Paper Citation Record · LEDGER

EchoONE: Segmenting Multiple echocardiography Planes in One Model

As of 19 August 2026, this Paper Citation Record lists 45 of 45 outbound references and 0 inbound Pith citation observations for arXiv:2412.02993.

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

pith.paper-citation-record.v1
2412.02993 v3

Coverage vector

measured 45 of 45 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T22:58:15.216635Z

measured 45 of 45 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+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

45 of 45 outbound references displayed

verified exact1
verified fuzzy32
unresolved12
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 961e7345-bc02-4cb3-82f7-d334d57944d9 · outbound

This paper cites Swin-unet: Unet-like pure transformer for medical image segmentation.

EchoONE: Segmenting Multiple echocardiography Planes in One Model Swin-unet: Unet-like pure transformer for medical image segmentation

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:58:15.931739Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation be80bd94-f555-4248-95ce-1483c0ac80ce · outbound

This paper cites Ladder Fine-tuning approach for SAM integrating complementary network.

EchoONE: Segmenting Multiple echocardiography Planes in One Model Ladder Fine-tuning approach for SAM integrating complementary network

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-11T22:58:15.012942Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation db8c6447-2024-4b63-b1ab-e054ca8ffc6b · outbound

This paper cites Ma-sam: Modality-agnostic sam adap- tation for 3d medical image segmentation.

EchoONE: Segmenting Multiple echocardiography Planes in One Model Ma-sam: Modality-agnostic sam adap- tation for 3d medical image segmentation

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:58:15.916536Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 800892c0-ce8b-4b85-980b-8290acdb41ac · outbound

This paper cites The ability of segmenting anything model (sam) to segment ultrasound images.

EchoONE: Segmenting Multiple echocardiography Planes in One Model The ability of segmenting anything model (sam) to segment ultrasound images

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:58:15.900768Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation cdaf0de2-f4cc-4d24-96cc-d419f02b3fa5 · outbound

This paper cites Rsprompter: Learning to prompt for remote sensing instance segmenta- tion based on visual foundation model.

EchoONE: Segmenting Multiple echocardiography Planes in One Model Rsprompter: Learning to prompt for remote sensing instance segmenta- tion based on visual foundation model

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-11T22:58:15.029779Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9e52fb6d-e194-414e-80c8-16ce9f381a7e · outbound

This paper cites Encoder-decoder with atrous separable convolution for semantic image segmentation.

EchoONE: Segmenting Multiple echocardiography Planes in One Model Encoder-decoder with atrous separable convolution for semantic image segmentation

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:58:15.875561Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 003103ce-1f36-458b-8970-73f24d6e243b · outbound

This paper cites SAM-Med2D.

EchoONE: Segmenting Multiple echocardiography Planes in One Model SAM-Med2D

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-11T22:58:15.039957Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 07e03daf-d33e-4adb-8d49-24dc8fc070ad · outbound

This paper cites SAMAug: Point Prompt Augmentation for Segment Anything Model.

EchoONE: Segmenting Multiple echocardiography Planes in One Model SAMAug: Point Prompt Augmentation for Segment Anything Model

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-11T22:58:15.045197Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aac60641-db59-43ee-8402-e5b76e8ef3b8 · outbound

This paper cites Mvfusfra: A multi-view dynamic fusion framework for multimodal brain tumor segmentation.

EchoONE: Segmenting Multiple echocardiography Planes in One Model Mvfusfra: A multi-view dynamic fusion framework for multimodal brain tumor segmentation

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:58:15.858663Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 2e76b21a-bbe4-42ef-b44a-bc1d358b4de5 · outbound

This paper cites Gvcnn: Group-view convolutional neural networks for 3d shape recognition.

EchoONE: Segmenting Multiple echocardiography Planes in One Model Gvcnn: Group-view convolutional neural networks for 3d shape recognition

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:58:15.843224Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 5ad921e0-a262-4fd1-9fa9-4f8a502388be · outbound

This paper cites H2former: An efficient hierarchical hybrid transformer for medical image segmentation.

EchoONE: Segmenting Multiple echocardiography Planes in One Model H2former: An efficient hierarchical hybrid transformer for medical image segmentation

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:58:15.828088Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 287f98a0-b19d-41b1-b83f-fe74d22f87b7 · outbound

This paper cites Deep residual learning for image recognition.

EchoONE: Segmenting Multiple echocardiography Planes in One Model Deep residual learning for image recognition

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-11T22:58:15.064128Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ec0a95a2-6e84-4acc-b723-5b2c98d91707 · outbound

This paper cites Relax image-specific prompt requirement in sam: A single generic prompt for segmenting camouflaged objects.

EchoONE: Segmenting Multiple echocardiography Planes in One Model Relax image-specific prompt requirement in sam: A single generic prompt for segmenting camouflaged objects

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:58:15.803536Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 2f752ecd-a875-4b3a-b9b2-8ac71592b1d4 · outbound

This paper cites Segment anything model for medical images? Medical Image Analysis, 92:103061, 2024.

EchoONE: Segmenting Multiple echocardiography Planes in One Model Segment anything model for medical images? Medical Image Analysis, 92:103061, 2024

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:58:15.788751Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation eab6ff9c-bb92-4362-a7b5-66e57a60ebb5 · outbound

This paper cites Multi-view sa-la net: A framework for simultaneous seg- mentation of rv on multi-view cardiac mr images.

EchoONE: Segmenting Multiple echocardiography Planes in One Model Multi-view sa-la net: A framework for simultaneous seg- mentation of rv on multi-view cardiac mr images

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:58:15.773781Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 743ff304-2d9d-4b78-b1fb-11c42f795dc0 · outbound

This paper cites Left ventricular wall motion estimation by active polynomials for acute myocardial infarction detection.

EchoONE: Segmenting Multiple echocardiography Planes in One Model Left ventricular wall motion estimation by active polynomials for acute myocardial infarction detection

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:58:15.759345Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 9e7c941a-46b7-4621-aec1-ac3f467ae969 · outbound

This paper cites Segment any- thing.

EchoONE: Segmenting Multiple echocardiography Planes in One Model Segment any- thing

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:58:15.743857Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 8cacf7de-9b0f-42c1-9d34-c31a3c4399de · outbound

This paper cites Deep learning for segmentation using an open large-scale dataset in 2d echocardiography.

EchoONE: Segmenting Multiple echocardiography Planes in One Model Deep learning for segmentation using an open large-scale dataset in 2d echocardiography

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:58:15.728090Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation e74a8c64-c0ff-4502-a942-acfdcf54be73 · outbound

This paper cites Promise: Prompt-driven 3d medical image segmentation us- ing pretrained image foundation models.

EchoONE: Segmenting Multiple echocardiography Planes in One Model Promise: Prompt-driven 3d medical image segmentation us- ing pretrained image foundation models

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:58:15.711358Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 302e4dfa-4709-40e3-9cb3-e9f0c5cc8267 · outbound

This paper cites Right ventricular segmentation from short-and long-axis mris via information transition.

EchoONE: Segmenting Multiple echocardiography Planes in One Model Right ventricular segmentation from short-and long-axis mris via information transition

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:58:15.694694Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 5c3a5fdc-e4f6-4279-bbd9-fb3e479bf412 · outbound

This paper cites Beyond Adapting SAM: Towards End-to-End Ultrasound Image Segmentation via Auto Prompting.

EchoONE: Segmenting Multiple echocardiography Planes in One Model Beyond Adapting SAM: Towards End-to-End Ultrasound Image Segmentation via Auto Prompting

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-11T22:58:15.104320Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d803230c-bdb7-4875-8335-6f364b467db0 · outbound

This paper cites Transfusion: multi-view divergent fusion for medical image segmentation with transformers.

EchoONE: Segmenting Multiple echocardiography Planes in One Model Transfusion: multi-view divergent fusion for medical image segmentation with transformers

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:58:15.679236Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 2d201043-2ff6-461b-8a8c-2f57a4a04eb8 · outbound

This paper cites Segment anything in medical images.

EchoONE: Segmenting Multiple echocardiography Planes in One Model Segment anything in medical images

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:58:15.664625Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f27db526-87d3-4e78-a052-447f1d54d70e · outbound

This paper cites Deep learning segmentation of the right ventricle in cardiac mri: the m&ms challenge.

EchoONE: Segmenting Multiple echocardiography Planes in One Model Deep learning segmentation of the right ventricle in cardiac mri: the m&ms challenge

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:58:15.650130Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation d59844fd-8c73-4c78-9533-b7b2ffb9e74b · outbound

This paper cites Segment anything model for medical image analysis: an experimental study.

EchoONE: Segmenting Multiple echocardiography Planes in One Model Segment anything model for medical image analysis: an experimental study

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-11T22:58:15.122298Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 926386d0-4984-4d8d-b85b-fcb6c20681a9 · outbound

This paper cites V-net: Fully convolutional neural networks for volumetric medical image segmentation.

EchoONE: Segmenting Multiple echocardiography Planes in One Model V-net: Fully convolutional neural networks for volumetric medical image segmentation

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:58:15.625200Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation b88683f4-7399-4a9b-862d-8aadcda0c36d · outbound

This paper cites Spin-nerf: Multiview segmentation and perceptual inpainting with neural radiance fields.

EchoONE: Segmenting Multiple echocardiography Planes in One Model Spin-nerf: Multiview segmentation and perceptual inpainting with neural radiance fields

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:58:15.610834Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 652c50f5-b720-443c-a14a-6014fda67419 · outbound

This paper cites Guidelines for performing a comprehensive transthoracic echocardiographic examination in adults: rec- ommendations from the american society of echocardiogra- phy.

EchoONE: Segmenting Multiple echocardiography Planes in One Model Guidelines for performing a comprehensive transthoracic echocardiographic examination in adults: rec- ommendations from the american society of echocardiogra- phy

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:58:15.595738Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 3e22bc7d-7a50-4a82-ae30-52b0d19733b6 · outbound

This paper cites The segment anything model (sam) for remote sensing applications: From zero to one shot.

EchoONE: Segmenting Multiple echocardiography Planes in One Model The segment anything model (sam) for remote sensing applications: From zero to one shot

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:58:15.579771Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 5545f0ac-6c9d-4bca-9bb3-bf8671f85bf9 · outbound

This paper cites Multi-view radar semantic segmen- tation.

EchoONE: Segmenting Multiple echocardiography Planes in One Model Multi-view radar semantic segmen- tation

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:58:15.564980Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 27ec6b93-e6b1-4b54-94d7-3811936801b7 · outbound

This paper cites Video-based ai for beat-to-beat assessment of cardiac func- tion.

EchoONE: Segmenting Multiple echocardiography Planes in One Model Video-based ai for beat-to-beat assessment of cardiac func- tion

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:58:15.549916Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c2bee071-5033-44d2-bfbb-20b9f845b4e1 · outbound

This paper cites U-net: Convolutional networks for biomedical image segmentation.

EchoONE: Segmenting Multiple echocardiography Planes in One Model U-net: Convolutional networks for biomedical image segmentation

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:58:15.534045Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f98e0f28-9a99-4256-baed-9c187e996915 · outbound

This paper cites Convolu- tional neural networks in medical image understanding: a survey.

EchoONE: Segmenting Multiple echocardiography Planes in One Model Convolu- tional neural networks in medical image understanding: a survey

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:58:15.518273Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 79eb0fb5-616a-4145-be62-4d3ef9daa2ab · outbound

This paper cites AutoSAM: Adapting SAM to Medical Images by Overloading the Prompt Encoder.

EchoONE: Segmenting Multiple echocardiography Planes in One Model AutoSAM: Adapting SAM to Medical Images by Overloading the Prompt Encoder

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-11T22:58:15.163733Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2ce5745e-38a0-4528-956a-5d641ba1c69d · outbound

This paper cites Transformers in medical imaging: A survey.

EchoONE: Segmenting Multiple echocardiography Planes in One Model Transformers in medical imaging: A survey

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-11T22:58:15.168636Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 48f5a6d7-5376-442f-8698-828324f5fbd4 · outbound

This paper cites Drcnn: Dynamic routing convolutional neural network for multi-view 3d object recognition.

EchoONE: Segmenting Multiple echocardiography Planes in One Model Drcnn: Dynamic routing convolutional neural network for multi-view 3d object recognition

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:58:15.492334Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 34c3d66a-f3e0-430c-9154-f7ae6f79f101 · outbound

This paper cites Vrp-sam: Sam with visual reference prompt.

EchoONE: Segmenting Multiple echocardiography Planes in One Model Vrp-sam: Sam with visual reference prompt

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:58:15.476237Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation fba8e5af-80cd-4cf6-938a-2e9afd3de131 · outbound

This paper cites Samrs: Scaling-up re- mote sensing segmentation dataset with segment anything model.

EchoONE: Segmenting Multiple echocardiography Planes in One Model Samrs: Scaling-up re- mote sensing segmentation dataset with segment anything model

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:58:15.460674Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 60e5fd24-ffed-4581-b22d-f5d6c3979af0 · outbound

This paper cites Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation.

EchoONE: Segmenting Multiple echocardiography Planes in One Model Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-11T22:58:15.187101Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 571b7ac0-d8bc-4bed-88f0-df0ca2c592b1 · outbound

This paper cites PA-SAM: Prompt Adapter SAM for High-Quality Image Segmentation.

EchoONE: Segmenting Multiple echocardiography Planes in One Model PA-SAM: Prompt Adapter SAM for High-Quality Image Segmentation

Reference 40

Resolution: verified exact; local_arxiv, observed 2026-08-11T22:58:15.294911Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 463a5b11-3eda-4fb3-b890-8142dd11f6d6 · outbound

This paper cites Open-vocabulary sam: Segment and recognize twenty-thousand classes interactively.

EchoONE: Segmenting Multiple echocardiography Planes in One Model Open-vocabulary sam: Segment and recognize twenty-thousand classes interactively

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:58:15.443851Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 460e3718-7549-4a74-a063-5c1fe04a188c · outbound

This paper cites Surgicalsam: Efficient class promptable sur- gical instrument segmentation.

EchoONE: Segmenting Multiple echocardiography Planes in One Model Surgicalsam: Efficient class promptable sur- gical instrument segmentation

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:58:15.427393Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 2c006035-b084-4cb2-8fef-3167b7369647 · outbound

This paper cites SAM3D: Zero-Shot 3D Object Detection via Segment Anything Model.

EchoONE: Segmenting Multiple echocardiography Planes in One Model SAM3D: Zero-Shot 3D Object Detection via Segment Anything Model

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-11T22:58:15.206519Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 45c84325-63ef-4b4f-ae41-46cd8d3438d8 · outbound

This paper cites Customized Segment Anything Model for Medical Image Segmentation.

EchoONE: Segmenting Multiple echocardiography Planes in One Model Customized Segment Anything Model for Medical Image Segmentation

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-11T22:58:15.211942Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 99bac19a-9c9c-41c6-ba26-50e53d95863a · outbound

This paper cites Cross-view discrepancy-dependency network for volumetric medical image segmentation.

EchoONE: Segmenting Multiple echocardiography Planes in One Model Cross-view discrepancy-dependency network for volumetric medical image segmentation

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:58:15.410331Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Pith citing papers

No inbound Pith citation observations are available.