PMC

Surface quality and surface identity differs using traditional surface creation and HSB surface creation workflows. a Incomplete surfaces are generated with traditional surface creation, while the HSB surface creation workflow generates complete surfaces. Splenocytes stained with different combinations of DAPI, CFSE, and propidium iodide were mixed and imaged. The composite image of all three channels was compared with surfaces generated using the traditional surface creation and the HSB surface creation workflow. Surfaces from traditional surface creation are rendered as a composite of the different subsets, while surfaces from the HSB surface creation workflow are rendered in the median hue with triple-positive cells colored as white. Traditional surface creation results in a cell with surfaces belonging to two different subsets as the three markers do not fully overlap, while HSB surface creation results in a complete surface. Scale bar, 10 µm. b Cell subsets are defined early in the traditional surface creation workflow, but are only defined during the analysis step using the HSB surface creation workflow. Splenocytes stained with different combinations of DAPI and CFSE were mixed and imaged. Using the traditional surface creation workflow, the DAPI⁺ CFSE⁺ cell subset is defined during the creation of the co-localization channel. The co-localization channel is used to mask the original DAPI and CSFE channels, thus defining the single-positive DAPI⁺ and CSFE⁺ subsets. Using the HSB surface creation workflow, surfaces of any cell is created in a one step process, allowing for cell subsets to be defined during analysis in flow cytometry plots. Scale bar, 20 µm. Abbreviations: DAPI 4’,6-Diamidino-2-Phenylindole Dihydrochloride, CFSE carboxyfluorescein succinimidyl ester

(a) Inhomogeneity correction and segmentation of MR, (b) creation of whole brain and CSF masks, (c) skull-stripping of inhomogeneity-corrected MR, (d) spatial normalization of skull-stripped MR to MNI template for skull-stripped MR, (e) spatial normalization of masks by applying normalization parameter, (f) creation of probabilistic maps for whole brain and CSF by averaging tissue masks, (g) coregistration of CT to inhomogeneity-corrected MR, (h) creation of skull mask, (i) spatial normalization of skull mask, (j) creation of probabilistic map for skull, (k) spatial normalization of coregistered CT, (l) creation of whole CT template by averaging, (m) creation of template mask for scalp-stripping, (n) spatial normalization of CT to whole CT template, (o) inverse normalization of template mask for scalp-stripping to create individual mask for scalp-stripping, (p) creation of scalp-stripped CT by applying mask, (q) segmentation of scalp-stripped CT into three tissue type by using probabilistic maps, (r) creation of ssCT by applying mask for whole brain segment, (s) coregistration of ssCT to MR by applying parameter coregistering original CT to MR, (t) spatial normalization of coregistered ssCT, (u) creation of ssCT template by averaging, (v) intensity transformation of original CT, (w) coregistration of itCT to MR, (x) spatial normalization of coregistered itCT, (y) creation of itCT template by averaging, and (z) spatial normalization of individual ssCT and itCT to specific CT templates. The processing steps inside the red (ssCT) and blue (itCT) dashed lines represent the image processing steps required for the spatial normalization of CT images with created CT templates.

Image processing steps for acquiring skull-stripped CT template.
(a) Inhomogeneity correction and segmentation of MR, (b) creation of whole brain mask, (c) creation of CSF mask, (d) extraction of whole brain with whole brain mask, (e) spatial normalization of whole brain extract to skull-stripped MNI template, (f) normalization of whole brain mask, (g) normalization of CSF mask, (h, i) creation of probabilistic template for whole brain and CSF, (j) coregistration to inhomogeneity-corrected MR, (k) extraction of skull, (l) spatial normalization of CT coregistered to MR, (m) spatial normalization of skull, (n) creation of probabilistic template for skull, (o) creation of CT template, (p) creation of template mask for scalp-stripping, (q) spatial normalization of original CT with normalization parameter normalizing CT to CT template, (r) inverse normalization of template mask for scalp-stripping to individual mask by using inverse normalization parameter, (s) creation of scalp-stripped CT with individual mask for scalp-stripping, (t) segmentation of scalp-stripped CT into skull, whole brain and CSF by using skull, whole brain and CSF probabilistic templates, (u) creation of skull-stripped CT with whole brain segment, (v) skull stripped CT coregistered to MR, (w) spatial normalization of skull-stripped CT, (x) creation of skull-stripped CT template by averaging. CSF = cerebrospinal fluid, MNI = Montreal Neurological Institute

Comparison of traditional surface creation with hue-saturation-brightness surface creation. Traditional surface creation: For a three color image acquired in the red, green, and channels, it is necessary to first identify the different possible combinations of triple positive and double-positive cells using the co-localization channel for each combination. Cell surfaces of the triple and double-positive cells are created using image analysis software, surfaces are merged and used to mask the relevant cells in the original red, green, and blue channels to generate single-positive cells. Each of the individual channels is then used for surface creation and statistics extraction. Hue-saturation brightness surface creation: Hue, saturation, and brightness channels will be generated from the original image using the HSB algorithm. Any cell that expresses a marker will be captured in the brightness channel, thus making it possible to generate cell surfaces for all the cells in one channel. Triple positive cells can be identified using the saturation channel, and information from the hue channel can then be used to render the surfaces in their native hue. Scale bar, 5 µm