Superpixel-based segmentation of muscle fibers in multi-channel microscopy

Background Confetti fluorescence and other multi-color genetic labelling strategies are useful for observing stem cell regeneration and for other problems of cell lineage tracing. One difficulty of such strategies is segmenting the cell boundaries, which is a very different problem from segmenting color images from the real world. This paper addresses the difficulties and presents a superpixel-based framework for segmentation of regenerated muscle fibers in mice. Results We propose to integrate an edge detector into a superpixel algorithm and customize the method for multi-channel images. The enhanced superpixel method outperforms the original and another advanced superpixel algorithm in terms of both boundary recall and under-segmentation error. Our framework was applied to cross-section and lateral section images of regenerated muscle fibers from confetti-fluorescent mice. Compared with “ground-truth” segmentations, our framework yielded median Dice similarity coefficients of 0.92 and higher. Conclusion Our segmentation framework is flexible and provides very good segmentations of multi-color muscle fibers. We anticipate our methods will be useful for segmenting a variety of tissues in confetti fluorecent mice and in mice with similar multi-color labels.National University of Singapore (Duke-NUS SRP Phase 2 Research Block Grant)Singapore. National Research Foundation (CREATE programme)Singapore-MIT Alliance for Research and Technology (SMART

Region Adjacency Graph Approach for Acral Melanocytic Lesion Segmentation

Malignant melanoma is among the fastest increasing malignancies in many countries. Due to its propensity to metastasize and lack of effective therapies for most patients with advanced disease, early detection of melanoma is a clinical imperative. In non-Caucasian populations, melanomas are frequently located in acral volar areas and their dermoscopic appearance differs from the non-acral ones. Although lesion segmentation is a natural preliminary step towards its further analysis, so far virtually no acral skin lesion segmentation method has been proposed. Our goal was to develop an effective segmentation algorithm dedicated for acral lesions

Epithelium and Stroma Identification in Histopathological Images using Unsupervised and Semi-supervised Superpixel-based Segmentation

We present superpixel-based segmentation frameworks for unsupervised and semi-supervised epithelium-stroma identification in histopathological images or oropharyngeal tissue micro arrays. A superpixel segmentation algorithm is initially used to split-up the image into binary regions (superpixels) and their colour features are extracted and fed into several base clustering algorithms with various parameter initializations. Two Consensus Clustering (CC) formulations are then used: the Evidence Accumulation Clustering (EAC) and the voting-based consensus function. These combine the base clustering outcomes to obtain a more robust detection of tissue compartments than the base clustering methods on their own. For the voting-based function, a technique is introduced to generate consistent labellings across the base clustering results. The obtained CC result is then utilized to build a self-training Semi-Supervised Classification (SSC) model. Unlike supervised segmentations, which rely on large number of labelled training images, our SSC approach performs a quality segmentation while relying on few labelled samples. Experiments conducted on forty-five hand-annotated images of oropharyngeal cancer tissue microarrays show that (a) the CC algorithm generates more accurate and stable results than individual clustering algorithms; (b) the clustering performance of the voting-based function outperforms the existing EAC; and (c) the proposed SSC algorithm outperforms the supervised methods, which is trained with only a few labelled instances

Automatic Segmentation of Cells of Different Types in Fluorescence Microscopy Images

Recognition of different cell compartments, types of cells, and their interactions is a critical aspect of quantitative cell biology. This provides a valuable insight for understanding cellular and subcellular interactions and mechanisms of biological processes, such as cancer cell dissemination, organ development and wound healing. Quantitative analysis of cell images is also the mainstay of numerous clinical diagnostic and grading procedures, for example in cancer, immunological, infectious, heart and lung disease. Computer automation of cellular biological samples quantification requires segmenting different cellular and sub-cellular structures in microscopy images. However, automating this problem has proven to be non-trivial, and requires solving multi-class image segmentation tasks that are challenging owing to the high similarity of objects from different classes and irregularly shaped structures. This thesis focuses on the development and application of probabilistic graphical models to multi-class cell segmentation. Graphical models can improve the segmentation accuracy by their ability to exploit prior knowledge and model inter-class dependencies. Directed acyclic graphs, such as trees have been widely used to model top-down statistical dependencies as a prior for improved image segmentation. However, using trees, a few inter-class constraints can be captured. To overcome this limitation, polytree graphical models are proposed in this thesis that capture label proximity relations more naturally compared to tree-based approaches. Polytrees can effectively impose the prior knowledge on the inclusion of different classes by capturing both same-level and across-level dependencies. A novel recursive mechanism based on two-pass message passing is developed to efficiently calculate closed form posteriors of graph nodes on polytrees. Furthermore, since an accurate and sufficiently large ground truth is not always available for training segmentation algorithms, a weakly supervised framework is developed to employ polytrees for multi-class segmentation that reduces the need for training with the aid of modeling the prior knowledge during segmentation. Generating a hierarchical graph for the superpixels in the image, labels of nodes are inferred through a novel efficient message-passing algorithm and the model parameters are optimized with Expectation Maximization (EM). Results of evaluation on the segmentation of simulated data and multiple publicly available fluorescence microscopy datasets indicate the outperformance of the proposed method compared to state-of-the-art. The proposed method has also been assessed in predicting the possible segmentation error and has been shown to outperform trees. This can pave the way to calculate uncertainty measures on the resulting segmentation and guide subsequent segmentation refinement, which can be useful in the development of an interactive segmentation framework

Whole-body integration of gene expression and single-cell morphology

Animal bodies are composed of cell types with unique expression programs that implement their distinct locations, shapes, structures, and functions. Based on these properties, cell types assemble into specific tissues and organs. To systematically explore the link between cell-type-specific gene expression and morphology, we registered an expression atlas to a whole-body electron microscopy volume of the nereid Platynereis dumerilii. Automated segmentation of cells and nuclei identifies major cell classes and establishes a link between gene activation, chromatin topography, and nuclear size. Clustering of segmented cells according to gene expression reveals spatially coherent tissues. In the brain, genetically defined groups of neurons match ganglionic nuclei with coherent projections. Besides interneurons, we uncover sensory-neurosecretory cells in the nereid mushroom bodies, which thus qualify as sensory organs. They furthermore resemble the vertebrate telencephalon by molecular anatomy. We provide an integrated browser as a Fiji plugin for remote exploration of all available multimodal datasets

Augmenting Wiring Diagrams of Neural Circuits with Activity in Larval Drosophila

Neural circuit models explain an animal's behavior as evoked activity of different circuit elements of its nervous system. Synaptic wiring diagrams mapped from structural imaging of nervous systems guide modeling of neural circuits on the basis of connectivity. However, connectivity alone may not sufficiently constrain the set of plausible circuit hypotheses for empirical study. Combining structural imaging of synaptic connectivity with functional information from activity imaging can further constrain these hypotheses to create unequivocal neural circuit models. This thesis develops computational methods and tools to cross-reference structural and activity imaging of explant larval Drosophila central nervous systems at cellular resolution. Augmenting synaptic wiring diagrams with activity maps via these methods relates circuit structure and function at the neuronal level on a per-behavior basis. Neuronal activity of larval central nervous systems expressing pan-neuronal calcium indicators is imaged in a light sheet microscope, which are then structurally imaged with high throughput electron microscopy. Methods and tools are provided for the assembly of these image volumes, spatial registration between imaging modalities, automated detection of relevant tissue and cellular structures in each, extraction of activity time series, and morphological identification of neurons in structural imaging using reference wiring diagrams mapped from other larvae. Using these methods, existing wiring diagrams mapped from a reference first instar larva were identified with neurons in a larva augmented with activity information for a neural circuit involved in peristaltic motor control. This demonstrates the feasibility of the contributed methods to associate the wiring diagrams of arbitrary circuits of interest with activity timeseries across multiple individuals, behaviors, and behavioral bouts. To demonstrate capability to augment wiring diagrams with information besides activity, these methods are also applied to multiple larvae each expressing specific neurotransmitter labels rather than calcium indicators in the light sheet microscopy. This work scaffolds future modeling of circuits underlying behavior that can only be mechanistically understood in the context of multi-modal observation of synaptic connectivity, functional activity and molecular markers. The methods developed also enable comparative connectomics between multiple individuals, which is necessary to study inter-individual variability in circuits and to observe experimental intervention in the development, structure, and function of neural circuits.Howard Hughes Medical Institute Janelia Research Campu

Measurement model of brass plated tyre steel cord based on wave feature extraction

In the production of Truck and Bus Radial (TBR) vehicle tyres, one of the essential components is the wire that supports the tyre. There are several types of tyre wire, one of which is Brass Plated Tyre Steel Cord (BPTSC), produced by Bekaert Indonesia Company. BPTSC object has a micro-size with a diameter of 0.230 mm and has a wave shape. In checking the quality of steel straps, brass-coated tyres are usually measured manually by experienced experts by measuring instruments to measure the diameter using a micrometre, wave amount, and wavelength using a profile projector. The manual measurement process results in inaccuracy due to fatigue in employees' eyes and low lighting and must be repeated, thus, consuming more time. Technological developments that use computer vision are increasingly widespread. Moreover, from the results of studies in various literature, it is proposed to combine the models obtained to find new models to solve this problem. The objectives of this study were to implement and evaluate an automatic segmentation method for obtaining regions of interest, to propose a BPTSC diameter, wave amount, and wavelength measurement model based on its edge, and to evaluate the proposed model by comparing the results with standard and industrial measurement results. The technique to prepare the brass plated tyre steel cord was done in two ways: image acquisition techniques with enhanced image quality, noise removal, and edge detection. Secondly, ground truth techniques were utilised to find the truth about the stages of the image acquisition process. Finally, sensitivity testing was conducted to find the similarity between the acquired images and the ground truth data using Jaccard, Dice, and Cosine similarity method. From 148 wire samples, the average similarity value was 93% by Jaccard, 96% by Dice, and 91% by the Cosine method. Thus, it can be concluded that the acquisition stage of the brass-coated steel tyre cable with image processing techniques can be carried out. For the subsequent process, the pixel distance and the sliding windows model applied can correctly detect the diameter of the BPTSC properly. The wave amount and wavelength of BPTSC objects in the form of waves were measured using several local minima and maxima approaches. This included maxima of local minima maxima distance, the average of local minima maxima distance, and perpendicular shape to centre distance for measuring wave amounts. While for wavelength measurements, the midpoint of local maxima minima distance and the intersection of local maxima minima with a central line were used. Measurement results were evaluated to determine the accuracy and efficiency of the measurement process compared to standard production values using the accuracy, precision, recall, and Root Mean Square Error (RMSE) test. From the evaluation results of the two methods, the accuracy rate of diameter measurement is 97%, wave rate measurement is 95%, and wavelength measurement is 90%. A new model was formed from the evaluation results that could solve these problems and provide scientific and beneficial contributions to society in general and the companies related to this industry

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