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( A ) Raw slices of the vascular channel with the neuronal channel subtracted to facilitate vessel visualization. ( B ) Corresponding slices of the segmentation mask generated <t>in</t> <t>Imaris</t> <t>9.2.1</t> manually by a rater overlaid onto the raw data. ( C ) Corresponding segmentation masks generated by our UNETR segmentation model overlaid onto the raw data.
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( A ) Raw slices of the vascular channel with the neuronal channel subtracted to facilitate vessel visualization. ( B ) Corresponding slices of the segmentation mask generated in Imaris 9.2.1 manually by a rater overlaid onto the raw data. ( C ) Corresponding segmentation masks generated by our UNETR segmentation model overlaid onto the raw data.

Journal: eLife

Article Title: A deep learning pipeline for mapping in situ network-level neurovascular coupling in multi-photon fluorescence microscopy

doi: 10.7554/eLife.95525

Figure Lengend Snippet: ( A ) Raw slices of the vascular channel with the neuronal channel subtracted to facilitate vessel visualization. ( B ) Corresponding slices of the segmentation mask generated in Imaris 9.2.1 manually by a rater overlaid onto the raw data. ( C ) Corresponding segmentation masks generated by our UNETR segmentation model overlaid onto the raw data.

Article Snippet: To compare our results with those achievable on these data with other pipelines for segmentation and graph network extraction, we compared segmentation results qualitatively with Imaris version 9.2.1 (Bitplane) and vascular graph extraction with VesselVio ( ).

Techniques: Generated