Automated segmentation of cardiomyocyte Z-disks from high-throughput scanning electron microscopy data.

BACKGROUND: With the advent of new high-throughput electron microscopy techniques such as serial block-face scanning electron microscopy (SBF-SEM) and focused ion-beam scanning electron microscopy (FIB-SEM) biomedical scientists can study sub-cellular structural mechanisms of heart disease at high resolution and high volume. Among several key components that determine healthy contractile function in cardiomyocytes are Z-disks or Z-lines, which are located at the lateral borders of the sarcomere, the fundamental unit of striated muscle. Z-disks play the important role of anchoring contractile proteins within the cell that make the heartbeat. Changes to their organization can affect the force with which the cardiomyocyte contracts and may also affect signaling pathways that regulate cardiomyocyte health and function. Compared to other components in the cell, such as mitochondria, Z-disks appear as very thin linear structures in microscopy data with limited difference in contrast to the remaining components of the cell. METHODS: In this paper, we propose to generate a 3D model of Z-disks within single adult cardiac cells from an automated segmentation of a large serial-block-face scanning electron microscopy (SBF-SEM) dataset. The proposed fully automated segmentation scheme is comprised of three main modules including "pre-processing", "segmentation" and "refinement". We represent a simple, yet effective model to perform segmentation and refinement steps. Contrast stretching, and Gaussian kernels are used to pre-process the dataset, and well-known "Sobel operators" are used in the segmentation module. RESULTS: We have validated our model by comparing segmentation results with ground-truth annotated Z-disks in terms of pixel-wise accuracy. The results show that our model correctly detects Z-disks with 90.56% accuracy. We also compare and contrast the accuracy of the proposed algorithm in segmenting a FIB-SEM dataset against the accuracy of segmentations from a machine learning program called Ilastik and discuss the advantages and disadvantages that these two approaches have. CONCLUSIONS: Our validation results demonstrate the robustness and reliability of our algorithm and model both in terms of validation metrics and in terms of a comparison with a 3D visualisation of Z-disks obtained using immunofluorescence based confocal imaging

Use of Serial Block Face-Scanning Electron Microscopy to Study the Ultrastructure of Vertebrate and Invertebrate Biology

PhD ThesisThe development of Serial Block Face Scanning Electron Microscopy (SBF-SEM) allows for acquisition of serially sectioned, imaged data of ultrastructure at high resolution. In this project, optimisation of both SBF-SEM methodology and 3-D image segmentation analysis was applied to the ultrastructural examination of two types of biological tissues, each requiring a different experimental approach. The first project was a connectomic based study, to determine the relationship between the neurons that synapse upon the Lobula Giant Movement Detector 2 (LGMD 2) neuron, within the optic lobe of the locust. A substantial portion of the LGMD 2 neuron was reconstructed along with the afferent neurons, enabling the discovery of retinotopic mapping from the photoreceptors of the eye onto the LGMD 2 neuron. A sub-class of afferent neurons was also found, most likely vital in the process of signal integration across the large LGMD 2 neuron. For the second project, two types of skeletal muscle (psoas and soleus) obtained from fetal and adult guinea pigs were analysed to assess tissue-specific changes in mitochondrial morphology with muscle maturation. Distinct mitochondrial shapes were found across both muscles and age groups and a classification system was developed. It was found that, in both muscles, by late fetal gestation the mitochondrial network is well developed and akin to that found in the adult. Quantitative and qualitative differences in mitochondria morphology and complexity were found between the two muscles in the adult group. These differences are likely to be related to functional specialisation. All data collected during the experiments have also been made available online on Zenodo, roughly 240GB, which can be used for further studies. Overall SBF-SEM was proven to be a robust method of gaining new insights into the ultrastructure in both models and has wide ranging capabilities for a variety of experimental objectives