All tumors were segmented using an in-house single-click ensemble segmentation algorithm on the Lung Tumor Analysis (LuTA) software program platform (Definiens Developer XD©, Munich, Germany) [25 (link)]. After applying the single click approach, the tumor delineations were inspected and edited if needed by a resident expert radiologist. The lung and tumor mask images obtained from LuTA software program were then imported into MATLAB® (Mathworks, Natick, MA) for image feature extraction as described below.
Developer xd
Manufactured by Definiens
Sourced in Germany
Developer XD is a high-performance imaging software that enables advanced image analysis and visualization. The core function of Developer XD is to provide a comprehensive platform for processing, analyzing, and interpreting digital images across a wide range of applications.
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Lab products found in correlation
Tissue studio, by Definiens (3 mentions)
Matlab, by MathWorks (2 mentions)
Imaris, by Oxford Instruments (1 mentions)
Imagej, by Oxford Instruments (1 mentions)
Dab map kit, by Roche (1 mentions)
Anti laminin, by Merck Group (1 mentions)
Ge discovery 750 hd, by GE Healthcare (1 mentions)
Ge lightspeed 16, by GE Healthcare (1 mentions)
21 protocols using developer xd
1
Lung Tumor Segmentation and Analysis
2
Biofilm Viability Microscopic Evaluation
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Qualitative and quantitative microscopic evaluations of the biofilms were carried out through a combination of the LIVE/DEAD BacLight viability staining and automated confocal laser scanning microscopy (CLSM), as previously described [28] (link). The 48-h-old biofilms of strains ATCC 16367, Sm1, and Sm2 were visualised after 24 h exposure to EGCg or COL at various concentrations. For this assay, the DNA-binding dyes Syto9 (green) and propidium iodide (PI; red) were used. This two-colour kit differentially stains living (green) and membrane-compromised/dead (red) bacteria according to differences in membrane permeability. Biofilm susceptibility was determined on the basis of the fractions of red (including co-localized) and green biovolume [μm3] calculated from the image stacks with a customer-designed solution in the software Developer XD (Definiens). The negative controls were biofilms treated with Luria Bertani (LB) medium supplemented with 1% (v/v) DMSO, and the positive controls (killing control) were treated with formalin at final concentration of 1% (v/v) formalin. Visualization of biofilm sections was performed with the software IMARIS (Bitplane). Data are expressed as means of two independent experiments. Experiments were carried out in duplicates.
3
Automated Quantitative Image Analysis of Tissue Fibrosis
4
Spatial Analysis of Bone Substitute Particles
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In addition to BS.N, the size of individual BS particles also affects the spatial organisation within the AA. Accordingly, a rule set was created to assign a unique number to each BS particle, starting with the particle located closest to the “sinus floor borderline” (i.e., in zone 0–100 µm in the augmentation area) towards the most apical particles in the AA (Definiens Developer XD, Definiens, Munich, Germany). If neighbouring BS particles were not automatically identified as single particles in the segmentation process, they were separated with a manually drawn line using Adobe Photoshop (Adobe, San Jose, CA, USA). Since most BS particles occupy more than one 100 µm zone, the numbered particles were assigned to the 100 µm zone where their respective centre of mass lies in.
Based on this assignment, the volume of each individual particle was calculated, and their average volume per 100 µm zone was plotted as a gradient of avgBSV over the length of the AA.
Based on this assignment, the volume of each individual particle was calculated, and their average volume per 100 µm zone was plotted as a gradient of avgBSV over the length of the AA.
5
Quantitative Biofilm Analysis Pipeline
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Both acquired image stacks were analyzed with the Developer XD (Definiens) software. The programmed customized solution of the Developer software is based on a previously described software called PHLIP47 (link) and is used to determine biofilm-specific parameter for both fluorescent channels separately, as well as combined including, for example, ratios of the differentially stained populations within the biofilm or the biovolume, which describes the biofilm biomass. To optimize the analysis, we combined data of the overview and the zoom image stacks; the biovolume was taken from the overview job to define the overall amount of biofilm, while we used the percentage of dead (red fluorescent) cells of the zoom job. The latter has a better image resolution and, thus, allowed a more accurate estimation of fluorescence ratios. With both values we determined the green fluorescent biofilm—the living population—and compared treated samples with the control. A series of antibiotic dilutions are needed to categorize the responsiveness. ImageJ and Imaris (Bitplane AG) were used for the visualization of biofilm image stacks, the latter for complex 3D reconstructions.
6
Automated Tumour Identification and Biomarker Quantification
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We developed an automated image analysis algorithm using Definiens Developer XD for tumour identification and biomarker quantification as described in Supplementary Materials .
7
Automated Image Analysis of Tumor Tissue
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The novel image analysis algorithm was created within the Definiens image analysis software packages: Tissue Studio® and Developer XD™ (Definiens AG, Munich). Images were imported into Definiens as .TIFF files. Initial image segmentation utilized an image analysis algorithm created in Tissue Studio® as described previously [3 (link)]. All segmented objects were further classified within a hierarchical system where the top level was automatically segmented through machine-based learning using Definiens Composer Technology™ into Regions of Interest (ROIs): ‘tumor’, ‘necrosis/lumen’, ‘no tissue’ and ‘stroma’. Next, the object level of the image analysis hierarchy captured all panCK and D2-40 positive objects in the stroma. The final layer of image analysis identified nuclei through the DAPI channel. Each nucleus was exclusively segregated into relevant subpopulations existing in the analysis layers above. The Tissue Studio® analyzed workspace was subsequently imported into Developer XD™ for bespoke object classification, optimization and parameter export. A full description of the image analysis methods, with accompanying figures and settings for the Definiens’ rulesets are listed in Supplementary Document 2 .
8
Quantitative Immunohistochemical Analysis
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The number of stained cells (CD8/CD20/CD68/MUM-1/PD-1 and LAG-3) per tissue areas was calculated using the computerized image analysis system Developer XD (Definiens Company®, Munich, Germany). With this method, each tissue area is divided into tiles consisting of 0.8 mm slices, and the mean density is the ratio of the number of immuno-labeled cells over the tiles’ surface.
The staining density of HCMV- and NKp46-positive cells could not be quantified by image analysis, because both cell types were present in small quantities, and staining of NKp46-positive cells was too weak. For these 2 antibodies, labeled cells present on the slide were counted manually, and the tissue surface was extrapolated from that calculated by image analysis of a consecutive section of the same brain sample.
The density of NKG2C-positive cells could not be calculated because of too strong a background signal for quantitative imaging and too high a number of positive cells for manual quantification. PD-L1 density could not be calculated because positive cells showed low staining intensity and aggregated in clusters, making both manual count and quantitative imaging impossible.
The staining density of HCMV- and NKp46-positive cells could not be quantified by image analysis, because both cell types were present in small quantities, and staining of NKp46-positive cells was too weak. For these 2 antibodies, labeled cells present on the slide were counted manually, and the tissue surface was extrapolated from that calculated by image analysis of a consecutive section of the same brain sample.
The density of NKG2C-positive cells could not be calculated because of too strong a background signal for quantitative imaging and too high a number of positive cells for manual quantification. PD-L1 density could not be calculated because positive cells showed low staining intensity and aggregated in clusters, making both manual count and quantitative imaging impossible.
9
Semi-Automated Tumor Segmentation and Feature Extraction
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Patient CT scans were segmented to identify lung fields and tumors. The delineation of the lung fields was carried out using single click ensemble algorithm developed using Lung Tumor Analysis (LuTA) tool within the Definiens Developer XD© (Munich, Germany) software platform. Target lesions were volumetrically segmented using semiautomatic approach. The resident radiologist (over 2 years of experience) oversaw the segmentation boundaries on the CT slices. Performing semi-automatic segmentation not only decreased user interaction and eliminated the need for a manually drawn boundary, but also provided robust, reproducible and consistent delineation of the tumor region across the CT slices. We have previously demonstrated that the single click ensemble segmentation algorithm reduced inter-observer variability while capturing the intricacies and important details of the tumor boundary[15 (link)].
Algorithms for image feature extraction and quantification of the segmented tumor regions were implemented in MATLAB (Mathworks, Natick, MA).
Algorithms for image feature extraction and quantification of the segmented tumor regions were implemented in MATLAB (Mathworks, Natick, MA).
10
Quantitative Immunohistochemistry for CD3 and CD8
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For each patient, a pre-treatment paraffin-embedded tumor block from the Center of the Tumor (CT) and the Invasive Margin (IM) was selected. Two tissue paraffin sections of 4 microns (µm) were processed for IHC staining for CD3 and CD8 antibodies according to a protocol optimized by HalioDX (a Veracyte Company) and previously described by [10 (link)]. Digital slides were obtained with a 20 × magnification and a resolution at 0.45 µm/pixel. Image analysis was performed on the scanned digital slides. The mean CD3 and CD8 cell densities were determined in the CT and IM regions using a specially developed Immunoscore® module (INSERM, Paris, France) integrated into the image analysis system Developer XD (Definiens, Munich, Germany).
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