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imaris workflow  (Oxford Instruments)


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    Structured Review

    Oxford Instruments imaris workflow
    Images illustrating <t>the</t> <t>Imaris</t> <t>workflow</t> developed for the vascular network modeling. A–I: Sequential workflow steps allowing the analysis of the cerebellar vascular network of a P4 mouse cerebellum on the Imaris software. From a 3D lightsheet acquisition (A) , the cerebellum is delineated (B) and a mask is created (C) . Within that selected volume, the cerebellar vascularization is segmented (D) , which allows the network visualization (E) and the separation of a deep and a superficial network (F) . Then the threshold of seedpoints is defined (G) , and thanks to the artificial intelligence module (AI), Imaris is able to discriminate “true” (blue) and “false” (red) seedpoints (H) , and “true” (blue) and “false” (red) segments (I) . AI: artificial intelligence; Px: postnatal day x
    Imaris Workflow, supplied by Oxford Instruments, used in various techniques. Bioz Stars score: 99/100, based on 42530 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/imaris workflow/product/Oxford Instruments
    Average 99 stars, based on 42530 article reviews
    imaris workflow - by Bioz Stars, 2026-05
    99/100 stars

    Images

    1) Product Images from "Innovative 3D-Image Analysis of Cerebellar Vascularization Highlights Angiogenic Gene Dysregulations in a Murine Model of Apnea of Prematurity"

    Article Title: Innovative 3D-Image Analysis of Cerebellar Vascularization Highlights Angiogenic Gene Dysregulations in a Murine Model of Apnea of Prematurity

    Journal: Cerebellum (London, England)

    doi: 10.1007/s12311-026-02006-1

    Images illustrating the Imaris workflow developed for the vascular network modeling. A–I: Sequential workflow steps allowing the analysis of the cerebellar vascular network of a P4 mouse cerebellum on the Imaris software. From a 3D lightsheet acquisition (A) , the cerebellum is delineated (B) and a mask is created (C) . Within that selected volume, the cerebellar vascularization is segmented (D) , which allows the network visualization (E) and the separation of a deep and a superficial network (F) . Then the threshold of seedpoints is defined (G) , and thanks to the artificial intelligence module (AI), Imaris is able to discriminate “true” (blue) and “false” (red) seedpoints (H) , and “true” (blue) and “false” (red) segments (I) . AI: artificial intelligence; Px: postnatal day x
    Figure Legend Snippet: Images illustrating the Imaris workflow developed for the vascular network modeling. A–I: Sequential workflow steps allowing the analysis of the cerebellar vascular network of a P4 mouse cerebellum on the Imaris software. From a 3D lightsheet acquisition (A) , the cerebellum is delineated (B) and a mask is created (C) . Within that selected volume, the cerebellar vascularization is segmented (D) , which allows the network visualization (E) and the separation of a deep and a superficial network (F) . Then the threshold of seedpoints is defined (G) , and thanks to the artificial intelligence module (AI), Imaris is able to discriminate “true” (blue) and “false” (red) seedpoints (H) , and “true” (blue) and “false” (red) segments (I) . AI: artificial intelligence; Px: postnatal day x

    Techniques Used: Software



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    Images illustrating <t>the</t> <t>Imaris</t> <t>workflow</t> developed for the vascular network modeling. A–I: Sequential workflow steps allowing the analysis of the cerebellar vascular network of a P4 mouse cerebellum on the Imaris software. From a 3D lightsheet acquisition (A) , the cerebellum is delineated (B) and a mask is created (C) . Within that selected volume, the cerebellar vascularization is segmented (D) , which allows the network visualization (E) and the separation of a deep and a superficial network (F) . Then the threshold of seedpoints is defined (G) , and thanks to the artificial intelligence module (AI), Imaris is able to discriminate “true” (blue) and “false” (red) seedpoints (H) , and “true” (blue) and “false” (red) segments (I) . AI: artificial intelligence; Px: postnatal day x
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    Images illustrating <t>the</t> <t>Imaris</t> <t>workflow</t> developed for the vascular network modeling. A–I: Sequential workflow steps allowing the analysis of the cerebellar vascular network of a P4 mouse cerebellum on the Imaris software. From a 3D lightsheet acquisition (A) , the cerebellum is delineated (B) and a mask is created (C) . Within that selected volume, the cerebellar vascularization is segmented (D) , which allows the network visualization (E) and the separation of a deep and a superficial network (F) . Then the threshold of seedpoints is defined (G) , and thanks to the artificial intelligence module (AI), Imaris is able to discriminate “true” (blue) and “false” (red) seedpoints (H) , and “true” (blue) and “false” (red) segments (I) . AI: artificial intelligence; Px: postnatal day x
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    Images illustrating <t>the</t> <t>Imaris</t> <t>workflow</t> developed for the vascular network modeling. A–I: Sequential workflow steps allowing the analysis of the cerebellar vascular network of a P4 mouse cerebellum on the Imaris software. From a 3D lightsheet acquisition (A) , the cerebellum is delineated (B) and a mask is created (C) . Within that selected volume, the cerebellar vascularization is segmented (D) , which allows the network visualization (E) and the separation of a deep and a superficial network (F) . Then the threshold of seedpoints is defined (G) , and thanks to the artificial intelligence module (AI), Imaris is able to discriminate “true” (blue) and “false” (red) seedpoints (H) , and “true” (blue) and “false” (red) segments (I) . AI: artificial intelligence; Px: postnatal day x
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    Images illustrating <t>the</t> <t>Imaris</t> <t>workflow</t> developed for the vascular network modeling. A–I: Sequential workflow steps allowing the analysis of the cerebellar vascular network of a P4 mouse cerebellum on the Imaris software. From a 3D lightsheet acquisition (A) , the cerebellum is delineated (B) and a mask is created (C) . Within that selected volume, the cerebellar vascularization is segmented (D) , which allows the network visualization (E) and the separation of a deep and a superficial network (F) . Then the threshold of seedpoints is defined (G) , and thanks to the artificial intelligence module (AI), Imaris is able to discriminate “true” (blue) and “false” (red) seedpoints (H) , and “true” (blue) and “false” (red) segments (I) . AI: artificial intelligence; Px: postnatal day x
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    Image Search Results


    Images illustrating the Imaris workflow developed for the vascular network modeling. A–I: Sequential workflow steps allowing the analysis of the cerebellar vascular network of a P4 mouse cerebellum on the Imaris software. From a 3D lightsheet acquisition (A) , the cerebellum is delineated (B) and a mask is created (C) . Within that selected volume, the cerebellar vascularization is segmented (D) , which allows the network visualization (E) and the separation of a deep and a superficial network (F) . Then the threshold of seedpoints is defined (G) , and thanks to the artificial intelligence module (AI), Imaris is able to discriminate “true” (blue) and “false” (red) seedpoints (H) , and “true” (blue) and “false” (red) segments (I) . AI: artificial intelligence; Px: postnatal day x

    Journal: Cerebellum (London, England)

    Article Title: Innovative 3D-Image Analysis of Cerebellar Vascularization Highlights Angiogenic Gene Dysregulations in a Murine Model of Apnea of Prematurity

    doi: 10.1007/s12311-026-02006-1

    Figure Lengend Snippet: Images illustrating the Imaris workflow developed for the vascular network modeling. A–I: Sequential workflow steps allowing the analysis of the cerebellar vascular network of a P4 mouse cerebellum on the Imaris software. From a 3D lightsheet acquisition (A) , the cerebellum is delineated (B) and a mask is created (C) . Within that selected volume, the cerebellar vascularization is segmented (D) , which allows the network visualization (E) and the separation of a deep and a superficial network (F) . Then the threshold of seedpoints is defined (G) , and thanks to the artificial intelligence module (AI), Imaris is able to discriminate “true” (blue) and “false” (red) seedpoints (H) , and “true” (blue) and “false” (red) segments (I) . AI: artificial intelligence; Px: postnatal day x

    Article Snippet: IH: intermittent hypoxia; N: normoxia; Px: postnatal day x Fig. 2 Images illustrating the Imaris workflow developed for the vascular network modeling.

    Techniques: Software

    Images illustrating the Imaris workflow developed for the vascular network modeling. A–I: Sequential workflow steps allowing the analysis of the cerebellar vascular network of a P4 mouse cerebellum on the Imaris software. From a 3D lightsheet acquisition (A) , the cerebellum is delineated (B) and a mask is created (C) . Within that selected volume, the cerebellar vascularization is segmented (D) , which allows the network visualization (E) and the separation of a deep and a superficial network (F) . Then the threshold of seedpoints is defined (G) , and thanks to the artificial intelligence module (AI), Imaris is able to discriminate “true” (blue) and “false” (red) seedpoints (H) , and “true” (blue) and “false” (red) segments (I) . AI: artificial intelligence; Px: postnatal day x

    Journal: Cerebellum (London, England)

    Article Title: Innovative 3D-Image Analysis of Cerebellar Vascularization Highlights Angiogenic Gene Dysregulations in a Murine Model of Apnea of Prematurity

    doi: 10.1007/s12311-026-02006-1

    Figure Lengend Snippet: Images illustrating the Imaris workflow developed for the vascular network modeling. A–I: Sequential workflow steps allowing the analysis of the cerebellar vascular network of a P4 mouse cerebellum on the Imaris software. From a 3D lightsheet acquisition (A) , the cerebellum is delineated (B) and a mask is created (C) . Within that selected volume, the cerebellar vascularization is segmented (D) , which allows the network visualization (E) and the separation of a deep and a superficial network (F) . Then the threshold of seedpoints is defined (G) , and thanks to the artificial intelligence module (AI), Imaris is able to discriminate “true” (blue) and “false” (red) seedpoints (H) , and “true” (blue) and “false” (red) segments (I) . AI: artificial intelligence; Px: postnatal day x

    Article Snippet: Moreover, to correlate putative modifications of gene expression with defects in vessel morphology, we developed an image analysis workflow using both Imaris and VesselVio software to visualize the cerebellar vascularization in 3D during postnatal development and obtain comparable quantitative parameters between normoxic and IH conditions.

    Techniques: Software