Paper Citation Record · LEDGER

Enhancing the automatic segmentation and analysis of 3D liver vasculature models

As of 13 August 2026, this Paper Citation Record lists 23 of 23 outbound references and 0 inbound Pith citation observations for arXiv:2411.15778.

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

pith.paper-citation-record.v1
2411.15778 v4

Coverage vector

measured 23 of 23 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T13:58:40.796353Z

measured 23 of 23 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00

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

23 of 23 outbound references displayed

verified exact7
verified fuzzy3
unresolved8
parse uncertain0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 870b3884-bffb-41b0-a078-b49ffb12b263 · outbound

This paper cites Ali et al., CoRe: An Automated Pipeline for the Prediction of Liver Resec- tion Complexity from Preoperative CT Scans, In: S.T.

Enhancing the automatic segmentation and analysis of 3D liver vasculature models Ali et al., CoRe: An Automated Pipeline for the Prediction of Liver Resec- tion Complexity from Preoperative CT Scans, In: S.T

Reference 1

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Source-reported events for the cited work

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Observation 1f9a665c-116b-4fde-898c-1cd39a0f472c · outbound

This paper cites Rumgay, et al.,Global burden of primary liver cancer in 2020 and predictions to 2040, Journal of Hepatology, vol.

Enhancing the automatic segmentation and analysis of 3D liver vasculature models Rumgay, et al.,Global burden of primary liver cancer in 2020 and predictions to 2040, Journal of Hepatology, vol

Reference 2

Resolution: malformed identifier; raw_fallback, observed 2026-08-12T13:58:42.382936Z

Source-reported events for the cited work

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Observation ec52ac5e-a669-40b5-bee2-09b80b02d3d8 · outbound

This paper cites Pro- moting Connectivity of Network-Like Structures by Enforcing Region Separation,.

Enhancing the automatic segmentation and analysis of 3D liver vasculature models Pro- moting Connectivity of Network-Like Structures by Enforcing Region Separation,

Reference 4

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Source-reported events for the cited work

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Observation 3c122101-2613-48ca-94f2-293b0e7605a3 · outbound

This paper cites an unresolved cited work.

Enhancing the automatic segmentation and analysis of 3D liver vasculature models Unresolved cited work

Reference 5

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Source-reported events for the cited work

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Observation b6502197-ef75-453e-b04b-b0863e6ce30d · outbound

This paper cites TopNet: Topology Preserving Metric Learning for Vessel Tree Reconstruction and Labelling.

Enhancing the automatic segmentation and analysis of 3D liver vasculature models TopNet: Topology Preserving Metric Learning for Vessel Tree Reconstruction and Labelling

Reference 6

Resolution: verified exact; local_arxiv, observed 2026-08-12T13:58:41.764500Z

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Observation 6fb3fa32-810d-4ede-bdf7-93f0a3c71ea9 · outbound

This paper cites an unresolved cited work.

Enhancing the automatic segmentation and analysis of 3D liver vasculature models Unresolved cited work

Reference 7

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Observation 49817a4c-4c67-4900-97b9-f35875dc17d4 · outbound

This paper cites an unresolved cited work.

Enhancing the automatic segmentation and analysis of 3D liver vasculature models Unresolved cited work

Reference 8

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Source-reported events for the cited work

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Observation 87ed7f02-8f58-45f3-9f74-fd6c332264fa · outbound

This paper cites A skeletonization algorithm for gradient-based optimization.

Enhancing the automatic segmentation and analysis of 3D liver vasculature models A skeletonization algorithm for gradient-based optimization

Reference 9

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Observation c061bf75-c5d4-48c9-bc5d-202d1aa1d343 · outbound

This paper cites U-Net based skeletonization and bag of tricks,.

Enhancing the automatic segmentation and analysis of 3D liver vasculature models U-Net based skeletonization and bag of tricks,

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-12T13:58:40.590042Z

Source-reported events for the cited work

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Observation f5e0e5a7-92b3-48a4-ba10-729098b3033d · outbound

This paper cites 1115–1118, 2014, doi:10.1109/ISBI.2014.6868070.

Enhancing the automatic segmentation and analysis of 3D liver vasculature models 1115–1118, 2014, doi:10.1109/ISBI.2014.6868070

Reference 11

Resolution: metadata mismatch; raw_fallback, observed 2026-08-12T13:58:41.370804Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation b0528dd6-35c8-49bc-8e6d-2c12a53ab652 · outbound

This paper cites Bilic et al.,The Liver Tumor Segmentation Benchmark (LiTS), Medical Image Analysis, Volume 84, 2023, 102680, ISSN 1361-8415,https://doi.org/10.1016/ j.media.2022.102680, 2022.

Enhancing the automatic segmentation and analysis of 3D liver vasculature models Bilic et al.,The Liver Tumor Segmentation Benchmark (LiTS), Medical Image Analysis, Volume 84, 2023, 102680, ISSN 1361-8415,https://doi.org/10.1016/ j.media.2022.102680, 2022

Reference 12

Resolution: malformed identifier; no resolver link, observed 2026-08-12T13:58:40.706437Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 87702603-8572-4d7f-8f35-44d5551a2c5b · outbound

This paper cites A new Python library to analyse skeleton images confirms malaria parasite remodelling of the red blood cell membrane skeleton.

Enhancing the automatic segmentation and analysis of 3D liver vasculature models A new Python library to analyse skeleton images confirms malaria parasite remodelling of the red blood cell membrane skeleton

Reference 13

Resolution: verified exact; doi, observed 2026-08-12T13:58:41.033070Z

Source-reported events for the cited work

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Observation d42ad805-e1dd-4937-b59c-75c707ea3d14 · outbound

This paper cites THE PHYSIOLOGICAL PRINCIPLE OF MINIMUM WORK AP- PLIED TO THE ANGLE OF BRANCHING OF ARTERIES.

Enhancing the automatic segmentation and analysis of 3D liver vasculature models THE PHYSIOLOGICAL PRINCIPLE OF MINIMUM WORK AP- PLIED TO THE ANGLE OF BRANCHING OF ARTERIES

Reference 14

Resolution: verified exact; doi, observed 2026-08-12T13:58:40.964183Z

Source-reported events for the cited work

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Observation b5e1c418-525a-4198-9093-43b0059b3a72 · outbound

This paper cites Com- plete removal of the tumor-bearing portal territory decreases local tumor recur- rence and improves disease-specific survival of patients with hepatocellular car- cinoma.

Enhancing the automatic segmentation and analysis of 3D liver vasculature models Com- plete removal of the tumor-bearing portal territory decreases local tumor recur- rence and improves disease-specific survival of patients with hepatocellular car- cinoma

Reference 15

Resolution: verified exact; doi, observed 2026-08-12T13:58:40.948270Z

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Observation 9456de3c-f164-4ede-9a44-c318e007d529 · outbound

This paper cites an unresolved cited work.

Enhancing the automatic segmentation and analysis of 3D liver vasculature models Unresolved cited work

Reference 16

Resolution: verified exact; doi, observed 2026-08-12T13:58:40.930875Z

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Observation 380e93fd-34ca-4722-b41c-13195991bf2c · outbound

This paper cites Lee, R.L.

Enhancing the automatic segmentation and analysis of 3D liver vasculature models Lee, R.L

Reference 17

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Source-reported events for the cited work

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Observation 5ef89fcd-e481-45b0-a522-a8968d3e8d15 · outbound

This paper cites Isensee, P.F.

Enhancing the automatic segmentation and analysis of 3D liver vasculature models Isensee, P.F

Reference 18

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Observation 3d7cdd6b-1639-475c-8413-a3e769a94c63 · outbound

This paper cites Soler, A.

Enhancing the automatic segmentation and analysis of 3D liver vasculature models Soler, A

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:58:42.160228Z

Source-reported events for the cited work

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Observation a04fb375-bd38-4626-abec-137652ce494d · outbound

This paper cites an unresolved cited work.

Enhancing the automatic segmentation and analysis of 3D liver vasculature models Unresolved cited work

Reference 20

Resolution: unresolved; raw_fallback, observed 2026-08-12T13:58:42.120526Z

Source-reported events for the cited work

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Observation 8842dfa7-dc38-4ee3-8c89-0d7020b40fdd · outbound

This paper cites an unresolved cited work.

Enhancing the automatic segmentation and analysis of 3D liver vasculature models Unresolved cited work

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-12T13:58:40.748452Z

Source-reported events for the cited work

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Observation 94e0505a-bae7-401f-a177-fd9f91412640 · outbound

This paper cites Deep learning to achieve clinically applicable segmentation of head and neck anatomy for radiotherapy.

Enhancing the automatic segmentation and analysis of 3D liver vasculature models Deep learning to achieve clinically applicable segmentation of head and neck anatomy for radiotherapy

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-12T13:58:40.753091Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 50b2e9a6-f376-401c-a8c0-d044362b3e15 · outbound

This paper cites Magnetic Resonance Imaging.

Enhancing the automatic segmentation and analysis of 3D liver vasculature models Magnetic Resonance Imaging

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:58:42.046213Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 631bafed-1351-43a6-b35e-2c7e0d5ac588 · outbound

This paper cites SCOPE: Structural Continuity Preservation for Medical Image Segmentation.

Enhancing the automatic segmentation and analysis of 3D liver vasculature models SCOPE: Structural Continuity Preservation for Medical Image Segmentation

Reference 2023

Resolution: verified exact; local_arxiv, observed 2026-08-12T13:58:42.029969Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Pith citing papers

No inbound Pith citation observations are available.