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Paper Citation Record · LEDGER

DISCo: Deep learning, Instance Segmentation, and Correlations for cell segmentation in calcium imaging

As of 16 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 0 inbound Pith citation observations for arXiv:1908.07957.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
1908.07957 v4

Coverage vector

measured 43 of 43 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-14T11:58:48.047033Z

measured 43 of 43 standing notices

One-hop event checks from named stored sources.

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measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

43 of 43 outbound references displayed

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External citation measurements

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Outbound references

Observation 3dc2a4b1-d828-4d53-8b73-4cb847aa8f9c · outbound

This paper cites Strickler, and Watt W.

DISCo: Deep learning, Instance Segmentation, and Correlations for cell segmentation in calcium imaging Strickler, and Watt W

Reference 1

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Observation 5872a69f-abfd-43a3-a237-d2cfe53702aa · outbound

This paper cites Deep tissue two-photon microscopy.

DISCo: Deep learning, Instance Segmentation, and Correlations for cell segmentation in calcium imaging Deep tissue two-photon microscopy

Reference 2

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Observation 4cad2b3d-626c-47bc-875b-c800014dcd8d · outbound

This paper cites High-speed, miniaturized fluorescence microscopy in freely moving mice.

DISCo: Deep learning, Instance Segmentation, and Correlations for cell segmentation in calcium imaging High-speed, miniaturized fluorescence microscopy in freely moving mice

Reference 3

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Observation f7133e66-ac17-4178-a1f6-b245c4860105 · outbound

This paper cites Hamprecht.

DISCo: Deep learning, Instance Segmentation, and Correlations for cell segmentation in calcium imaging Hamprecht

Reference 4

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Observation da26e16b-5a52-4e7b-a88f-fb2e7a93b734 · outbound

This paper cites Neurofinder public benchmark.

DISCo: Deep learning, Instance Segmentation, and Correlations for cell segmentation in calcium imaging Neurofinder public benchmark

Reference 5

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Observation c92429a7-35bc-4ad5-91c5-424ac2c13063 · outbound

This paper cites Chet- tih, Matthias Minderer, Christopher Harvey, and Dorit S.

DISCo: Deep learning, Instance Segmentation, and Correlations for cell segmentation in calcium imaging Chet- tih, Matthias Minderer, Christopher Harvey, and Dorit S

Reference 6

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Observation 23d94d50-3ff0-4778-86ec-8b812acfb1fd · outbound

This paper cites Automated analysis of cellular signals from large-scale cal- cium imaging data.

DISCo: Deep learning, Instance Segmentation, and Correlations for cell segmentation in calcium imaging Automated analysis of cellular signals from large-scale cal- cium imaging data

Reference 7

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Observation aa8dd0ad-c637-4619-940d-c2017d77b90c · outbound

This paper cites Sparse non- negative deconvolution for compressive calcium imaging: algorithms and phase transitions.

DISCo: Deep learning, Instance Segmentation, and Correlations for cell segmentation in calcium imaging Sparse non- negative deconvolution for compressive calcium imaging: algorithms and phase transitions

Reference 8

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Observation e762603e-1dc7-411c-a6c4-72803523e915 · outbound

This paper cites Rank-penalized nonnegative spatiotemporal deconvolu- tion and demixing of calcium imaging data.

DISCo: Deep learning, Instance Segmentation, and Correlations for cell segmentation in calcium imaging Rank-penalized nonnegative spatiotemporal deconvolu- tion and demixing of calcium imaging data

Reference 9

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Observation 1c02becb-88b4-4223-98c1-097676358901 · outbound

This paper cites A structured matrix factorization framework for large scale calcium imaging data analysis.

DISCo: Deep learning, Instance Segmentation, and Correlations for cell segmentation in calcium imaging A structured matrix factorization framework for large scale calcium imaging data analysis

Reference 10

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Observation c8663e4c-42c5-4d60-9d86-479d419b3811 · outbound

This paper cites Sparse space-time deconvolution for calcium image analysis.

DISCo: Deep learning, Instance Segmentation, and Correlations for cell segmentation in calcium imaging Sparse space-time deconvolution for calcium image analysis

Reference 11

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Observation 3182401c-cd9b-466b-9505-6c9e53034074 · outbound

This paper cites Detecting cells using non-negative matrix factorization on calcium imaging data.

DISCo: Deep learning, Instance Segmentation, and Correlations for cell segmentation in calcium imaging Detecting cells using non-negative matrix factorization on calcium imaging data

Reference 12

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This paper cites Pnevmatikakis, Daniel Soudry, Yuanjun Gao, Timothy A.

DISCo: Deep learning, Instance Segmentation, and Correlations for cell segmentation in calcium imaging Pnevmatikakis, Daniel Soudry, Yuanjun Gao, Timothy A

Reference 13

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Observation abc0767d-8c71-4fdd-9243-87e36fb2b2f3 · outbound

This paper cites Fast online deconvolution of calcium imaging data.

DISCo: Deep learning, Instance Segmentation, and Correlations for cell segmentation in calcium imaging Fast online deconvolution of calcium imaging data

Reference 14

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Observation 708216f1-ed49-4573-9994-da7489f0676b · outbound

This paper cites Erdogdu, and Mark Schnitzer.

DISCo: Deep learning, Instance Segmentation, and Correlations for cell segmentation in calcium imaging Erdogdu, and Mark Schnitzer

Reference 15

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Observation d0af82f4-7437-456f-b31b-98d55aca13d5 · outbound

This paper cites Onacid: Online analysis of cal- cium imaging data in real time.

DISCo: Deep learning, Instance Segmentation, and Correlations for cell segmentation in calcium imaging Onacid: Online analysis of cal- cium imaging data in real time

Reference 16

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Observation fad02bf4-f9b4-4da4-99ee-8507bcc87aae · outbound

This paper cites Efficient and accurate extraction of in vivo calcium signals from microen- doscopic video data.

DISCo: Deep learning, Instance Segmentation, and Correlations for cell segmentation in calcium imaging Efficient and accurate extraction of in vivo calcium signals from microen- doscopic video data

Reference 17

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This paper cites Caiman an open source tool for scalable calcium imaging data analysis.

DISCo: Deep learning, Instance Segmentation, and Correlations for cell segmentation in calcium imaging Caiman an open source tool for scalable calcium imaging data analysis

Reference 18

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This paper cites SIMA: Python software for analysis of dynamic fluorescence imaging data.

DISCo: Deep learning, Instance Segmentation, and Correlations for cell segmentation in calcium imaging SIMA: Python software for analysis of dynamic fluorescence imaging data

Reference 19

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This paper cites Hamprecht.

DISCo: Deep learning, Instance Segmentation, and Correlations for cell segmentation in calcium imaging Hamprecht

Reference 20

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This paper cites Learning multi-level sparse representations.

DISCo: Deep learning, Instance Segmentation, and Correlations for cell segmentation in calcium imaging Learning multi-level sparse representations

Reference 21

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Observation cb6d2e40-f7f9-45d2-902e-5fc290622366 · outbound

This paper cites Extracting regions of interest from biological images with convolutional sparse block coding.

DISCo: Deep learning, Instance Segmentation, and Correlations for cell segmentation in calcium imaging Extracting regions of interest from biological images with convolutional sparse block coding

Reference 22

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Observation 537a1115-c73b-47e3-a26a-5b310b4b0321 · outbound

This paper cites Scalpel: Extracting neurons from calcium imaging data.

DISCo: Deep learning, Instance Segmentation, and Correlations for cell segmentation in calcium imaging Scalpel: Extracting neurons from calcium imaging data

Reference 23

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This paper cites Apthorpe, Alexander J.

DISCo: Deep learning, Instance Segmentation, and Correlations for cell segmentation in calcium imaging Apthorpe, Alexander J

Reference 24

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This paper cites Fast, simple calcium imaging segmentation with fully convolutional networks.

DISCo: Deep learning, Instance Segmentation, and Correlations for cell segmentation in calcium imaging Fast, simple calcium imaging segmentation with fully convolutional networks

Reference 25

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This paper cites Fast and robust active neu- ron segmentation in two-photon calcium imaging using spa- tiotemporal deep learning.

DISCo: Deep learning, Instance Segmentation, and Correlations for cell segmentation in calcium imaging Fast and robust active neu- ron segmentation in two-photon calcium imaging using spa- tiotemporal deep learning

Reference 26

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DISCo: Deep learning, Instance Segmentation, and Correlations for cell segmentation in calcium imaging Unresolved cited work

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DISCo: Deep learning, Instance Segmentation, and Correlations for cell segmentation in calcium imaging Suite2p: beyond 10,000 neu- rons with standard two-photon microscopy

Reference 28

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DISCo: Deep learning, Instance Segmentation, and Correlations for cell segmentation in calcium imaging Allen Brain Observatory (ABO) datasets

Reference 29

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This paper cites Scaled correlation analysis: a better way to compute a cross-correlogram.

DISCo: Deep learning, Instance Segmentation, and Correlations for cell segmentation in calcium imaging Scaled correlation analysis: a better way to compute a cross-correlogram

Reference 30

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This paper cites Synchronisation hubs in the visual cor- tex may arise from strong rhythmic inhibition during gamma oscillations.

DISCo: Deep learning, Instance Segmentation, and Correlations for cell segmentation in calcium imaging Synchronisation hubs in the visual cor- tex may arise from strong rhythmic inhibition during gamma oscillations

Reference 31

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This paper cites A scaled- correlation based approach for defining and analyzing func- tional networks.

DISCo: Deep learning, Instance Segmentation, and Correlations for cell segmentation in calcium imaging A scaled- correlation based approach for defining and analyzing func- tional networks

Reference 32

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DISCo: Deep learning, Instance Segmentation, and Correlations for cell segmentation in calcium imaging Notes on regression and inheritance in the case of two parents

Reference 33

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DISCo: Deep learning, Instance Segmentation, and Correlations for cell segmentation in calcium imaging Measur- ing and testing dependence by correlation of distances

Reference 34

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation e984f9b7-f4f5-4ef3-8594-cea5c634dee4 · outbound

This paper cites U-net: Convolutional networks for biomedical image segmentation.

DISCo: Deep learning, Instance Segmentation, and Correlations for cell segmentation in calcium imaging U-net: Convolutional networks for biomedical image segmentation

Reference 35

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation b0252416-1cef-4272-aaa8-9a8d20a76ae8 · outbound

This paper cites Measures of the amount of ecologic association between species.

DISCo: Deep learning, Instance Segmentation, and Correlations for cell segmentation in calcium imaging Measures of the amount of ecologic association between species

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T11:58:48.146621Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation 23dfbf65-6efe-40bf-93ee-1c9c6c9cbfc6 · outbound

This paper cites A method of establishing groups of equal amplitude in plant sociology based on similarity of species content and its application to analyses of the vegeta- tion on danish commons.

DISCo: Deep learning, Instance Segmentation, and Correlations for cell segmentation in calcium imaging A method of establishing groups of equal amplitude in plant sociology based on similarity of species content and its application to analyses of the vegeta- tion on danish commons

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T11:58:48.138801Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation 3fbf5965-ee21-4065-bc47-cf9687bc7259 · outbound

This paper cites Ham- precht.

DISCo: Deep learning, Instance Segmentation, and Correlations for cell segmentation in calcium imaging Ham- precht

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T11:58:48.130889Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T11:58:48.036231Z digest=sha256:39fee646564f80a116cd22d69ce52ed19596637669c2ab984798bda68509b34f

Observation e4b97725-c04c-4936-b527-cf59681d4b63 · outbound

This paper cites GASP, a generalized framework for agglomerative clustering of signed graphs and its application to Instance Segmentation.

DISCo: Deep learning, Instance Segmentation, and Correlations for cell segmentation in calcium imaging GASP, a generalized framework for agglomerative clustering of signed graphs and its application to Instance Segmentation

Reference 39

Resolution
unresolved
no resolver link, observed 2026-08-14T11:58:48.039051Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-14T11:58:48.039051Z digest=sha256:1b44e54f022b9dfd48a0221fe005875e28cb441830269abb7c71f90e7c293a42

Observation e279ccb5-179e-430a-9388-a425d025eba1 · outbound

This paper cites Efficient decomposition of image and mesh graphs by lifted multi- cuts.

DISCo: Deep learning, Instance Segmentation, and Correlations for cell segmentation in calcium imaging Efficient decomposition of image and mesh graphs by lifted multi- cuts

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T11:58:48.122531Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation 5df64765-bc15-47ea-8969-5c838ebf855c · outbound

This paper cites A comparative study of local search algorithms for correlation clustering.

DISCo: Deep learning, Instance Segmentation, and Correlations for cell segmentation in calcium imaging A comparative study of local search algorithms for correlation clustering

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T11:58:48.113435Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T11:58:48.044554Z digest=sha256:f13fddad1b3656b117ebe6805eaa15c8a97e9f97ab787f6e3e1e6d6e93b6932d

Observation 6d532868-bdc1-4d68-8167-fb7473a23e1b · outbound

This paper cites Adam: A method for stochastic optimization.

DISCo: Deep learning, Instance Segmentation, and Correlations for cell segmentation in calcium imaging Adam: A method for stochastic optimization

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T11:58:48.104919Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation cb2a5309-82b7-4df6-a96e-54586154756c · outbound

This paper cites an unresolved cited work.

DISCo: Deep learning, Instance Segmentation, and Correlations for cell segmentation in calcium imaging Unresolved cited work

Reference 2019

Resolution
unresolved
raw_fallback, observed 2026-08-14T11:58:48.359106Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Pith citing papers

No inbound Pith citation observations are available.