Journal: Computational and Structural Biotechnology Journal

Article Title: CometAnalyser : A user-friendly, open-source deep-learning microscopy tool for quantitative comet assay analysis

doi: 10.1016/j.csbj.2022.07.053

Figure Lengend Snippet: Tools freely available - characteristics (X = available/yes; O = not available/no, last access: 30/07/2022).

Article Snippet: CellProfiler , : CellProfiler is a popular MATLAB / Python software suite worldwide used to set several microscopy image-based analyses by aligning in a pipeline customised modules for standard image processing tasks.

Techniques: Fluorescence, Selection

Journal: Computational and Structural Biotechnology Journal

Article Title: CometAnalyser : A user-friendly, open-source deep-learning microscopy tool for quantitative comet assay analysis

doi: 10.1016/j.csbj.2022.07.053

Figure Lengend Snippet: Jaccard Index values (the better value for each row reported in Italics).

Article Snippet: CellProfiler , : CellProfiler is a popular MATLAB / Python software suite worldwide used to set several microscopy image-based analyses by aligning in a pipeline customised modules for standard image processing tasks.

Techniques:

Journal: Computational and Structural Biotechnology Journal

Article Title: CometAnalyser : A user-friendly, open-source deep-learning microscopy tool for quantitative comet assay analysis

doi: 10.1016/j.csbj.2022.07.053

Figure Lengend Snippet: Segmentations. Ground truth (yellow outline), CometAnalyser ’s masks (green outline), CellProfiler ’s masks (red outline). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Article Snippet: CellProfiler , : CellProfiler is a popular MATLAB / Python software suite worldwide used to set several microscopy image-based analyses by aligning in a pipeline customised modules for standard image processing tasks.

Techniques:

Journal: Developmental biology

Article Title: ShapeMetrics: A userfriendly pipeline for 3D cell segmentation and spatial tissue analysis

doi: 10.1016/j.ydbio.2020.02.003

Figure Lengend Snippet: (A) Part 1: Ilastik machine learning. The original z-stack image of epithelial ureteric bud in developing kidney (.tif) used as an input file. Pixel annotations are made by manual labeling of the membrane (red) and the background (green). Prediction overlay based on the labels that have been used for the training, colors indicate the predicted pixel values: red = 0, green = 1. Uncertain pixels are marked with cyan to indicate the pixel value 0.5. Prediction map is the final file obtained after full training; the file consists of a matrix array of pixel values between 0 and 1, with one value corresponding to each pixel in the original input image. Part 2: Segmentation in MATLAB. Prediction map serves as an input file for the MATLAB segmentation script. Tresholding the prediction map selects all the pixels with a value greater than 0.95. Substraction sets the size threshold for the pixel clusters. This allows getting rid of the white background (that survived from the pixel value threshold) and only the cells are left. The (watershed) segmentation is done by using the thresholded prediction map together with the original z-stack image as the seed for the watershed segmentation algorithm. 3D rendering of the final watershed label matrix of segmented cells. The histogram shows the distributions of the cell volumes. Part 3: Extraction of spatial parameters in MATLAB. The heatmap visualizes the hierarchical clustering of spatial parameter values calculated individually for each cell in the segmented label matrix. Each row corresponds to one parameter whereas each column corresponds to each individual cell. Red color points to high representation of a certain parameter and blue to low, respectively. Spatial localization is based on the heat map clusters where each clustered group of cells is mapped back to their spatial location and visualized by pseudo-coloring the cells using the same, respective color-code. (B) A strong and specific immunostaining is a requirement for successful cell segmentation as shown in single plane images of our example tissues, which are from the top: ureteric bud in E13.5 mouse kidney, embryonic HH9 chicken neural tube, and a human neuroepithelial spheroid. Scale bar 40 μm. (C) Presentation of the volumetric parameters used in the ShapeMetrics script. Number of cells: Number of cells is calculated from the final segmented label matrix as a number of distinguishable (separable) group of voxels. Cell volume: Calculated as the number of voxels in each cell. Longest Axis: Length of the longest axis out of three principal axes from parameter “Principal axis length” extracted from Matlab function regionprops3. Cell Elongation: Longest axis length divided by the average length of intermediate and minor axis. High values of elongation are presented in red and small values in blue. Cell ellipticity: As demonstrated in the figure, ellipticity is calculated by dividing the subtraction of the longest and minor axis lengths by the longest axis length. Volume-to-surface area ratio: This parameter is calculated by dividing the volume with surface area. It shows the difference between round and platonic cells (polyhedronic or irregular spiky membranes). A ball will display the darkest intensity of red.

Article Snippet: The performance of our new MATLAB-based pipeline, ShapeMetrics, was compared to multiple existing free programs including ImageJ/FIJI, CellProfiler TM , and CellSegm as well as the commercially available Imaris and its application ImarisCell specifically developed for this type of 3D and 2D analysis of cells.

Techniques: Labeling, Membrane, Extraction, Immunostaining

Journal: Developmental biology

Article Title: ShapeMetrics: A userfriendly pipeline for 3D cell segmentation and spatial tissue analysis

doi: 10.1016/j.ydbio.2020.02.003

Figure Lengend Snippet: (A) ShapeMetrics shows no bias between different tissue types as shown by segmentation of mesenchyme and epithelium from the same embryonic kidney sample stained with two different cadherins (E-cadherin in red and N-cadherin in green). Scale bar 50 μm. (B) Maximum projections of the distinct channels, (C) 3D renderings and (D) histograms showing distribution of cell volumes in a realistic biological range (epithelium in the upper row and mesenchyme in the lower row). (E) Single stack of the original neural tube image used for the analysis and (F) the respective 3D rendition and (G) corresponding histogram of cell volume distributions. (H) Sox9 immunostaining for the same sample and (I) the resulting 3D rendition of the Sox9-positive nuclei. (J) 3D rendition of the cells that express Sox9 (i.e. share the same voxel coordinates with the Sox9 rendition). (K) The histogram of volume distributions as well as (L) the spatial localization of these cells labelled with pseudo-coloring in the original image. (M) Heatmap of all the cells segmented from the sample. One of the two main branches that contains the large cells is selected in red. (N) Visualization of the spatial localization of the cells included in the red cluster from the heat map. (O) Visualization of only the Sox9 positive cells in the red subgroup presented by pseudo-coloring on the original image.

Article Snippet: The performance of our new MATLAB-based pipeline, ShapeMetrics, was compared to multiple existing free programs including ImageJ/FIJI, CellProfiler TM , and CellSegm as well as the commercially available Imaris and its application ImarisCell specifically developed for this type of 3D and 2D analysis of cells.

Techniques: Staining, Immunostaining

Journal: Developmental biology

Article Title: ShapeMetrics: A userfriendly pipeline for 3D cell segmentation and spatial tissue analysis

doi: 10.1016/j.ydbio.2020.02.003

Figure Lengend Snippet: (A) The original image used for all softwares. (B) FIJI: visualization of 2D (left) and 3D (right) segmentation results of the binary watershed segmentation option. (C) CellProfiler: Visualization of the 2D (top) and 3D (below) monolayer segmentation results using IdentifyObjects and Measure modules. (D) ImarisCell: Visualization of the 2D (left) and 3D (right) segmentation results. (E) ShapeMetrics: visualization of the 2D (left) and 3D (right) watershed segmentation results.

Article Snippet: The performance of our new MATLAB-based pipeline, ShapeMetrics, was compared to multiple existing free programs including ImageJ/FIJI, CellProfiler TM , and CellSegm as well as the commercially available Imaris and its application ImarisCell specifically developed for this type of 3D and 2D analysis of cells.

Techniques:

Journal: Developmental biology

Article Title: ShapeMetrics: A userfriendly pipeline for 3D cell segmentation and spatial tissue analysis

doi: 10.1016/j.ydbio.2020.02.003

Figure Lengend Snippet: The quantitative results of the comparison show that our code ShapeMetrics outputs very similar cell numbers and volumes as ImarisCell. Fiji, on the other hand, outputs a similar number of cells but the mean volume of the cells as well as the visualization in Fig. 7 reveal that the segmentation is not successful. CellProfiler is not applicable for 3D segmentation of cells.

Article Snippet: The performance of our new MATLAB-based pipeline, ShapeMetrics, was compared to multiple existing free programs including ImageJ/FIJI, CellProfiler TM , and CellSegm as well as the commercially available Imaris and its application ImarisCell specifically developed for this type of 3D and 2D analysis of cells.

Techniques: Comparison