of research laboratories to store and manage this data is lagging greatly. This leads to limits on the collection of valuable data and slows data analysis and research progress. Here we review common ways researchers store data and outline the drawbacks and benefits of each method. We also offer a blueprint and budget estimation for a currently deployed data server used to store large datasets from zebrafish brain activity experiments using light-sheet microscopy. Data storage strategy should be carefully considered and different options compared when designing imaging experiments.

Two methods are especially suited for tomographic imaging with histological detail of macroscopic samples that consist of multiple tissue types (bone, muscle, nerve or fat): Light sheet (based) fluorescence microscopy (LSFM) and micro-computed tomography (micro-CT). Micro-CT requires staining with heavy chemical elements (and thus fixation and sometimes dehydration) in order to make soft tissue imageable when measured alongside denser structures. LSMF requires fixation, decalcification, dehydration, clearing and staining with a fluorescent dye. The specimen preparation of both imaging methods is prone to shrinkage, which is often not mentioned, let alone quantified. In this paper the presence and degree of shrinkage are quantitatively identified for the selected preparation methods/stains. LSFM delivers a volume shrinkage of 17% for bone, 56% for muscle and 62% for brain tissue. The three most popular micro-CT stains (phosphotungstic acid, iodine with potassium iodide, and iodine in absolute ethanol) deliver a volume shrinkage ranging from 10 to 56% for muscle and 27–66% for brain, while bone does not shrink in micro-CT preparation.