Paper Citation Record · LEDGER

Towards Segment Anything Model (SAM) for Medical Image Segmentation: A Survey

As of 12 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2305.03678.

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

pith.paper-citation-record.v1
2305.03678 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 8 of 8 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T13:44:05.118917Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T06:56:44.569758Z

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 21049c7b-ff66-4ec9-98e9-e7b04c3a106b · inbound

Few-Shot Adaptation of Training-Free Foundation Model for 3D Medical Image Segmentation cites this paper.

Few-Shot Adaptation of Training-Free Foundation Model for 3D Medical Image Segmentation Towards Segment Anything Model (SAM) for Medical Image Segmentation: A Survey

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-10T20:15:26.535357Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4e49623e-56e3-4c0d-b9f7-1f31966aff56 · inbound

Foundational Models for 3D Point Clouds: A Survey and Outlook cites this paper.

Foundational Models for 3D Point Clouds: A Survey and Outlook Towards Segment Anything Model (SAM) for Medical Image Segmentation: A Survey

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-09T22:54:24.377087Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bd56553f-3107-4696-b1a0-5646b04e4449 · inbound

SAMed-2: Selective Memory Enhanced Medical Segment Anything Model cites this paper.

SAMed-2: Selective Memory Enhanced Medical Segment Anything Model Towards Segment Anything Model (SAM) for Medical Image Segmentation: A Survey

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-06T20:09:31.587292Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a5212793-1c11-4e3b-ae40-15e19fe40275 · inbound

Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges cites this paper.

Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges Towards Segment Anything Model (SAM) for Medical Image Segmentation: A Survey

Reference 86

Resolution: unresolved; no resolver link, observed 2026-08-06T17:56:03.835409Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 28e38cec-142e-4f6a-9f40-4dfeebf825fd · inbound

Semantic Segmentation of iPS Cells: Case Study on Model Complexity in Biomedical Imaging cites this paper.

Semantic Segmentation of iPS Cells: Case Study on Model Complexity in Biomedical Imaging Towards Segment Anything Model (SAM) for Medical Image Segmentation: A Survey

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-06T12:37:58.480278Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7970f7fe-3836-4852-9346-184513293056 · inbound

Enhancing MedSAM with a Lightweight Box Predictor for Medical Image Segmentation cites this paper.

Enhancing MedSAM with a Lightweight Box Predictor for Medical Image Segmentation Towards Segment Anything Model (SAM) for Medical Image Segmentation: A Survey

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-07-02T06:56:44.571725Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation bedfad8d-e154-4638-b88e-316e66d773ec · inbound

Enhancing MedSAM with a Lightweight Box Predictor for Medical Image Segmentation cites this paper.

Enhancing MedSAM with a Lightweight Box Predictor for Medical Image Segmentation Towards Segment Anything Model (SAM) for Medical Image Segmentation: A Survey

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-02T12:27:07.631019Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ff46563f-fe9b-4b6f-960b-6021de46441f · inbound

NeuroRefiner: Morphology-Aware Multi-Agent Refinement for 3D Fluorescence Microscopy Neuron Segmentation cites this paper.

NeuroRefiner: Morphology-Aware Multi-Agent Refinement for 3D Fluorescence Microscopy Neuron Segmentation Towards Segment Anything Model (SAM) for Medical Image Segmentation: A Survey

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-11T13:44:05.118917Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.