Paper Citation Record · LEDGER

3D U$^2$-Net: A 3D Universal U-Net for Multi-Domain Medical Image Segmentation

As of 15 August 2026, this Paper Citation Record lists 19 of 19 outbound references and 0 inbound Pith citation observations for arXiv:1909.06012.

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

pith.paper-citation-record.v1
1909.06012 v1

Coverage vector

measured 19 of 19 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-14T05:10:00.566141Z

measured 19 of 19 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

19 of 19 outbound references displayed

verified exact0
verified fuzzy12
unresolved6
parse uncertain0
malformed identifier0
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 4c0ec1aa-df86-4986-8ec6-d856288a6950 · outbound

This paper cites In: Proc.

3D U$^2$-Net: A 3D Universal U-Net for Multi-Domain Medical Image Segmentation In: Proc

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:10:00.787775Z

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.

Observation 72a949f3-6c37-4980-9c8f-06f3d7e7c907 · outbound

This paper cites Universal representations:The missing link between faces, text, planktons, and cat breeds.

3D U$^2$-Net: A 3D Universal U-Net for Multi-Domain Medical Image Segmentation Universal representations:The missing link between faces, text, planktons, and cat breeds

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-14T05:10:00.500400Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fd88a915-11a7-4811-8130-bf21ce7ee862 · outbound

This paper cites In: Proc.

3D U$^2$-Net: A 3D Universal U-Net for Multi-Domain Medical Image Segmentation In: Proc

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:10:00.778612Z

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.

Observation dd490a9f-af62-40ad-bcb9-ae333d5233a2 · outbound

This paper cites In: Proc.

3D U$^2$-Net: A 3D Universal U-Net for Multi-Domain Medical Image Segmentation In: Proc

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:10:00.768663Z

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.

Observation 17db0031-c8e3-4276-a83a-032bdf4f4405 · outbound

This paper cites Depthwise Convolution is All You Need for Learning Multiple Visual Domains.

3D U$^2$-Net: A 3D Universal U-Net for Multi-Domain Medical Image Segmentation Depthwise Convolution is All You Need for Learning Multiple Visual Domains

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-14T05:10:00.513041Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f7777bad-04f3-4f98-9063-8e91b79b7f29 · outbound

This paper cites nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation.

3D U$^2$-Net: A 3D Universal U-Net for Multi-Domain Medical Image Segmentation nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-14T05:10:00.517106Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2a782d0a-8890-4a2c-8ced-d267e46fb2c5 · outbound

This paper cites In: Proc.

3D U$^2$-Net: A 3D Universal U-Net for Multi-Domain Medical Image Segmentation In: Proc

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:10:00.758184Z

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.

Observation 4403ddea-80de-4cf0-ab0c-dac2e159daf1 · outbound

This paper cites CNN-based Segmentation of Medical Imaging Data.

3D U$^2$-Net: A 3D Universal U-Net for Multi-Domain Medical Image Segmentation CNN-based Segmentation of Medical Imaging Data

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-14T05:10:00.524527Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 74c2913f-a753-4228-9ebc-19bea72c80e9 · outbound

This paper cites In: Proc.

3D U$^2$-Net: A 3D Universal U-Net for Multi-Domain Medical Image Segmentation In: Proc

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:10:00.747265Z

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.

Observation 286b6e26-48d7-4a52-afef-af1da6d5f72e · outbound

This paper cites In: Proc.

3D U$^2$-Net: A 3D Universal U-Net for Multi-Domain Medical Image Segmentation In: Proc

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:10:00.737357Z

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.

Observation ab31930e-e64f-45c8-acf4-94174dd3f814 · outbound

This paper cites In: Proc.

3D U$^2$-Net: A 3D Universal U-Net for Multi-Domain Medical Image Segmentation In: Proc

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:10:00.727426Z

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.

Observation 382856c0-684b-4ee1-be1c-c2fefa7aa2d8 · outbound

This paper cites In: Proc.

3D U$^2$-Net: A 3D Universal U-Net for Multi-Domain Medical Image Segmentation In: Proc

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:10:00.716024Z

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.

Observation 7b2b5411-4191-47fe-b7c2-6f6af83b696e · outbound

This paper cites In: Proc.

3D U$^2$-Net: A 3D Universal U-Net for Multi-Domain Medical Image Segmentation In: Proc

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:10:00.705064Z

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.

Observation 3be8c3a5-60f0-41a2-bfd0-0ca0cbf8ee15 · outbound

This paper cites In: Proc.

3D U$^2$-Net: A 3D Universal U-Net for Multi-Domain Medical Image Segmentation In: Proc

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:10:00.693945Z

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.

Observation 44b0d68e-60bb-425c-91c9-63c74a789163 · outbound

This paper cites In: Proc.

3D U$^2$-Net: A 3D Universal U-Net for Multi-Domain Medical Image Segmentation In: Proc

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:10:00.682022Z

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.

Observation 390f332c-25cb-42f3-b572-c73ab7005ac8 · outbound

This paper cites In: Proc.

3D U$^2$-Net: A 3D Universal U-Net for Multi-Domain Medical Image Segmentation In: Proc

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:10:00.671047Z

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.

Observation 09a5fa10-cd1b-42a8-b1af-80ac2a16ffac · outbound

This paper cites Hierarchical 3D fully convolutional networks for multi-organ segmentation.

3D U$^2$-Net: A 3D Universal U-Net for Multi-Domain Medical Image Segmentation Hierarchical 3D fully convolutional networks for multi-organ segmentation

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-14T05:10:00.558090Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 16fd3fea-5b7e-4fd4-8115-d80f01efe159 · outbound

This paper cites V-FCNN: Volumetric Fully Convolution Neural Network For Automatic Atrial Segmentation.

3D U$^2$-Net: A 3D Universal U-Net for Multi-Domain Medical Image Segmentation V-FCNN: Volumetric Fully Convolution Neural Network For Automatic Atrial Segmentation

Reference 18

Resolution: metadata mismatch; local_arxiv, observed 2026-08-14T05:10:00.613130Z

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.

Observation 2d9ca8f9-f207-4e18-b257-0d2def245429 · outbound

This paper cites A large annotated medical image dataset for the development and evaluation of segmentation algorithms.

3D U$^2$-Net: A 3D Universal U-Net for Multi-Domain Medical Image Segmentation A large annotated medical image dataset for the development and evaluation of segmentation algorithms

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-14T05:10:00.566141Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.