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machine learning software  (Oxford Instruments)


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    Oxford Instruments machine learning software
    Machine Learning Software, supplied by Oxford Instruments, used in various techniques. Bioz Stars score: 99/100, based on 44064 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/machine+learning+software/Imaris/pm40299956-254-1-0
    Average 99 stars, based on 44064 article reviews
    machine learning software - by Bioz Stars, 2026-09
    99/100 stars

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    Related Articles

    Software:

    Article Title: Increased luminal pressure in brain capillaries drives TRPC3-dependent depolarization and constriction of transitional pericytes.
    Article Snippet: Cerebral autoregulation ensures constant blood flow, an essential condition of brain health.. A fundamental parameter of the brain circulation is the dynamic regulation of microvessel diameter to allow for adjustments in resistance to blood pressure changes.. Pericytes are a family of mural cells that wrap around the capillary endothelium and contribute to the dynamic control of capillary diameter.

    Article Title: Increased luminal pressure in brain capillaries drives TRPC3-dependent depolarization and constriction of transitional pericytes
    Article Snippet: .. Imaris’ machine learning software was used to define lumen borders as foreground and all other spaces as background. ..

    Article Title: Extracellular vesicles from mucopolysaccharidosis type III microglia impair neurite growth
    Article Snippet: .. Analysis of large mosaic images (25 squares, 10x objective) using Imaris machine-learning software enabled us to quantify the dimensions of the neuronal extensions ( ). ..

    Article Title: Sensory neuron LKB1 mediates ovarian and reproductive function
    Article Snippet: .. We utilized imagining and machine learning software, IMARIS (v. 10.1.0, Oxford Instruments), to quantify the number of Stk11 puncta in Scn10a expressing neurons. ..

    Article Title: The chemotrophic behaviour of Aspergillus niger: Mapping hyphal filaments during chemo-sensing; the first step towards directed materials formation.
    Article Snippet: .. The fungal growth area towards nutrient sources was mapped and analysed by the machine learning software, Imaris. ..

    Article Title: The chemotrophic behaviour of Aspergillus niger: Mapping hyphal filaments during chemo-sensing; the first step towards directed materials formation.
    Article Snippet: .. The agar-based system developed for the chemo sensing studies was designed to minimise interdiffusion of the chemotropic compounds and give reproducible results, which were then taken forward to be mapped and analysed by the machine learning software, Imaris. ..

    Expressing:

    Article Title: Sensory neuron LKB1 mediates ovarian and reproductive function
    Article Snippet: .. We utilized imagining and machine learning software, IMARIS (v. 10.1.0, Oxford Instruments), to quantify the number of Stk11 puncta in Scn10a expressing neurons. ..



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    Image Search Results


    Flowchart of study design. DM, diabetes mellitus; CCTA, coronary computed tomography angiography; CAD, coronary artery disease; CT-FFR, CCTA-derived fractional flow reserve; FAI, fat attenuation index; MACE, major adverse cardiovascular events

    Journal: BMC Medical Imaging

    Article Title: Incremental prognostic value of pericoronary fat attenuation index in diabetic patients with non-obstructive coronary artery disease

    doi: 10.1186/s12880-025-02146-6

    Figure Lengend Snippet: Flowchart of study design. DM, diabetes mellitus; CCTA, coronary computed tomography angiography; CAD, coronary artery disease; CT-FFR, CCTA-derived fractional flow reserve; FAI, fat attenuation index; MACE, major adverse cardiovascular events

    Article Snippet: CT-FFR analysis was performed using a machine learning-based CT-FFR software (version 3.5, Siemens Healthineers, Germany).

    Techniques: Computed Tomography, Derivative Assay

    Kaplan–Meier curves for cumulative MACE rates ( A , B , C ) and cumulative MACCE rates ( D , E , F ) for stratified groups based on HRP, CT-FFR, and pericoronary FAI. MACE, major adverse cardiovascular events; MACCE, major adverse cardiovascular and cerebrovascular events; HRP, high-risk plaque; CT-FFR, CCTA-derived fractional flow reserve; FAI, fat attenuation index; HU, Hounsfield units

    Journal: BMC Medical Imaging

    Article Title: Incremental prognostic value of pericoronary fat attenuation index in diabetic patients with non-obstructive coronary artery disease

    doi: 10.1186/s12880-025-02146-6

    Figure Lengend Snippet: Kaplan–Meier curves for cumulative MACE rates ( A , B , C ) and cumulative MACCE rates ( D , E , F ) for stratified groups based on HRP, CT-FFR, and pericoronary FAI. MACE, major adverse cardiovascular events; MACCE, major adverse cardiovascular and cerebrovascular events; HRP, high-risk plaque; CT-FFR, CCTA-derived fractional flow reserve; FAI, fat attenuation index; HU, Hounsfield units

    Article Snippet: CT-FFR analysis was performed using a machine learning-based CT-FFR software (version 3.5, Siemens Healthineers, Germany).

    Techniques: Derivative Assay

    ROC curves of all models in predicting MACE ( A ) and MACCE ( B ). Model 1: HRP; Model 2: CT-FFR; Model 3: pericoronary FAI; Model 4: HRP + CT-FFR; Model 5: Model 4 + pericoronary FAI. ROC, receiver operating characteristic; MACE, major adverse cardiovascular events; MACCE, major adverse cardiovascular and cerebrovascular events; HRP, high-risk plaque; CT-FFR, CCTA-derived fractional flow reserve; FAI, fat attenuation index; AUC, area under the curve; CI, confidence interval

    Journal: BMC Medical Imaging

    Article Title: Incremental prognostic value of pericoronary fat attenuation index in diabetic patients with non-obstructive coronary artery disease

    doi: 10.1186/s12880-025-02146-6

    Figure Lengend Snippet: ROC curves of all models in predicting MACE ( A ) and MACCE ( B ). Model 1: HRP; Model 2: CT-FFR; Model 3: pericoronary FAI; Model 4: HRP + CT-FFR; Model 5: Model 4 + pericoronary FAI. ROC, receiver operating characteristic; MACE, major adverse cardiovascular events; MACCE, major adverse cardiovascular and cerebrovascular events; HRP, high-risk plaque; CT-FFR, CCTA-derived fractional flow reserve; FAI, fat attenuation index; AUC, area under the curve; CI, confidence interval

    Article Snippet: CT-FFR analysis was performed using a machine learning-based CT-FFR software (version 3.5, Siemens Healthineers, Germany).

    Techniques: Derivative Assay

    A representative case of DM patients with non-obstructive CAD. CCTA showed CAD-RADS 2, with 25–49% stenosis in LAD as well as 1–24% stenosis in RCA, and there was a HRP characterized by low attenuation plaque and spotty calcification in LAD; CT-FFR was 0.88; pericoronary FAI was − 60.97 HU. This patient underwent acute non-ST-segment elevation myocardial infarction 40 months after CCTA. DM, diabetes mellitus; CAD, coronary artery disease; CCTA, coronary computed tomography angiography; CAD-RADS, Coronary Artery Disease-Reporting and Data System; LAD, left anterior descending; LCX, left circumflex; RCA, right coronary artery; HRP, high-risk plaque; CT-FFR, CCTA-derived fractional flow reserve; FAI, fat attenuation index; HU, Hounsfield units

    Journal: BMC Medical Imaging

    Article Title: Incremental prognostic value of pericoronary fat attenuation index in diabetic patients with non-obstructive coronary artery disease

    doi: 10.1186/s12880-025-02146-6

    Figure Lengend Snippet: A representative case of DM patients with non-obstructive CAD. CCTA showed CAD-RADS 2, with 25–49% stenosis in LAD as well as 1–24% stenosis in RCA, and there was a HRP characterized by low attenuation plaque and spotty calcification in LAD; CT-FFR was 0.88; pericoronary FAI was − 60.97 HU. This patient underwent acute non-ST-segment elevation myocardial infarction 40 months after CCTA. DM, diabetes mellitus; CAD, coronary artery disease; CCTA, coronary computed tomography angiography; CAD-RADS, Coronary Artery Disease-Reporting and Data System; LAD, left anterior descending; LCX, left circumflex; RCA, right coronary artery; HRP, high-risk plaque; CT-FFR, CCTA-derived fractional flow reserve; FAI, fat attenuation index; HU, Hounsfield units

    Article Snippet: CT-FFR analysis was performed using a machine learning-based CT-FFR software (version 3.5, Siemens Healthineers, Germany).

    Techniques: Computed Tomography, Derivative Assay