MOTIVATION: Microscopy images of stained cells and tissues play a central role in most biomedical experiments and routine histopathology. Storing colour histological images digitally opens the possibility to process numerically colour distribution and intensity to extract quantitative data. Among those numerical procedures are colour deconvolution, which enable decomposing an RGB image into channels representing the optical absorbance and transmittance of the dyes when their RGB representation is known. Consequently, a range of new applications become possible for morphological and histochemical segmentation, automated marker localization and image enhancement. AVAILABILITY AND IMPLEMENTATION: Colour deconvolution is presented here in two open-source forms: a MATLAB program/function and an ImageJ plugin written in Java. Both versions run in Windows, Macintosh and UNIX-based systems under the respective platforms. Source code and further documentation are available at: https://blog.bham.ac.uk/intellimic/g-landini-software/colour-deconvolution-2/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SUMMARY: Segmentation of single cells in microscopy images is one of the major challenges in computational biology. It is the first step of most bioimage analysis tasks, and essential to create training sets for more advanced deep learning approaches. Here, we propose 3D-Cell-Annotator to solve this task using 3D active surfaces together with shape descriptors as prior information in a semi-automated fashion. The software uses the convenient 3D interface of the widely used Medical Imaging Interaction Toolkit (MITK). Results on 3D biological structures (e.g. spheroids, organoids and embryos) show that the precision of the segmentation reaches the level of a human expert. AVAILABILITY AND IMPLEMENTATION: 3D-Cell-Annotator is implemented in CUDA/C++ as a patch for the segmentation module of MITK. The 3D-Cell-Annotator enabled MITK distribution can be downloaded at: www.3D-cell-annotator.org. It works under Windows 64-bit systems and recent Linux distributions even on a consumer level laptop with a CUDA-enabled video card using recent NVIDIA drivers. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Time-lapse experiments play a key role in studying the dynamic behavior of cells. Single-cell tracking is one of the fundamental tools for such analyses. The vast majority of the recently introduced cell tracking methods are limited to fluorescently labeled cells. An equally important limitation is that most software cannot be effectively used by biologists without reasonable expertise in image processing. Here we present CellTracker, a user-friendly open-source software tool for tracking cells imaged with various imaging modalities, including fluorescent, phase contrast and differential interference contrast (DIC) techniques. AVAILABILITY AND IMPLEMENTATION: CellTracker is written in MATLAB (The MathWorks, Inc., USA). It works with Windows, Macintosh and UNIX-based systems. Source code and graphical user interface (GUI) are freely available at: http://celltracker.website/ CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Digital imaging is nowadays widely employed in the field of optical microscopy. One of the most apparent benefits consists in the possibility for the researcher to see the whole biological sample in one image, achieved by collecting all the parts being inspected. Common approaches work in batch mode and rely on known motorized x-y stage offsets of the microscope holder. Or alternatively, the methods are conceived just to provide visually pleasant mosaics off-line, that are often built by altering the photometric values or the geometric properties of the original component images. This work presents an incremental mosaicing method for optical microscopy imagery, compliant with on-line requirements and suitable even for non-motorized microscopes. The resulting mosaics are very accurate and preserve the consistency of the original images so to be used for further global measurement steps. Nevertheless, the mosaics are visually pleasant so to be used for visual inspection as well. The experimental results obtained in different biological examinations confirm the efficacy of our approach.

Automated microscopic image analysis techniques are increasingly gaining attention in the field of biological imaging. The success of these applications mostly depends on the earlier image processing steps applied to the acquired images, aiming at enhancing image content while performing noise and artifacts removal. One such artifact is the vignetting effect that in general occurs in most imaging sensors due to an uneven illumination of the scene being imaged. As a consequence, images are usually lighter near the optical center and darker at image borders. This effect is particularly evident when stitching images into a mosaic in order to increase the field of view of the microscope. The existing approaches deal with either the parametric model of the known light distribution or the estimation of the illumination field based on just one image or a sequence of empty-field images. These approaches are only feasible when the acquisition apparatus is at one's disposal. We propose a non parametric and general purpose approach, without using prior information about the light distribution, where the illumination field is estimated from the background, that is built automatically stemming from a sequence of images containing even the objects of interest.