Measuring the confluency of multiple sites in a data object and with a more aggressive brightening:

A: The adjustment only contains brightening the image by two levels (each pixel intensity value increased by twice its pixel intensity value):

B: For the segmentation, we perform standard deviation filter to detect the more homogenous regions which are the cell free medium:

C: We perform the denuded area (cell-free zone) detection with two standard deviation sizes and call them small and big window: 1- standard deviation filter with a small window, 2- binarize the image using the small window filtered image, 3- standard deviation filter using a big window, 4- binarize the image using the big window filtered image, 5- dilate the binarized image with big window:

D: Finally, we determine the intersect of the cellular regions to indicate our final culture cell area and will perform a subsequent closing and opening to remove any holes and very small components: 1- standard deviation filter with a small window, 2- binarize the image using the small window filtered image, 3- standard deviation filter using a big window, 4- binarize the image using the big window filtered image, 5- dilate the binarized image with big window, 6- find the intersect of the dilated image with the small window binaized, 7- perform closing followed by opening with the intersect result:

A: The adjustment steps are: 1- increasing the brightness of the image (given our camera setup the images are a little dark) and 2- trimming the image to select a sepecific square in the hemocytometer grid which depending on the type of the grid, is a pair of number {r,c} where r and c are between 1-3 or 1-4. In this case we are selecting square position {1,3}. The pixel coordinates can be determined by first plotting the input like PlotImage[myCloudFile]:

B: As for the standard segmentation procedure, we perform: 1- edge detection for the adjusted image so we will detect the contours of all components, 2- we close the detected lines (all circles) using Closing primitive which calls mathematica's Closing, 3,4- We perform dilation and erosion with the same kernel so for the detected components any holes will be filled remove any extremely small component will be removed:

C: After we find all components we need to remove the hemocytometer grid lines. For that we first use ridge filter to detect the grid lines and then binarize and finally remove the detected lines: 5- Use RidgeFilter to further use to find the mask (background grid) and ImageAdjust ing the result, 6- MorphologicalBinarize the result of ridge filter with a low threshold (0.05-0.5) to find the background grid mask, 7- Remove the background grid using Inpaint with the method Diffusion which is a compromise between time and quality:

D: Finally after removing the background grid lines, we use a standard process to avoid any overlap between the cells. This is done by first finding the distance of the cell boundary relative to their centers and then using watershed algorithm: 8- Find the distance transform which normalize the pixels according to their distance to edges of black and white boundary, 9- Find the maximum of the distance transform which indicates the centers of the components, 10- Find the regions of rapid pixel value change with gradient filter to indicate the regions associated with the components, 11- Finally perform the watershed algorithm on the result of the gradient filter with the marker set as the result of MaxDetect:

Open AnalyzeCellCount in the command builder to access the interactive app for image selection:

Open AnalyzeCellCount in the command builder to access the interactive app for manual counting:

A: Segmenting the cells that are stained in one channel:

B: Standard segmentation of red blood cells taken from the mathematica guide pages:

C: Analyzing the deformation and morphology of the detected RBCs. We extract echinocytes cells by selecting cells of a certain size that exhibit tentacles of more than 5% of their total area:

Segmentation of Epifluorescence channel of Mammalian, HeLa-dsRed/GFP model cells:

Segmentation of small sparse tissue culture cells using the default algorithm:

Measure the viability of the cells from the colored image where blue indicates dead cells:

Adding a criteria to exclude cells smaller than 90 Pixel squared:

Explicitily specifying the type of the cell:

If set as Automatic for data object, it will be taken from the CellModels field of the data object if it is Bacterial, Mammalian, Yeast, Plant, or Insect:

Measure the viability of the cells with specifying threshold:

Explicitly specifying the type of the type of cell adhesion to the substrate if it is adherent or suspension:

If CultureAdhesion is Automatic and the input is a data object CultureAdhesion will be determined from the field CellModels:

Automatically specifying the grid pattern from the Object[Container,Hemocytometer] in the field ContainersIn of the protocol object:

Explicitly informing that the grid pattern of the hemocytometer from the data object which affects the default square specification:

Cell count analysis of a hemocytometer image with a short-hand notation, Hemocytometer->True:

Explicitly informing that the raw image is taken from a hemocytometer instrument:

If a data object is given, the hemocytometer will be populated based on the data object protocol's ContaintersIn field:

Explicitly informing the square position to use for counting the cells of the hemocytometer. By default {1,1} which is the left bottom square will be selected:

Showing cells using their contour:

Showing cells with labeled circles:

Change the default histogram shown on the statistics panel:

Explicitly provide the image adjustment steps including trimming the image:

Explicitly provide the image specification in the data object. This should be useful only to pick a specific item that is autopopulated by ImageSelection:

Specifying the image scale if the input is not a data object:

Explicitly provide the image segmentation steps for confluency measurement of high density cultured cells:

Explicitly provide the image segmentation steps for confluency measurement of low density cultured cells:

Explicitly provide the ImageSelection to select specific sites available in the data object:

Adding a criteria to exclude cells with fluoresent intensity less than the threshold:

The manual coordinates stores the coordinates of the selected (clicked) cell positions:

The number of manual cells is automatically calculated in the app after clicking Update Table button. This can be checked with PreviewValue[PreviewSymbol[AnalyzeCellCount], ManualSampleCellDensity].:

Specifying the maximum cell radius used for calculating the cell count distribution when there are multiple cells per component:

Adding a criteria to exclude components with radius larger than 10 Pixels:

Measure the viability of the cells:

Explicitly provide the MeasureConfluency master switch to automatically determine the adjustment and segmentation steps:

For the CellType Mammalian and CultureAdhesion Adherent, the MeasureConfluency is set to True:

Manual counting pane as the preview specifically used for user builder interaction:

Specifying the minimum cell radius used for calculating the cell count distribution when there are multiple cells per component:

Adding a criteria to exclude components with radius smaller than 2 Pixels:

The number of manual cells is automatically calculated in the app after clicking Update Table button. This can be checked with PreviewValue[PreviewSymbol[AnalyzeCellCount], NumberOfManualCells].:

For plotting the results of all middle steps in the adjustment and segmentation of the images:

Measuring all properties associated with the pattern ComponentPropertiesAreaP, ComponentPropertiesPerimeterP, ComponentPropertiesCentroidP, ComponentPropertiesEllipseP:

Use a generated analysis object as the template:

If the algorithms Hemocytometer and MeasureConfluency are set at the same time, the latter will be enforced to False:

If the minimum cell radius is larger than maximum:

If the minimum component radius is larger than maximum:

If the minimum cell radius is larger than maximum. One is specified and the other is set to the default:

If the Images field is empty:

The keys in the MicroscopeImage primitive is not complete:

The primitives provided for images option do not correspond to a valid image in the data object:

Manual and Hybrid methods are used for hemocytometer images:

Pooled options should be indexed with the number of images in the object:

Requested label is not avaialable:

SelectComponents is not specified to take the max radius into effect:

If Method is Manual or Hybrid, the HemocytometerSquarePosition is set to All: