Paper Citation Record · LEDGER

Deep learning to achieve clinically applicable segmentation of head and neck anatomy for radiotherapy

As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 14 inbound Pith citation observations for arXiv:1809.04430.

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

pith.paper-citation-record.v1
1809.04430 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 14 of 14 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 14 of 14 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T04:49:37.526953Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-01T10:05:41.107332Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
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External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 4cb89d44-ca82-408b-9d45-896ee06a3fc3 · inbound

Anatomically Consistent Segmentation of Organs at Risk in MRI with Convolutional Neural Networks cites this paper.

Anatomically Consistent Segmentation of Organs at Risk in MRI with Convolutional Neural Networks Deep learning to achieve clinically applicable segmentation of head and neck anatomy for radiotherapy

Reference 38

Resolution: verified exact; arxiv_id, observed 2026-05-25T09:35:35.461807Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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

Enhancing the automatic segmentation and analysis of 3D liver vasculature models cites this paper.

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 3c642a65-d815-4f7d-a757-e103d48b1aba · inbound

Understanding the Impact of Evaluation Metrics in Kinetic Models for Consensus-based Segmentation cites this paper.

Understanding the Impact of Evaluation Metrics in Kinetic Models for Consensus-based Segmentation Deep learning to achieve clinically applicable segmentation of head and neck anatomy for radiotherapy

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-11T22:27:31.238213Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 176b28e9-801c-40a1-b0d2-d77f770e47ba · inbound

Multimodal HIE Lesion Segmentation in Neonates: A Comparative Study of Loss Functions cites this paper.

Multimodal HIE Lesion Segmentation in Neonates: A Comparative Study of Loss Functions Deep learning to achieve clinically applicable segmentation of head and neck anatomy for radiotherapy

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T22:32:36.938274Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation edcc2827-6456-4413-948e-19ab0990a5f0 · inbound

AI-Assisted Decision-Making for Clinical Assessment of Auto-Segmented Contour Quality cites this paper.

AI-Assisted Decision-Making for Clinical Assessment of Auto-Segmented Contour Quality Deep learning to achieve clinically applicable segmentation of head and neck anatomy for radiotherapy

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-16T04:49:37.526953Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6f6591f1-7d90-41a0-87e9-3600bd7464c0 · inbound

Seamless and Efficient Interactions within a Mixed-Dimensional Information Space cites this paper.

Seamless and Efficient Interactions within a Mixed-Dimensional Information Space Deep learning to achieve clinically applicable segmentation of head and neck anatomy for radiotherapy

Reference 207

Resolution: unresolved; no resolver link, observed 2026-08-07T10:45:31.065728Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d0ea242a-18a0-4d9d-a1b8-53224438c14d · inbound

Pixel-wise Modulated Dice Loss for Medical Image Segmentation cites this paper.

Pixel-wise Modulated Dice Loss for Medical Image Segmentation Deep learning to achieve clinically applicable segmentation of head and neck anatomy for radiotherapy

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-07T00:14:31.925703Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 559beb59-6e5a-4b10-acb1-b43bb535042f · inbound

CRISP-SAM2: SAM2 with Cross-Modal Interaction and Semantic Prompting for Multi-Organ Segmentation cites this paper.

CRISP-SAM2: SAM2 with Cross-Modal Interaction and Semantic Prompting for Multi-Organ Segmentation Deep learning to achieve clinically applicable segmentation of head and neck anatomy for radiotherapy

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-06T21:53:41.024097Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cd6af4d1-a23b-43e7-8f92-5e9c26c1b7d0 · inbound

RadGenome-Anatomy: A Large-Scale Anatomy-Labeled Chest Radiograph Dataset via Physically Grounded Volumetric Projection cites this paper.

RadGenome-Anatomy: A Large-Scale Anatomy-Labeled Chest Radiograph Dataset via Physically Grounded Volumetric Projection Deep learning to achieve clinically applicable segmentation of head and neck anatomy for radiotherapy

Reference 30

Resolution: verified exact; arxiv_id, observed 2026-05-20T13:23:18.438374Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation d40dc160-f143-49d6-a444-b98ea1ac973f · inbound

Towards Voxel Spacing Consistency for Medical Image Segmentation cites this paper.

Towards Voxel Spacing Consistency for Medical Image Segmentation Deep learning to achieve clinically applicable segmentation of head and neck anatomy for radiotherapy

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-07-01T10:05:41.109073Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 526fe04a-eaee-4115-b58d-a9a2d3232670 · inbound

BrainNext: A General-Purpose Self-Supervised Foundation Model for Brain MRI Analysis cites this paper.

BrainNext: A General-Purpose Self-Supervised Foundation Model for Brain MRI Analysis Deep learning to achieve clinically applicable segmentation of head and neck anatomy for radiotherapy

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-15T15:38:11.934587Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e4396347-4132-4366-9a51-22c3edc98b2d · inbound

DynoDINO: Harnessing Dynamic Latent Information from DINO Features for Multi-Phase Medical Image Segmentation cites this paper.

DynoDINO: Harnessing Dynamic Latent Information from DINO Features for Multi-Phase Medical Image Segmentation Deep learning to achieve clinically applicable segmentation of head and neck anatomy for radiotherapy

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-03T04:29:05.752620Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6ec89dbd-6942-41b1-873f-7c240a0b8b21 · inbound

EliSeg: Verified Target Construction for Report-Grounded Abnormality Segmentation cites this paper.

EliSeg: Verified Target Construction for Report-Grounded Abnormality Segmentation Deep learning to achieve clinically applicable segmentation of head and neck anatomy for radiotherapy

Reference 654

Resolution: unresolved; no resolver link, observed 2026-08-10T10:50:25.223418Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e5771771-62c8-4fa4-b3a8-041016d8e9dc · inbound

EliSeg: Verified Target Construction for Report-Grounded Abnormality Segmentation cites this paper.

EliSeg: Verified Target Construction for Report-Grounded Abnormality Segmentation Deep learning to achieve clinically applicable segmentation of head and neck anatomy for radiotherapy

Reference 654

Resolution: unresolved; no resolver link, observed 2026-08-12T00:54:56.898790Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.