Identifying biological landmarks using a novel cell measuring image analysis tool: Cell-o-Tape

We present a tool to count and measure individual neighbouring cells along a defined file in confocal laser scanning microscope images. The tool allows the user to extract this generic information in a flexible and intuitive manner, and builds on the raw data to detect a significant change in cell length along the file. This facility can be used, for example, to provide an estimate of the position of transition into the elongation zone of an Arabidopsis root, traditionally a location sensitive to the subjectivity of the experimenter.Cell-o-tape is shown to locate cell walls with a high degree of accuracy and estimate the location of the transition feature point in good agreement with human experts. The tool is an open source ImageJ/Fiji macro and is available online.Software capable of aiding the manual phenotyping of plants has become the subject of much interest in recent times [1]. There has been a push to develop high throughput imaging platforms [2-5] in an attempt to make use of digital-image-derived data for phenotyping, which has lagged behind the vast quantities of genomic data now available [6]. To cope with the resulting surge in raw data, new tools are being developed to automate image analysis processes and assist experts in performing traditionally labour-intensive tasks such as cell scale segmentation [7,8], root length measurement [2] and architecture description [9,10]. Additionally, software tools are opening up new ways to analyse data which is not practical manually, using techniques such as optic flow [11] to examine growth at the pixel-scale, and 3D segmentation of confocal time series datasets [12].Filling a gap in the capability of such existing software, with the aims of reducing labour and decreasing subjectivity provides the motivation behind developing Cell-o-Tape, an easy-to-use software tool which semi-automates the measurement and counting of cells, and can automatically estimate the location of a feature point traditionally estimated