Paper Citation Record · LEDGER

SAM3D: Segment Anything Model in Volumetric Medical Images

As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2309.03493.

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

pith.paper-citation-record.v1
2309.03493 v4

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T22:18:37.417807Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-09T11:21:46.018214Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

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 e744ba17-fc4c-4131-b5c4-9965d610d026 · inbound

Memorizing SAM: 3D Medical Segment Anything Model with Memorizing Transformer cites this paper.

Memorizing SAM: 3D Medical Segment Anything Model with Memorizing Transformer SAM3D: Segment Anything Model in Volumetric Medical Images

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-11T12:43:39.542591Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 37df8d51-fff2-4bfc-9c62-26030cf3c601 · inbound

Self-Prompt SAM: Medical Image Segmentation via Automatic Prompt SAM Adaptation cites this paper.

Self-Prompt SAM: Medical Image Segmentation via Automatic Prompt SAM Adaptation SAM3D: Segment Anything Model in Volumetric Medical Images

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-09T18:20:58.594473Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9adb8d88-9c83-4631-bc2a-88e2a389bcbc · inbound

RFMedSAM 2: Automatic Prompt Refinement for Enhanced Volumetric Medical Image Segmentation with SAM 2 cites this paper.

RFMedSAM 2: Automatic Prompt Refinement for Enhanced Volumetric Medical Image Segmentation with SAM 2 SAM3D: Segment Anything Model in Volumetric Medical Images

Reference 1

Resolution: verified exact; local_arxiv, observed 2026-08-09T11:21:46.024808Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 7929020f-3b3a-49dd-ac24-f0110834c335 · inbound

MAIS: Memory-Attention for Interactive Segmentation cites this paper.

MAIS: Memory-Attention for Interactive Segmentation SAM3D: Segment Anything Model in Volumetric Medical Images

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-15T22:18:37.417807Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 20b6af51-71b9-4d05-9988-a199061ee719 · inbound

A Self-training Framework for Semi-supervised Pulmonary Vessel Segmentation and Its Application in COPD cites this paper.

A Self-training Framework for Semi-supervised Pulmonary Vessel Segmentation and Its Application in COPD SAM3D: Segment Anything Model in Volumetric Medical Images

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-15T18:05:54.555199Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.