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.

Selective object segmentation is a difficult, but often required image analysis task which has numerous applications in various fields such as in biology, satellite imaging or video surveillance. We present a family of active contours designed to segment objects with predefined size and simple shape characteristics using the ratio of area and perimeter, and the second central moment of the contour. These priors allow targeted selection of objects by their size and shape with certain flexibility. A novel data model is introduced based on local regions of the contour points. The proposed methodology is tested on synthetic and fluorescent microscopic images containing a mixture of genetically modified bacterial populations. The method was capable of selecting objects by their size and shape.

Illumination-robust optical flow algorithms are needed in numerous machine vision applications such as vision-based intelligent vehicles, surveillance and traffic monitoring. Recently, we have proposed an implicit nonlinear scheme for variational optical flow that assumes no particular analytical form of energy functional and can accommodate various image components and data metrics. Using test data with brightness and colour illumination changes, we study different features and metrics and demonstrate that cross-correlation is superior to the L1metric for all combinations of the features.

We present an optical flow calculation method based on the variational approach. The method uses an implicit non-linear numerical scheme that makes no assumption on the analytical form of the optical constraints and combines local and global smoothness criteria in a natural way. Defining the optical constraints with normalised cross-correlation, we obtain a technique that is robust against illumination changes in colour and greyscale images.