Paper Citation Record · LEDGER

ContextLoss: Context Information for Topology-Preserving Segmentation

As of 22 August 2026, this Paper Citation Record lists 30 of 30 outbound references and 1 inbound Pith citation observation for arXiv:2506.11134.

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

pith.paper-citation-record.v1
2506.11134 v1

Coverage vector

measured 30 of 30 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T05:00:16.244771Z

measured 31 of 31 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T05:00:16.155098Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T05:00:16.310155Z

Reference resolution

30 of 30 outbound references displayed

verified exact1
verified fuzzy27
unresolved1
parse uncertain0
malformed identifier0
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 45974c49-639c-49c5-81c0-4a66ca430af1 · outbound

This paper cites ContextLoss: Context Information for Topology-Preserving Segmentation.

ContextLoss: Context Information for Topology-Preserving Segmentation ContextLoss: Context Information for Topology-Preserving Segmentation

Reference 1

Resolution: metadata mismatch; local_arxiv, observed 2026-08-07T05:00:16.314790Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 6b82f5fc-f15c-47c2-bdd2-c9da03608011 · outbound

This paper cites ContextLoss We propose the novel loss function CLoss to promote topology- preserving segmentation with arbitrary segmentation net- works.

ContextLoss: Context Information for Topology-Preserving Segmentation ContextLoss We propose the novel loss function CLoss to promote topology- preserving segmentation with arbitrary segmentation net- works

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:00:16.636439Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 979f9ff6-3295-41be-9b4b-43a91a706dd2 · outbound

This paper cites 4: Evaluation artifacts.

ContextLoss: Context Information for Topology-Preserving Segmentation 4: Evaluation artifacts

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:00:16.624484Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 95adcc0d-a9ec-4d21-91d9-dbd8e9425bea · outbound

This paper cites Their topology is a key property for their function [15, 16], there- fore automated topology-preserving segmentation creates a Table 1: Quantitative results.

ContextLoss: Context Information for Topology-Preserving Segmentation Their topology is a key property for their function [15, 16], there- fore automated topology-preserving segmentation creates a Table 1: Quantitative results

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:00:16.614145Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation ee927d65-55af-48d0-8ebc-c2520fadb2e3 · outbound

This paper cites Following other work on topology-preserving segmentation [3–8, 13], we use the two public 2D datasets, Massachusetts Roads (Roads) [19] and HRF-Retina [20].

ContextLoss: Context Information for Topology-Preserving Segmentation Following other work on topology-preserving segmentation [3–8, 13], we use the two public 2D datasets, Massachusetts Roads (Roads) [19] and HRF-Retina [20]

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:00:16.603736Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 59f02f25-d0f7-49a4-9707-811a0ac537e8 · outbound

This paper cites We apply our proposed topological post-processing (Sec.

ContextLoss: Context Information for Topology-Preserving Segmentation We apply our proposed topological post-processing (Sec

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:00:16.593249Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 16b54eb6-4904-49c4-99be-2785dc65f2c9 · outbound

This paper cites CLoss is based on a critical pixel mask, which considers the whole context of topological errors.

ContextLoss: Context Information for Topology-Preserving Segmentation CLoss is based on a critical pixel mask, which considers the whole context of topological errors

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:00:16.583078Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 6ea7d66f-c6a9-44bc-8e8e-e6d8cf805b6c · outbound

This paper cites Loss odyssey in medical image seg- mentation,.

ContextLoss: Context Information for Topology-Preserving Segmentation Loss odyssey in medical image seg- mentation,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:00:16.572080Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 5c8bef21-0301-442e-a91e-3c39358d9062 · outbound

This paper cites Do we really need dice? the hidden region-size biases of segmentation losses,.

ContextLoss: Context Information for Topology-Preserving Segmentation Do we really need dice? the hidden region-size biases of segmentation losses,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:00:16.561461Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation bfc765e7-9b99-4d0f-b835-bf1a7c2e6989 · outbound

This paper cites clDice - a Novel Topology-Preserving Loss Function for Tubular Structure Segmentation,.

ContextLoss: Context Information for Topology-Preserving Segmentation clDice - a Novel Topology-Preserving Loss Function for Tubular Structure Segmentation,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:00:16.551139Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 76801c23-f4c3-45ab-bb3d-f53f4f1ff166 · outbound

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

ContextLoss: Context Information for Topology-Preserving Segmentation A skeletonization algorithm for gradient- based optimization,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:00:16.539935Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation e2734899-e525-496b-ad87-fcafff767159 · outbound

This paper cites Skeleton recall loss for connectivity conserving and resource efficient seg- mentation of thin tubular structures,.

ContextLoss: Context Information for Topology-Preserving Segmentation Skeleton recall loss for connectivity conserving and resource efficient seg- mentation of thin tubular structures,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:00:16.529659Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 3b52255d-ef2c-4c17-9fcd-6f229896f051 · outbound

This paper cites Topology- preserving deep image segmentation,.

ContextLoss: Context Information for Topology-Preserving Segmentation Topology- preserving deep image segmentation,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:00:16.518333Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation cbcb85d3-d7d6-4571-86f9-7fcbe4a91624 · outbound

This paper cites Structure-aware image segmentation with ho- motopy warping,.

ContextLoss: Context Information for Topology-Preserving Segmentation Structure-aware image segmentation with ho- motopy warping,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:00:16.507294Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 39434c9d-6962-49e2-bc42-27817586dff8 · outbound

This paper cites The centerline-cross entropy loss for vessel-like struc- ture segmentation: Better topology consistency without sacrificing accuracy,.

ContextLoss: Context Information for Topology-Preserving Segmentation The centerline-cross entropy loss for vessel-like struc- ture segmentation: Better topology consistency without sacrificing accuracy,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:00:16.495814Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 98d9ec97-012b-4d4a-8f8c-08fd893f7ac8 · outbound

This paper cites Revisiting 3D Medical Scribble Supervision: Benchmarking Beyond Cardiac Segmentation.

ContextLoss: Context Information for Topology-Preserving Segmentation Revisiting 3D Medical Scribble Supervision: Benchmarking Beyond Cardiac Segmentation

Reference 16

Resolution: verified exact; local_arxiv, observed 2026-08-07T05:00:16.296027Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation aac6e7f7-32a6-4b03-8720-121e921e51c4 · outbound

This paper cites nnU-Net: a self-configuring method for deep learning-based biomedical image segmenta- tion,.

ContextLoss: Context Information for Topology-Preserving Segmentation nnU-Net: a self-configuring method for deep learning-based biomedical image segmenta- tion,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:00:16.483156Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 30015c11-6765-4882-b33a-f65c4f8da162 · outbound

This paper cites Metrics reloaded: recommen- dations for image analysis validation,.

ContextLoss: Context Information for Topology-Preserving Segmentation Metrics reloaded: recommen- dations for image analysis validation,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:00:16.471142Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 4624631d-3a7d-406e-b4da-b547cebca20d · outbound

This paper cites Pitfalls of topology-aware image segmentation.

ContextLoss: Context Information for Topology-Preserving Segmentation Pitfalls of topology-aware image segmentation

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-07T05:00:16.211135Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation be1a576d-2f69-4239-8748-6d5af022a38c · outbound

This paper cites Topograph: An efficient graph-based framework for strictly topology preserving image segmentation,.

ContextLoss: Context Information for Topology-Preserving Segmentation Topograph: An efficient graph-based framework for strictly topology preserving image segmentation,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:00:16.460421Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 66bf3405-9314-45ff-b0a3-bd96650cdec9 · outbound

This paper cites Robust segmentation via topology viola- tion detection and feature synthesis,.

ContextLoss: Context Information for Topology-Preserving Segmentation Robust segmentation via topology viola- tion detection and feature synthesis,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:00:16.448929Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 445794d5-0769-4c2b-899c-d42f722ae40f · outbound

This paper cites Trabecular bone struc- tural units and their cement lines change with age, bone volume fraction, structure, and strength in female hu- man vertebrae,.

ContextLoss: Context Information for Topology-Preserving Segmentation Trabecular bone struc- tural units and their cement lines change with age, bone volume fraction, structure, and strength in female hu- man vertebrae,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:00:16.433856Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 1b71334c-0c62-446f-8ff1-3e40ad579954 · outbound

This paper cites Bone min- eral properties and 3D orientation of human lamellar bone around cement lines and the Haversian system,.

ContextLoss: Context Information for Topology-Preserving Segmentation Bone min- eral properties and 3D orientation of human lamellar bone around cement lines and the Haversian system,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:00:16.417152Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 61425b75-d8f7-480a-aab1-d57dfadbad32 · outbound

This paper cites X-ray Zernike phase contrast tomography: 3D ROI visualization of mm-sized mice organ tissues down to sub-cellular com- ponents,.

ContextLoss: Context Information for Topology-Preserving Segmentation X-ray Zernike phase contrast tomography: 3D ROI visualization of mm-sized mice organ tissues down to sub-cellular com- ponents,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:00:16.403240Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation caf525e6-fcea-4d47-8746-14be66e8fcab · outbound

This paper cites Multiscale morpho- logical analysis of bone microarchitecture around mg- 10gd implants,.

ContextLoss: Context Information for Topology-Preserving Segmentation Multiscale morpho- logical analysis of bone microarchitecture around mg- 10gd implants,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:00:16.390619Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation be1e31f1-9479-4a01-a77f-8fa4ec6f746e · outbound

This paper cites thesis, University of Toronto, 2013.

ContextLoss: Context Information for Topology-Preserving Segmentation thesis, University of Toronto, 2013

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:00:16.378869Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation b01236db-1028-4690-8cf0-5306e4152260 · outbound

This paper cites Robust Vessel Segmentation in Fundus Images,.

ContextLoss: Context Information for Topology-Preserving Segmentation Robust Vessel Segmentation in Fundus Images,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:00:16.365931Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation fb916632-bc75-43b6-9dea-9af776ae5827 · outbound

This paper cites Automated analy- sis of whole brain vasculature using machine learning,.

ContextLoss: Context Information for Topology-Preserving Segmentation Automated analy- sis of whole brain vasculature using machine learning,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:00:16.353155Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 4162682b-a123-4d9f-97d8-4ebf0826bf38 · outbound

This paper cites nnU-Net Revisited: A Call for Rigorous Validation in 3D Medical Image Segmentation ,.

ContextLoss: Context Information for Topology-Preserving Segmentation nnU-Net Revisited: A Call for Rigorous Validation in 3D Medical Image Segmentation ,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:00:16.340187Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 26b89b7a-664e-4f6f-854e-9e5b59b9427c · outbound

This paper cites U-net: Convo- lutional networks for biomedical image segmentation,.

ContextLoss: Context Information for Topology-Preserving Segmentation U-net: Convo- lutional networks for biomedical image segmentation,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:00:16.327361Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Pith citing papers

Observation 45974c49-639c-49c5-81c0-4a66ca430af1 · inbound