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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 <t>intelligence</t> 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 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 <t>intelligence</t> 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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STMicroelectronics Pte intelligent power module
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 <t>intelligence</t> 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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Carl Zeiss intelligence module
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 <t>intelligence</t> 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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CooperSurgical artificial intelligence (pgtai) module
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 <t>intelligence</t> 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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Indica Labs halo indica tissue classifier artificial intelligence module
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 <t>intelligence</t> 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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Philips Healthcare radiomics module of the intelligent space for medicina scientia
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 <t>intelligence</t> 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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Nikon artificial intelligence modules
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 <t>intelligence</t> 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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SPECTRO Analytical spectro-temporal modulation transfer functions and speech intelligibility
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 <t>intelligence</t> 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
Spectro Temporal Modulation Transfer Functions And Speech Intelligibility, supplied by SPECTRO Analytical, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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HORIBA Ltd intelligent raman module based on confocal microscopy smart raman
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 <t>intelligence</t> 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: 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) .

Techniques: Software