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Paper Citation Record · LEDGER

Preserving instance continuity and length in segmentation through connectivity-aware loss computation

As of 15 August 2026, this Paper Citation Record lists 46 of 46 outbound references and 0 inbound Pith citation observations for arXiv:2509.03154.

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

pith.paper-citation-record.v1
2509.03154 v1

Coverage vector

measured 46 of 46 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T11:10:10.770873Z

measured 46 of 46 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

46 of 46 outbound references displayed

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External citation measurements

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Outbound references

Observation 42eadacc-b6ad-4b85-9140-62b0bf452cd1 · outbound

This paper cites Optimizing the Dice Score and Jaccard Index for Medical Image Segmentation: Theory & Practice.

Preserving instance continuity and length in segmentation through connectivity-aware loss computation Optimizing the Dice Score and Jaccard Index for Medical Image Segmentation: Theory & Practice

Reference 1

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Observation 16c2aa98-d53c-4be6-b4c1-9cb698308f79 · outbound

This paper cites Clough, Israel Valverde, Giovanni Montana, and Andrew P.

Preserving instance continuity and length in segmentation through connectivity-aware loss computation Clough, Israel Valverde, Giovanni Montana, and Andrew P

Reference 2

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This paper cites Light-sheet fluo- rescence expansion microscopy: Fast mapping of neural cir- cuits at super resolution.

Preserving instance continuity and length in segmentation through connectivity-aware loss computation Light-sheet fluo- rescence expansion microscopy: Fast mapping of neural cir- cuits at super resolution

Reference 3

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Observation dc7dffd6-674a-459f-962e-70d67d354248 · outbound

This paper cites Tillberg, and Edward S.

Preserving instance continuity and length in segmentation through connectivity-aware loss computation Tillberg, and Edward S

Reference 4

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Observation 65fe9a83-e6fc-4e95-8ad0-c4df0d243aec · outbound

This paper cites Dumitrescu, Mark D.

Preserving instance continuity and length in segmentation through connectivity-aware loss computation Dumitrescu, Mark D

Reference 5

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This paper cites Topological persistence and simplification.

Preserving instance continuity and length in segmentation through connectivity-aware loss computation Topological persistence and simplification

Reference 6

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Observation 82eea909-1e72-4cc0-99b9-ce9f73f94a08 · outbound

This paper cites Hoogenraad.

Preserving instance continuity and length in segmentation through connectivity-aware loss computation Hoogenraad

Reference 7

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Observation 990f21ad-905f-44ee-803c-53020c99ba36 · outbound

This paper cites Contribution of axon initial segment structure and channels to brain pathology.

Preserving instance continuity and length in segmentation through connectivity-aware loss computation Contribution of axon initial segment structure and channels to brain pathology

Reference 8

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This paper cites Activity-dependent re- location of the axon initial segment fine-tunes neuronal ex- citability.

Preserving instance continuity and length in segmentation through connectivity-aware loss computation Activity-dependent re- location of the axon initial segment fine-tunes neuronal ex- citability

Reference 9

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Observation fddfe9b7-f6a6-4113-b32e-1d074bfbbec1 · outbound

This paper cites Neuroscience: A plastic axonal hotspot.

Preserving instance continuity and length in segmentation through connectivity-aware loss computation Neuroscience: A plastic axonal hotspot

Reference 10

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Observation ec1fa156-e390-439a-9642-9f01a5748ef4 · outbound

This paper cites Impact of loss function in deep learning methods for accurate retinal vessel segmenta- tion, 2022.

Preserving instance continuity and length in segmentation through connectivity-aware loss computation Impact of loss function in deep learning methods for accurate retinal vessel segmenta- tion, 2022

Reference 11

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Observation 1db8c36d-a995-4d3f-99a7-2e76a72a2c0b · outbound

This paper cites Topology-preserving deep image segmentation, 2019.

Preserving instance continuity and length in segmentation through connectivity-aware loss computation Topology-preserving deep image segmentation, 2019

Reference 12

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Observation c850c4a6-1952-4f22-aec7-b1ba9b7d5e8d · outbound

This paper cites Heterogeneity of the axon initial segment in interneurons and pyramidal cells of rodent visual cortex.

Preserving instance continuity and length in segmentation through connectivity-aware loss computation Heterogeneity of the axon initial segment in interneurons and pyramidal cells of rodent visual cortex

Reference 13

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This paper cites Jaeger, Simon Kohl, Jakob Wasserthal, Gregor Koehler, Tobias Norajitra, Sebastian Wirkert, and Klaus H.

Preserving instance continuity and length in segmentation through connectivity-aware loss computation Jaeger, Simon Kohl, Jakob Wasserthal, Gregor Koehler, Tobias Norajitra, Sebastian Wirkert, and Klaus H

Reference 14

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Preserving instance continuity and length in segmentation through connectivity-aware loss computation Unresolved cited work

Reference 15

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Observation c9cfdbd5-13d8-47a2-89bf-73a02828447a · outbound

This paper cites Sensory input drives rapid homeostatic scaling of the axon initial segment in mouse barrel cortex.

Preserving instance continuity and length in segmentation through connectivity-aware loss computation Sensory input drives rapid homeostatic scaling of the axon initial segment in mouse barrel cortex

Reference 16

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Observation 41cbe957-c82b-4e77-bf68-299c234394e5 · outbound

This paper cites Manjunath.

Preserving instance continuity and length in segmentation through connectivity-aware loss computation Manjunath

Reference 17

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Observation 82997fe3-e05f-4dbf-82ac-4fa2a22a2e53 · outbound

This paper cites Iyer, Eric A.

Preserving instance continuity and length in segmentation through connectivity-aware loss computation Iyer, Eric A

Reference 18

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Observation a24833a5-ea71-4c33-81f1-47dad2c8cc54 · outbound

This paper cites Automated sorting of neu- ronal trees in fluorescent images of neuronal networks us- ing neurotreetracer.

Preserving instance continuity and length in segmentation through connectivity-aware loss computation Automated sorting of neu- ronal trees in fluorescent images of neuronal networks us- ing neurotreetracer

Reference 19

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Preserving instance continuity and length in segmentation through connectivity-aware loss computation Dynamic deep net- works for retinal vessel segmentation, 2019

Reference 20

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Preserving instance continuity and length in segmentation through connectivity-aware loss computation Action potential generation requires a high sodium channel density in the axon initial segment

Reference 21

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Observation d3775dff-6b8c-419d-8cfe-28c3fe744d50 · outbound

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Preserving instance continuity and length in segmentation through connectivity-aware loss computation The electrical signifi- cance of axon location diversity

Reference 22

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This paper cites Presynap- tic activity regulates na channel distribution at the axon ini- tial segment.

Preserving instance continuity and length in segmentation through connectivity-aware loss computation Presynap- tic activity regulates na channel distribution at the axon ini- tial segment

Reference 23

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This paper cites Measure anything: Real-time, multi- stage vision-based dimensional measurement using segment anything, 2024.

Preserving instance continuity and length in segmentation through connectivity-aware loss computation Measure anything: Real-time, multi- stage vision-based dimensional measurement using segment anything, 2024

Reference 24

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Observation 2e618d3e-f9b2-44e3-8fc5-26d2b6f007e0 · outbound

This paper cites Alterations in the axon initial segment plasticity is involved in early pathogenesis in alzheimer’s disease.

Preserving instance continuity and length in segmentation through connectivity-aware loss computation Alterations in the axon initial segment plasticity is involved in early pathogenesis in alzheimer’s disease

Reference 25

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This paper cites Berger, Alexander Weers, Nico Stucki, Daniel Rueckert, Ulrich Bauer, and Johannes C.

Preserving instance continuity and length in segmentation through connectivity-aware loss computation Berger, Alexander Weers, Nico Stucki, Daniel Rueckert, Ulrich Bauer, and Johannes C

Reference 26

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Preserving instance continuity and length in segmentation through connectivity-aware loss computation Unresolved cited work

Reference 27

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This paper cites Tizabi, Florian Buettner, Evangelia Christodoulou, Ben Glocker, Fabian Isensee, Jens Kleesiek, Michal Kozubek, Mauricio Reyes, Michael A.

Preserving instance continuity and length in segmentation through connectivity-aware loss computation Tizabi, Florian Buettner, Evangelia Christodoulou, Ben Glocker, Fabian Isensee, Jens Kleesiek, Michal Kozubek, Mauricio Reyes, Michael A

Reference 28

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This paper cites napari: a multi-dimensional image viewer for python, 2019.

Preserving instance continuity and length in segmentation through connectivity-aware loss computation napari: a multi-dimensional image viewer for python, 2019

Reference 29

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This paper cites Topology-aware loss for aorta and great vessel segmentation in computed tomography im- ages, 2024.

Preserving instance continuity and length in segmentation through connectivity-aware loss computation Topology-aware loss for aorta and great vessel segmentation in computed tomography im- ages, 2024

Reference 30

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Preserving instance continuity and length in segmentation through connectivity-aware loss computation The axon initial segment and the main- tenance of neuronal polarity

Reference 31

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Preserving instance continuity and length in segmentation through connectivity-aware loss computation Imaging of the axon initial segment

Reference 32

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Preserving instance continuity and length in segmentation through connectivity-aware loss computation U-net: Convolutional networks for biomedical image segmentation,

Reference 33

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Preserving instance continuity and length in segmentation through connectivity-aware loss computation Fiji: an open-source platform for biological- image analysis

Reference 34

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raw_fallback, observed 2026-08-05T11:10:11.061840Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-05T11:10:10.719240Z digest=sha256:6b6601ad10329058c9dcd503e7000d04b7cdfc72b4ff8aee01be3c3e038c31fd

Observation 0dd688e6-d2f1-4b0a-9cca-395e85c90bf1 · outbound

This paper cites GitHub repos- itory for clDice: Topology-aware Loss Function for Tubu- lar Structure Segmentation.

Preserving instance continuity and length in segmentation through connectivity-aware loss computation GitHub repos- itory for clDice: Topology-aware Loss Function for Tubu- lar Structure Segmentation

Reference 35

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T11:10:11.041099Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-05T11:10:10.723768Z digest=sha256:2d44faf1e8f941c687235507db373e3b1d6391191362a0ca318fce91e69a8c7d

Observation d4b2e86b-909b-4b75-bd5e-bb11ecbde860 · outbound

This paper cites cldice-a novel topology-preserving loss function for tubular structure seg- mentation.

Preserving instance continuity and length in segmentation through connectivity-aware loss computation cldice-a novel topology-preserving loss function for tubular structure seg- mentation

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T11:10:11.011940Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-05T11:10:10.728045Z digest=sha256:d332fe6d303a215b328c1639d8c930637ff532efb3f6158a805aed1d3f4d6d9f

Observation b42bd605-4bde-4f0a-ac0c-37fe08c7157a · outbound

This paper cites Paetzold, and Ul- rich Bauer.

Preserving instance continuity and length in segmentation through connectivity-aware loss computation Paetzold, and Ul- rich Bauer

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T11:10:10.987483Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-05T11:10:10.732111Z digest=sha256:d8685e8f886029e213b62a682122cd04f0966fda81ea0b0badf3c9b8830f2635

Observation 684f08bd-bda6-4f70-8dd3-4cb5ce26ed2d · outbound

This paper cites Live imaging of excitable axonal microdomains in ankyrin-g-gfp mice.

Preserving instance continuity and length in segmentation through connectivity-aware loss computation Live imaging of excitable axonal microdomains in ankyrin-g-gfp mice

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T11:10:10.967395Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-05T11:10:10.736192Z digest=sha256:f5ae4220a5ed3ed87260ae53ed0a7e5442212c508b22e0f41884eb15fa7d36bd

Observation 684db2d8-4050-4f24-844c-39e48bd67f87 · outbound

This paper cites Protein-retention expansion microscopy of cells and tissues labeled using standard fluorescent proteins and antibodies.

Preserving instance continuity and length in segmentation through connectivity-aware loss computation Protein-retention expansion microscopy of cells and tissues labeled using standard fluorescent proteins and antibodies

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T11:10:10.946939Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-05T11:10:10.740500Z digest=sha256:214efe9408baa44064b4da0fcc4388495050dac7d02825294b807aa66876d175

Observation e8fe3096-b261-4451-824e-ce452736d75e · outbound

This paper cites Efficient com- putation of persistent homology for cubical data.

Preserving instance continuity and length in segmentation through connectivity-aware loss computation Efficient com- putation of persistent homology for cubical data

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T11:10:10.922092Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-05T11:10:10.744482Z digest=sha256:3e73bc2e4505626c2162f6d95a367c61b7b52a65ba1937762607baab25cb09ca

Observation 3a878e01-4673-4026-90d2-71dff61d56af · outbound

This paper cites App2: Automatic tracing of 3d neuron morphology based on hierarchical pruning of gray-weighted image distance-trees.

Preserving instance continuity and length in segmentation through connectivity-aware loss computation App2: Automatic tracing of 3d neuron morphology based on hierarchical pruning of gray-weighted image distance-trees

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T11:10:10.905794Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-05T11:10:10.748939Z digest=sha256:0c759c19f91b7ad8ae7ba9954859ef2fd3dfb103f250d2c8701db3bd62003130

Observation f56c6ed1-4c65-4da6-8afd-853af20ed396 · outbound

This paper cites CP-loss: Connectivity-preserving Loss for Road Curb Detection in Autonomous Driving with Aerial Images.

Preserving instance continuity and length in segmentation through connectivity-aware loss computation CP-loss: Connectivity-preserving Loss for Road Curb Detection in Autonomous Driving with Aerial Images

Reference 42

Resolution
verified exact
local_arxiv, observed 2026-08-05T11:10:10.812828Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-05T11:10:10.753459Z digest=sha256:eb4b6e819f9cb7f2e431bfb676d34c15e35742467939b567e71787251bb619a8

Observation 3ef381cd-e5a7-449e-8496-e4dbed45f735 · outbound

This paper cites Angstmann, Yuhuang Wu, Holly Stefen, Esmeralda Pari ´c, Thomas Fath, and Paul M.G.

Preserving instance continuity and length in segmentation through connectivity-aware loss computation Angstmann, Yuhuang Wu, Holly Stefen, Esmeralda Pari ´c, Thomas Fath, and Paul M.G

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T11:10:10.890504Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-05T11:10:10.757886Z digest=sha256:2a6328734795fdc45a3b728300a284e0c9883dbe72b36982434181c74b42c082

Observation e93021bb-850d-4e66-85fd-a00fa97c3a25 · outbound

This paper cites Yermakov, Domenica E.

Preserving instance continuity and length in segmentation through connectivity-aware loss computation Yermakov, Domenica E

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T11:10:10.876752Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-05T11:10:10.762278Z digest=sha256:5933e216fe7a207ccdd2832b41cb07bec130eba867f16d5da225348bf25511c9

Observation 3b220c6b-d57c-4ee5-9976-0f8a21b8ef80 · outbound

This paper cites Axon initial seg- ments: Diverse and dynamic neuronal compartments.

Preserving instance continuity and length in segmentation through connectivity-aware loss computation Axon initial seg- ments: Diverse and dynamic neuronal compartments

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T11:10:10.863094Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-05T11:10:10.766752Z digest=sha256:bac599eba5fb15ff6101cece5cf5bb34177119755a6045c59340e4f8a3696140

Observation 12fd0c43-aa35-4ca2-a6c9-2db36aed6986 · outbound

This paper cites Topological deep learning: a review of an emerging paradigm.

Preserving instance continuity and length in segmentation through connectivity-aware loss computation Topological deep learning: a review of an emerging paradigm

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T11:10:10.847422Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-05T11:10:10.770873Z digest=sha256:f86717dc2b80bb09bc296df8f58e17fd09fa75ca59b8660946492d5d82c8348f

Pith citing papers

No inbound Pith citation observations are available.