standard two-photon microscope Search Results


two-photon laser point scanning microscopy  (TPLSM laboratories)
90
TPLSM laboratories two-photon laser point scanning microscopy
Two Photon Laser Point Scanning Microscopy, supplied by TPLSM laboratories, 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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standard two-photon system femto2d-dual  (Femtonics inc)
90
Femtonics inc standard two-photon system femto2d-dual
Standard Two Photon System Femto2d Dual, supplied by Femtonics inc, 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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two photon excitation pulses  (Coherent Corp)
99
Coherent Corp two photon excitation pulses
(a) Left column: Standard deviation (SD) images of simulated LFM movie containing temporally active neurons (top) and a single plane from reconstructed SD image (bottom). The brightness values (i.e., magnitude of SD of their activity) of neurons in the SD image is chosen such that dimmest (least active) neuron is 8 times dimmer than the brightest (most active) one (indicated by yellow double arrow). Right column: Same as left column with brightness scaled 5× for clarity. (b) Result of morphological segmentation (Supplementary Note 6) applied to simulated data shown in (a). Individual segments highlighted in different colors. Dimmest neuron (yellow arow) is not segmented successfully and missed. (c) Result of NMF-based segmentation (Supplementary Note 7) applied to simulated data shown in (a). Individual segments highlighted in different colors. Dimmest neuron (yellow arow) is segmented successfully and detected. (d) Runtime comparison of NMF- and morphology-based methods. The morphology-based method is faster by orders of magnitude as the input frame side length increases. (e) Comparison between “ground truth” CaImAn-based segmentation <t>of</t> <t>two-photon</t> microscopy data (first column) to segmentation results obtained from morphological segmentation (second column), NMF-based segmentation (third column), and the combination of both (fourth column), for two different depths (170 μm, top row. 300 μm, bottom row). The purely morphological segmentation approach performs well in segmenting neurons but sometimes misses very dim neurons in the temporal summary image (highlighted with pink arrows). The NMF-based approach on the other hand tends to detect dim neurons more reliably (pink arrows) but comes with the risk of mis-segmenting close-by neurons and assigning them to one component only (yellow arrows). Combining both methods lead to the best results but incurs the largest computational cost of the three approaches since NMF has to be run on the full frames. It should therefore only be used if maximal detection sensitivity is required or if only a subset of the full MesoLF FOV is being used.
Two Photon Excitation Pulses, supplied by Coherent Corp, used in various techniques. Bioz Stars score: 99/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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3d ao laser-scanning microscope atlas  (Femtonics inc)
90
Femtonics inc 3d ao laser-scanning microscope atlas
(a) Left column: Standard deviation (SD) images of simulated LFM movie containing temporally active neurons (top) and a single plane from reconstructed SD image (bottom). The brightness values (i.e., magnitude of SD of their activity) of neurons in the SD image is chosen such that dimmest (least active) neuron is 8 times dimmer than the brightest (most active) one (indicated by yellow double arrow). Right column: Same as left column with brightness scaled 5× for clarity. (b) Result of morphological segmentation (Supplementary Note 6) applied to simulated data shown in (a). Individual segments highlighted in different colors. Dimmest neuron (yellow arow) is not segmented successfully and missed. (c) Result of NMF-based segmentation (Supplementary Note 7) applied to simulated data shown in (a). Individual segments highlighted in different colors. Dimmest neuron (yellow arow) is segmented successfully and detected. (d) Runtime comparison of NMF- and morphology-based methods. The morphology-based method is faster by orders of magnitude as the input frame side length increases. (e) Comparison between “ground truth” CaImAn-based segmentation <t>of</t> <t>two-photon</t> microscopy data (first column) to segmentation results obtained from morphological segmentation (second column), NMF-based segmentation (third column), and the combination of both (fourth column), for two different depths (170 μm, top row. 300 μm, bottom row). The purely morphological segmentation approach performs well in segmenting neurons but sometimes misses very dim neurons in the temporal summary image (highlighted with pink arrows). The NMF-based approach on the other hand tends to detect dim neurons more reliably (pink arrows) but comes with the risk of mis-segmenting close-by neurons and assigning them to one component only (yellow arrows). Combining both methods lead to the best results but incurs the largest computational cost of the three approaches since NMF has to be run on the full frames. It should therefore only be used if maximal detection sensitivity is required or if only a subset of the full MesoLF FOV is being used.
3d Ao Laser Scanning Microscope Atlas, supplied by Femtonics inc, 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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non-linear two-photon microscopy  (TPLSM laboratories)
90
TPLSM laboratories non-linear two-photon microscopy
(a) Left column: Standard deviation (SD) images of simulated LFM movie containing temporally active neurons (top) and a single plane from reconstructed SD image (bottom). The brightness values (i.e., magnitude of SD of their activity) of neurons in the SD image is chosen such that dimmest (least active) neuron is 8 times dimmer than the brightest (most active) one (indicated by yellow double arrow). Right column: Same as left column with brightness scaled 5× for clarity. (b) Result of morphological segmentation (Supplementary Note 6) applied to simulated data shown in (a). Individual segments highlighted in different colors. Dimmest neuron (yellow arow) is not segmented successfully and missed. (c) Result of NMF-based segmentation (Supplementary Note 7) applied to simulated data shown in (a). Individual segments highlighted in different colors. Dimmest neuron (yellow arow) is segmented successfully and detected. (d) Runtime comparison of NMF- and morphology-based methods. The morphology-based method is faster by orders of magnitude as the input frame side length increases. (e) Comparison between “ground truth” CaImAn-based segmentation <t>of</t> <t>two-photon</t> microscopy data (first column) to segmentation results obtained from morphological segmentation (second column), NMF-based segmentation (third column), and the combination of both (fourth column), for two different depths (170 μm, top row. 300 μm, bottom row). The purely morphological segmentation approach performs well in segmenting neurons but sometimes misses very dim neurons in the temporal summary image (highlighted with pink arrows). The NMF-based approach on the other hand tends to detect dim neurons more reliably (pink arrows) but comes with the risk of mis-segmenting close-by neurons and assigning them to one component only (yellow arrows). Combining both methods lead to the best results but incurs the largest computational cost of the three approaches since NMF has to be run on the full frames. It should therefore only be used if maximal detection sensitivity is required or if only a subset of the full MesoLF FOV is being used.
Non Linear Two Photon Microscopy, supplied by TPLSM laboratories, 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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nikon 16  (Nikon)
99
Nikon nikon 16
(a) Left column: Standard deviation (SD) images of simulated LFM movie containing temporally active neurons (top) and a single plane from reconstructed SD image (bottom). The brightness values (i.e., magnitude of SD of their activity) of neurons in the SD image is chosen such that dimmest (least active) neuron is 8 times dimmer than the brightest (most active) one (indicated by yellow double arrow). Right column: Same as left column with brightness scaled 5× for clarity. (b) Result of morphological segmentation (Supplementary Note 6) applied to simulated data shown in (a). Individual segments highlighted in different colors. Dimmest neuron (yellow arow) is not segmented successfully and missed. (c) Result of NMF-based segmentation (Supplementary Note 7) applied to simulated data shown in (a). Individual segments highlighted in different colors. Dimmest neuron (yellow arow) is segmented successfully and detected. (d) Runtime comparison of NMF- and morphology-based methods. The morphology-based method is faster by orders of magnitude as the input frame side length increases. (e) Comparison between “ground truth” CaImAn-based segmentation <t>of</t> <t>two-photon</t> microscopy data (first column) to segmentation results obtained from morphological segmentation (second column), NMF-based segmentation (third column), and the combination of both (fourth column), for two different depths (170 μm, top row. 300 μm, bottom row). The purely morphological segmentation approach performs well in segmenting neurons but sometimes misses very dim neurons in the temporal summary image (highlighted with pink arrows). The NMF-based approach on the other hand tends to detect dim neurons more reliably (pink arrows) but comes with the risk of mis-segmenting close-by neurons and assigning them to one component only (yellow arrows). Combining both methods lead to the best results but incurs the largest computational cost of the three approaches since NMF has to be run on the full frames. It should therefore only be used if maximal detection sensitivity is required or if only a subset of the full MesoLF FOV is being used.
Nikon 16, supplied by Nikon, used in various techniques. Bioz Stars score: 99/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/standard+two-photon+microscope/Objectives/pmc05604087-126-14-14
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resonant microscope  (Thorlabs)
86
Thorlabs resonant microscope
(a) Left column: Standard deviation (SD) images of simulated LFM movie containing temporally active neurons (top) and a single plane from reconstructed SD image (bottom). The brightness values (i.e., magnitude of SD of their activity) of neurons in the SD image is chosen such that dimmest (least active) neuron is 8 times dimmer than the brightest (most active) one (indicated by yellow double arrow). Right column: Same as left column with brightness scaled 5× for clarity. (b) Result of morphological segmentation (Supplementary Note 6) applied to simulated data shown in (a). Individual segments highlighted in different colors. Dimmest neuron (yellow arow) is not segmented successfully and missed. (c) Result of NMF-based segmentation (Supplementary Note 7) applied to simulated data shown in (a). Individual segments highlighted in different colors. Dimmest neuron (yellow arow) is segmented successfully and detected. (d) Runtime comparison of NMF- and morphology-based methods. The morphology-based method is faster by orders of magnitude as the input frame side length increases. (e) Comparison between “ground truth” CaImAn-based segmentation <t>of</t> <t>two-photon</t> microscopy data (first column) to segmentation results obtained from morphological segmentation (second column), NMF-based segmentation (third column), and the combination of both (fourth column), for two different depths (170 μm, top row. 300 μm, bottom row). The purely morphological segmentation approach performs well in segmenting neurons but sometimes misses very dim neurons in the temporal summary image (highlighted with pink arrows). The NMF-based approach on the other hand tends to detect dim neurons more reliably (pink arrows) but comes with the risk of mis-segmenting close-by neurons and assigning them to one component only (yellow arrows). Combining both methods lead to the best results but incurs the largest computational cost of the three approaches since NMF has to be run on the full frames. It should therefore only be used if maximal detection sensitivity is required or if only a subset of the full MesoLF FOV is being used.
Resonant Microscope, supplied by Thorlabs, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/standard+two-photon+microscope/b+resonant+scanner+scope/bio_rxiv__64898__2025__12__21__695778-196-8-11
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resonant scanning  (Thorlabs)
86
Thorlabs resonant scanning
(a) Left column: Standard deviation (SD) images of simulated LFM movie containing temporally active neurons (top) and a single plane from reconstructed SD image (bottom). The brightness values (i.e., magnitude of SD of their activity) of neurons in the SD image is chosen such that dimmest (least active) neuron is 8 times dimmer than the brightest (most active) one (indicated by yellow double arrow). Right column: Same as left column with brightness scaled 5× for clarity. (b) Result of morphological segmentation (Supplementary Note 6) applied to simulated data shown in (a). Individual segments highlighted in different colors. Dimmest neuron (yellow arow) is not segmented successfully and missed. (c) Result of NMF-based segmentation (Supplementary Note 7) applied to simulated data shown in (a). Individual segments highlighted in different colors. Dimmest neuron (yellow arow) is segmented successfully and detected. (d) Runtime comparison of NMF- and morphology-based methods. The morphology-based method is faster by orders of magnitude as the input frame side length increases. (e) Comparison between “ground truth” CaImAn-based segmentation <t>of</t> <t>two-photon</t> microscopy data (first column) to segmentation results obtained from morphological segmentation (second column), NMF-based segmentation (third column), and the combination of both (fourth column), for two different depths (170 μm, top row. 300 μm, bottom row). The purely morphological segmentation approach performs well in segmenting neurons but sometimes misses very dim neurons in the temporal summary image (highlighted with pink arrows). The NMF-based approach on the other hand tends to detect dim neurons more reliably (pink arrows) but comes with the risk of mis-segmenting close-by neurons and assigning them to one component only (yellow arrows). Combining both methods lead to the best results but incurs the largest computational cost of the three approaches since NMF has to be run on the full frames. It should therefore only be used if maximal detection sensitivity is required or if only a subset of the full MesoLF FOV is being used.
Resonant Scanning, supplied by Thorlabs, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/standard+two-photon+microscope/microscope+photon+two/pm41436654-429-13-18
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resonant microscope  (Scientifica)
86
Scientifica resonant microscope
(a) Left column: Standard deviation (SD) images of simulated LFM movie containing temporally active neurons (top) and a single plane from reconstructed SD image (bottom). The brightness values (i.e., magnitude of SD of their activity) of neurons in the SD image is chosen such that dimmest (least active) neuron is 8 times dimmer than the brightest (most active) one (indicated by yellow double arrow). Right column: Same as left column with brightness scaled 5× for clarity. (b) Result of morphological segmentation (Supplementary Note 6) applied to simulated data shown in (a). Individual segments highlighted in different colors. Dimmest neuron (yellow arow) is not segmented successfully and missed. (c) Result of NMF-based segmentation (Supplementary Note 7) applied to simulated data shown in (a). Individual segments highlighted in different colors. Dimmest neuron (yellow arow) is segmented successfully and detected. (d) Runtime comparison of NMF- and morphology-based methods. The morphology-based method is faster by orders of magnitude as the input frame side length increases. (e) Comparison between “ground truth” CaImAn-based segmentation <t>of</t> <t>two-photon</t> microscopy data (first column) to segmentation results obtained from morphological segmentation (second column), NMF-based segmentation (third column), and the combination of both (fourth column), for two different depths (170 μm, top row. 300 μm, bottom row). The purely morphological segmentation approach performs well in segmenting neurons but sometimes misses very dim neurons in the temporal summary image (highlighted with pink arrows). The NMF-based approach on the other hand tends to detect dim neurons more reliably (pink arrows) but comes with the risk of mis-segmenting close-by neurons and assigning them to one component only (yellow arrows). Combining both methods lead to the best results but incurs the largest computational cost of the three approaches since NMF has to be run on the full frames. It should therefore only be used if maximal detection sensitivity is required or if only a subset of the full MesoLF FOV is being used.
Resonant Microscope, supplied by Scientifica, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/standard+two-photon+microscope/2p+laser+microscope+scanning/bio_rxiv__64898__2026__04__21__719991-131-12-15
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photomultiplier tube  (Hamamatsu)
90
Hamamatsu photomultiplier tube
(a) Left column: Standard deviation (SD) images of simulated LFM movie containing temporally active neurons (top) and a single plane from reconstructed SD image (bottom). The brightness values (i.e., magnitude of SD of their activity) of neurons in the SD image is chosen such that dimmest (least active) neuron is 8 times dimmer than the brightest (most active) one (indicated by yellow double arrow). Right column: Same as left column with brightness scaled 5× for clarity. (b) Result of morphological segmentation (Supplementary Note 6) applied to simulated data shown in (a). Individual segments highlighted in different colors. Dimmest neuron (yellow arow) is not segmented successfully and missed. (c) Result of NMF-based segmentation (Supplementary Note 7) applied to simulated data shown in (a). Individual segments highlighted in different colors. Dimmest neuron (yellow arow) is segmented successfully and detected. (d) Runtime comparison of NMF- and morphology-based methods. The morphology-based method is faster by orders of magnitude as the input frame side length increases. (e) Comparison between “ground truth” CaImAn-based segmentation <t>of</t> <t>two-photon</t> microscopy data (first column) to segmentation results obtained from morphological segmentation (second column), NMF-based segmentation (third column), and the combination of both (fourth column), for two different depths (170 μm, top row. 300 μm, bottom row). The purely morphological segmentation approach performs well in segmenting neurons but sometimes misses very dim neurons in the temporal summary image (highlighted with pink arrows). The NMF-based approach on the other hand tends to detect dim neurons more reliably (pink arrows) but comes with the risk of mis-segmenting close-by neurons and assigning them to one component only (yellow arrows). Combining both methods lead to the best results but incurs the largest computational cost of the three approaches since NMF has to be run on the full frames. It should therefore only be used if maximal detection sensitivity is required or if only a subset of the full MesoLF FOV is being used.
Photomultiplier Tube, supplied by Hamamatsu, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/standard+two-photon+microscope/photomultiplier+tube/10__1002_slash_adom__202101649-219-19-21
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multiphoton imaging microscope  (Thorlabs)
86
Thorlabs multiphoton imaging microscope
(a) Left column: Standard deviation (SD) images of simulated LFM movie containing temporally active neurons (top) and a single plane from reconstructed SD image (bottom). The brightness values (i.e., magnitude of SD of their activity) of neurons in the SD image is chosen such that dimmest (least active) neuron is 8 times dimmer than the brightest (most active) one (indicated by yellow double arrow). Right column: Same as left column with brightness scaled 5× for clarity. (b) Result of morphological segmentation (Supplementary Note 6) applied to simulated data shown in (a). Individual segments highlighted in different colors. Dimmest neuron (yellow arow) is not segmented successfully and missed. (c) Result of NMF-based segmentation (Supplementary Note 7) applied to simulated data shown in (a). Individual segments highlighted in different colors. Dimmest neuron (yellow arow) is segmented successfully and detected. (d) Runtime comparison of NMF- and morphology-based methods. The morphology-based method is faster by orders of magnitude as the input frame side length increases. (e) Comparison between “ground truth” CaImAn-based segmentation <t>of</t> <t>two-photon</t> microscopy data (first column) to segmentation results obtained from morphological segmentation (second column), NMF-based segmentation (third column), and the combination of both (fourth column), for two different depths (170 μm, top row. 300 μm, bottom row). The purely morphological segmentation approach performs well in segmenting neurons but sometimes misses very dim neurons in the temporal summary image (highlighted with pink arrows). The NMF-based approach on the other hand tends to detect dim neurons more reliably (pink arrows) but comes with the risk of mis-segmenting close-by neurons and assigning them to one component only (yellow arrows). Combining both methods lead to the best results but incurs the largest computational cost of the three approaches since NMF has to be run on the full frames. It should therefore only be used if maximal detection sensitivity is required or if only a subset of the full MesoLF FOV is being used.
Multiphoton Imaging Microscope, supplied by Thorlabs, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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glass microcuvettes inner diameter 50 m  (VitroCom Inc)
90
VitroCom Inc glass microcuvettes inner diameter 50 m
(a) Left column: Standard deviation (SD) images of simulated LFM movie containing temporally active neurons (top) and a single plane from reconstructed SD image (bottom). The brightness values (i.e., magnitude of SD of their activity) of neurons in the SD image is chosen such that dimmest (least active) neuron is 8 times dimmer than the brightest (most active) one (indicated by yellow double arrow). Right column: Same as left column with brightness scaled 5× for clarity. (b) Result of morphological segmentation (Supplementary Note 6) applied to simulated data shown in (a). Individual segments highlighted in different colors. Dimmest neuron (yellow arow) is not segmented successfully and missed. (c) Result of NMF-based segmentation (Supplementary Note 7) applied to simulated data shown in (a). Individual segments highlighted in different colors. Dimmest neuron (yellow arow) is segmented successfully and detected. (d) Runtime comparison of NMF- and morphology-based methods. The morphology-based method is faster by orders of magnitude as the input frame side length increases. (e) Comparison between “ground truth” CaImAn-based segmentation <t>of</t> <t>two-photon</t> microscopy data (first column) to segmentation results obtained from morphological segmentation (second column), NMF-based segmentation (third column), and the combination of both (fourth column), for two different depths (170 μm, top row. 300 μm, bottom row). The purely morphological segmentation approach performs well in segmenting neurons but sometimes misses very dim neurons in the temporal summary image (highlighted with pink arrows). The NMF-based approach on the other hand tends to detect dim neurons more reliably (pink arrows) but comes with the risk of mis-segmenting close-by neurons and assigning them to one component only (yellow arrows). Combining both methods lead to the best results but incurs the largest computational cost of the three approaches since NMF has to be run on the full frames. It should therefore only be used if maximal detection sensitivity is required or if only a subset of the full MesoLF FOV is being used.
Glass Microcuvettes Inner Diameter 50 M, supplied by VitroCom Inc, 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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Image Search Results


(a) Left column: Standard deviation (SD) images of simulated LFM movie containing temporally active neurons (top) and a single plane from reconstructed SD image (bottom). The brightness values (i.e., magnitude of SD of their activity) of neurons in the SD image is chosen such that dimmest (least active) neuron is 8 times dimmer than the brightest (most active) one (indicated by yellow double arrow). Right column: Same as left column with brightness scaled 5× for clarity. (b) Result of morphological segmentation (Supplementary Note 6) applied to simulated data shown in (a). Individual segments highlighted in different colors. Dimmest neuron (yellow arow) is not segmented successfully and missed. (c) Result of NMF-based segmentation (Supplementary Note 7) applied to simulated data shown in (a). Individual segments highlighted in different colors. Dimmest neuron (yellow arow) is segmented successfully and detected. (d) Runtime comparison of NMF- and morphology-based methods. The morphology-based method is faster by orders of magnitude as the input frame side length increases. (e) Comparison between “ground truth” CaImAn-based segmentation of two-photon microscopy data (first column) to segmentation results obtained from morphological segmentation (second column), NMF-based segmentation (third column), and the combination of both (fourth column), for two different depths (170 μm, top row. 300 μm, bottom row). The purely morphological segmentation approach performs well in segmenting neurons but sometimes misses very dim neurons in the temporal summary image (highlighted with pink arrows). The NMF-based approach on the other hand tends to detect dim neurons more reliably (pink arrows) but comes with the risk of mis-segmenting close-by neurons and assigning them to one component only (yellow arrows). Combining both methods lead to the best results but incurs the largest computational cost of the three approaches since NMF has to be run on the full frames. It should therefore only be used if maximal detection sensitivity is required or if only a subset of the full MesoLF FOV is being used.

Journal: Nature methods

Article Title: Mesoscale volumetric light field (MesoLF) imaging of neuroactivity across cortical areas at 18 Hz

doi: 10.1038/s41592-023-01789-z

Figure Lengend Snippet: (a) Left column: Standard deviation (SD) images of simulated LFM movie containing temporally active neurons (top) and a single plane from reconstructed SD image (bottom). The brightness values (i.e., magnitude of SD of their activity) of neurons in the SD image is chosen such that dimmest (least active) neuron is 8 times dimmer than the brightest (most active) one (indicated by yellow double arrow). Right column: Same as left column with brightness scaled 5× for clarity. (b) Result of morphological segmentation (Supplementary Note 6) applied to simulated data shown in (a). Individual segments highlighted in different colors. Dimmest neuron (yellow arow) is not segmented successfully and missed. (c) Result of NMF-based segmentation (Supplementary Note 7) applied to simulated data shown in (a). Individual segments highlighted in different colors. Dimmest neuron (yellow arow) is segmented successfully and detected. (d) Runtime comparison of NMF- and morphology-based methods. The morphology-based method is faster by orders of magnitude as the input frame side length increases. (e) Comparison between “ground truth” CaImAn-based segmentation of two-photon microscopy data (first column) to segmentation results obtained from morphological segmentation (second column), NMF-based segmentation (third column), and the combination of both (fourth column), for two different depths (170 μm, top row. 300 μm, bottom row). The purely morphological segmentation approach performs well in segmenting neurons but sometimes misses very dim neurons in the temporal summary image (highlighted with pink arrows). The NMF-based approach on the other hand tends to detect dim neurons more reliably (pink arrows) but comes with the risk of mis-segmenting close-by neurons and assigning them to one component only (yellow arrows). Combining both methods lead to the best results but incurs the largest computational cost of the three approaches since NMF has to be run on the full frames. It should therefore only be used if maximal detection sensitivity is required or if only a subset of the full MesoLF FOV is being used.

Article Snippet: Two-photon excitation pulses (920 nm, 140 fs pulse duration, 80 MHz pulse rate, Coherent Chameleon) were focused into mouse cortex and scanned in planes parallel to the cranial window at a series of depths, via the Slicescope’s galvo-galvo scan path and a Nikon 16×/0.8NA objective mounted on a motorized stage that allowed for axial translation.

Techniques: Standard Deviation, Activity Assay, Comparison, Microscopy