Automatic segmentation of cell nuclei is an essential step in image cytometry and histometry. Despite substantial progress, there is a need to improve accuracy, speed, level of automation, and adaptability to new applications. This paper presents a robust and accurate novel method for segmenting cell nuclei using a combination of ideas. The image foreground is extracted automatically using a graph-cuts-based binarization. Next, nuclear seed points are detected by a novel method combining multiscale Laplacian-of-Gaussian filtering constrained by distance-map-based adaptive scale selection. These points are used to perform an initial segmentation that is refined using a second graph-cuts-based algorithm incorporating the method of alpha expansions and graph coloring to reduce computational complexity. Nuclear segmentation results were manually validated over 25 representative images (15 in vitro images and 10 in vivo images, containing more than 7400 nuclei) drawn from diverse cancer histopathology studies, and four types of segmentation errors were investigated. The overall accuracy of the proposed segmentation algorithm exceeded 86%. The accuracy was found to exceed 94% when only over- and undersegmentation errors were considered. The confounding image characteristics that led to most detection/segmentation errors were high cell density, high degree of clustering, poor image contrast and noisy background, damaged/irregular nuclei, and poor edge information. We present an efficient semiautomated approach to editing automated segmentation results that requires two mouse clicks per operation.

Long-term integration of neuroprosthetic devices is challenged by reactive responses that compromise the brain-device interface. The contribution of physical insertion parameters to immediate damage is not well described. We have developed an ex vivo preparation to capture real-time images of tissue deformation during device insertion using thick tissue slices from rat brains prepared with fluorescently labeled vasculature. Qualitative and quantitative assessments of damage were made for insertions using devices with different tip shapes inserted at different speeds. Direct damage to the vasculature included severing, rupturing and dragging, and was often observed several hundred micrometers from the insertion site. Slower insertions generally resulted in more vascular damage. Cortical surface features greatly affected insertion success; insertions attempted through pial blood vessels resulted in severe tissue compression. Automated image analysis techniques were developed to quantify tissue deformation and calculate mean effective strain. Quantitative measures demonstrated that, within the range of experimental conditions studied, faster insertion of sharp devices resulted in lower mean effective strain. Variability within each insertion condition indicates that multiple biological factors may influence insertion success. Multiple biological factors may contribute to tissue distortion, thus a wide variability was observed among insertions made under the same conditions.

No abstract

Brain structural complexity has confounded prior efforts to extract quantitative image-based measurements. We present a systematic 'divide and conquer' methodology for analyzing threedimensional (3D) multi-parameter images of brain tissue to delineate and classify key structures, and compute quantitative associations among them. To demonstrate the method, thick (~100 μm) slices of rat brain tissue were labeled using 3 -5 fluorescent signals, and imaged using spectral confocal microscopy and unmixing algorithms. Automated 3D segmentation and tracing algorithms were used to delineate cell nuclei, vasculature, and cell processes. From these segmentations, a set of 23 intrinsic and 8 associative image-based measurements was computed for each cell. These features were used to classify astrocytes, microglia, neurons, and endothelial cells. Associations among cells and between cells and vasculature were computed and represented as graphical networks to enable further analysis. The automated results were validated using a graphical interface that permits investigator inspection and corrective editing of each cell in 3D. Nuclear counting accuracy was >89%, and cell classification accuracy ranged from 81-92% depending on cell type. We present a software system named FARSIGHT implementing our methodology. Its output is a detailed XML file containing measurements that may be used for diverse quantitative hypothesis-driven and exploratory studies of the central nervous system.

BackgroundAutomatic and reliable characterization of cells in cell cultures is key to several applications such as cancer research and drug discovery. Given the recent advances in light microscopy and the need for accurate and high-throughput analysis of cells, automated algorithms have been developed for segmenting and analyzing the cells in microscopy images. Nevertheless, accurate, generic and robust whole-cell segmentation is still a persisting need to precisely quantify its morphological properties, phenotypes and sub-cellular dynamics.ResultsWe present a single-channel whole cell segmentation algorithm. We use markers that stain the whole cell, but with less staining in the nucleus, and without using a separate nuclear stain. We show the utility of our approach in microscopy images of cell cultures in a wide variety of conditions. Our algorithm uses a deep learning approach to learn and predict locations of the cells and their nuclei, and combines that with thresholding and watershed-based segmentation. We trained and validated our approach using different sets of images, containing cells stained with various markers and imaged at different magnifications. Our approach achieved a 86% similarity to ground truth segmentation when identifying and separating cells.ConclusionsThe proposed algorithm is able to automatically segment cells from single channel images using a variety of markers and magnifications.Electronic supplementary materialThe online version of this article (10.1186/s12859-018-2375-z) contains supplementary material, which is available to authorized users.