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Reflectance confocal microscopy (RCM) is a powerful tool to visualize the skin layers at cellular resolution. The dermal-epidermal junction (DEJ) is a thin complex 3D structure. It appears as a low-contrasted structure in confocal en-face sections, which is difficult to recognize visually, leading to uncertainty in the classification. In this article, we propose an automated method for segmenting...
With the advancement of high throughput and high resolution volumetric brain imaging, there is an unmet need to trace dense neuron fibers and study long-range neuron connectivity. An initial pipeline is described for processing cellular-level neuronal fiber data acquired by a new super resolution imaging method called Magnified Analysis of the Proteome (MAP). First, a multiscale vessel enhancement...
We propose a novel multiple-input multiple-output convolution neural network (MIMO-Net) for cell segmentation in fluorescence microscopy images. The proposed network trains the network parameters using multiple resolutions of the input image, connects the intermediate layers for better localization and context and generates the output using multi-resolution deconvolution filters. The MIMO-Net allows...
We present a new method for cell segmentation which combines a marked point process model with a combinatorics-based method of finding global optima. The method employs an energy term that assesses possible segmentations by their fidelity to both local image information and a simple model of cell interaction, and we use a randomized iterative reweighting technique for its minimization. Our approach...
We propose a novel label fusion technique as well as a crowdsourcing protocol to efficiently obtain accurate epithelial cell segmentations from non-expert crowd workers. Our label fusion technique simultaneously estimates the true segmentation, the performance levels of individual crowd workers, and an image segmentation model in the form of a pairwise Markov random field. We term our approach image-aware...
In developmental imaging, 3D+t series of microscopic images allow to follow the organism development at the cell level and have now became the standard way of imaging the development of living organs. Dedicated tools for cell segmentation in 3D images as well as cell lineage calculation from 3D+t sequences have been proposed to analyze these data. For some applications, it may be desirable to interpolate...
Fluorescence microscopy has emerged as a powerful tool for studying cell biology because it enables the acquisition of 3D image volumes deeper into tissue and the imaging of complex subcellular structures. Quantitative analysis of these structures, which is needed to characterize the structure and constitution of tissue volumes, is facilitated by nuclei segmentation. However, manual segmentation is...
Image segmentation is an important step in the quantitative analysis of fluorescence microscopy data. Since fluorescence microscopy volumes suffer from intensity inhomogeneity, low image contrast and limited depth resolution, poor edge details, and irregular structure shape, segmentation still remains a challenging problem. This paper describes a nuclei segmentation method for fluorescence microscopy...
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