matlab-based image analysis pipeline  (MathWorks Inc)

Journal: Brain Sciences

Article Title: Diffusion Measures of Subcortical Structures Using High-Field MRI

doi: 10.3390/brainsci13030391

Figure Lengend Snippet: Basic processing Lead−connectome pipeline. Minimally preprocessed human connectome project images are co-registered to the structural T1w image using SPM. Structural images are registered to templates using ANTs registration, generating inverse transforms to translate MNI PD25 template to b0 space. DSI Studio is used to apply Q−sampling on diffusion images to reconstruct diffusion orientations and generate diffusion network statistics of segmented ROIs and ROI diffusion connectivity.

Article Snippet: LEAD Connectome, a MATLAB-based structural connectomic analysis pipeline which utilizes DSI studio for generalized Q-ball imaging (GQI), was used to generate structural connectome for each 3T and 7T acquisition [ ].

Techniques: Sampling, Diffusion-based Assay

Journal: Brain Sciences

Article Title: Diffusion Measures of Subcortical Structures Using High-Field MRI

doi: 10.3390/brainsci13030391

Figure Lengend Snippet: Connectivity matrices of significant, FDR−corrected mean differences of diffusion measures, FA, MD, QA, between 3T and 7T connectomes. Positive values (near red on the color bar) represent higher value of 3T mean diffusion measure while negative values (near dark blue on the color bar) represent higher value of 7T mean diffusion measure. Insignificant values were set to 0.

Article Snippet: LEAD Connectome, a MATLAB-based structural connectomic analysis pipeline which utilizes DSI studio for generalized Q-ball imaging (GQI), was used to generate structural connectome for each 3T and 7T acquisition [ ].

Techniques: Diffusion-based Assay

Journal: Journal of Pathology Informatics

Article Title: Predicting IHC staining classes of NF1 using features in the hematoxylin channel

doi: 10.1016/j.jpi.2023.100196

Figure Lengend Snippet: Workflow of data generation and visualization. (A) Data structure. 10 TMA slides include 5 ccRCC TMAs (50 patients), 3 ChRCC TMAs (30 patients), and 2 PRCC TMAs (17 patients) displaying 3 benign and 3 cancer cores from each of 97 patients. TMA slides are stained by IHC with an anti-NF1 antibody. (B) Digital H-score. A digital H-score is generated in QuPath for each core or for individual tubules. (C) Targeted feature extraction. After color deconvolution into hematoxylin (H channel) and DAB (NF1 channel) channels in QuPath, 33 targeted feature values of morphology and hematoxylin (H&M features) are exported from each cell for further analysis. (D) Unsupervised cell clustering based on H&M features using CytoMap. (E) Training of XGBost prediction model. H&M feature values are used as the input into prediction models that predict the NF1 staining intensity class.

Article Snippet: CytoMap is an MatLab-based Histo-Cytometric Multidimensional Analysis Pipeline (CytoMap) for spatial analysis of segmented cell objects, which utilizes diverse statistical approaches to extract and quantify information about cellular spatial positioning, preferential cell–cell associations, and global tissue structure.

Techniques: Staining, Generated, Extraction

Journal: Journal of Pathology Informatics

Article Title: Predicting IHC staining classes of NF1 using features in the hematoxylin channel

doi: 10.1016/j.jpi.2023.100196

Figure Lengend Snippet: Cell clustering using CytoMap . (A) Schematic workflow using CytoMap . Cell-wise H&M feature values together with cell coordinates are entered into CytoMap. (B) CytoMap output . CytoMap identifies the optimal number of clusters in the data and provides each cell with a cluster label. The pink shaded areas represent the range of NF1 staining intensity in high-NF1 expressing tubules, while the gray shaded area indicates NF1 staining levels in low-NF1 tubules. (C) Cluster visualization . Cluster labels are retuned to QuPath for overlay with the original image.

Article Snippet: CytoMap is an MatLab-based Histo-Cytometric Multidimensional Analysis Pipeline (CytoMap) for spatial analysis of segmented cell objects, which utilizes diverse statistical approaches to extract and quantify information about cellular spatial positioning, preferential cell–cell associations, and global tissue structure.

Techniques: Staining, Expressing

Journal: Journal of Pathology Informatics

Article Title: Predicting IHC staining classes of NF1 using features in the hematoxylin channel

doi: 10.1016/j.jpi.2023.100196

Figure Lengend Snippet: Cluster analysis of ccRCC, ChRCC, and PRCC . (A) Representative region of interest from cores analyzed in CytoMap. Columns 1 and 2 show corresponding H&E and IHC images after co-registration. The distance between the H&E and IHC tissue sections hinders a direct comparison at the cell level. The IHC image is analyzed in QuPath as described in to identify the NF1 class of each cell (third column). Fourth column shows the CytoMap cluster designation of each cell in the tissue context. (B) NF1 intensity comparison among clusters . Box plots show the NF1 staining intensity in each cluster. (C) Cluster representation inside NF1 classes. The percentage of cells within each cluster is plotted on the y-axis for NF1-negative, NF1-low, and NF1-high classes (X-axis) . Tissue images are screenshots at 40X magnification in QuPath.

Article Snippet: CytoMap is an MatLab-based Histo-Cytometric Multidimensional Analysis Pipeline (CytoMap) for spatial analysis of segmented cell objects, which utilizes diverse statistical approaches to extract and quantify information about cellular spatial positioning, preferential cell–cell associations, and global tissue structure.

Techniques: Comparison, Staining