Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
MRSegmentator: Multi-Modality Segmentation of 40 Classes in MRI and CT
As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2405.06463.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-11T22:28:07.629539Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-06-30T06:54:20.423261Z
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 99d928db-2861-4dae-b8be-0f4062031d49 · inbound
MRGen: Segmentation Data Engine for Underrepresented MRI Modalities cites this paper.
MRGen: Segmentation Data Engine for Underrepresented MRI Modalities MRSegmentator: Multi-Modality Segmentation of 40 Classes in MRI and CT
Reference 16
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T22:28:07.629539Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a18afcc0-1c60-4db6-81b8-c2367bffe17a · inbound
Benchmarking of Deep Learning Methods for Generic MRI Multi-Organ Abdominal Segmentation cites this paper.
Benchmarking of Deep Learning Methods for Generic MRI Multi-Organ Abdominal Segmentation MRSegmentator: Multi-Modality Segmentation of 40 Classes in MRI and CT
Reference 1
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T14:41:56.738534Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 063fe603-7e11-48a6-8f7f-8736910989c8 · inbound
LETT-NeXt: A Lightweight RECIST-Guided Model for 3D CT Lesion Segmentation cites this paper.
LETT-NeXt: A Lightweight RECIST-Guided Model for 3D CT Lesion Segmentation MRSegmentator: Multi-Modality Segmentation of 40 Classes in MRI and CT
Reference 12
- Resolution
- verified exact
- arxiv_id, observed 2026-06-30T06:54:20.424956Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.