Paper Citation Record · LEDGER

One Sequence to Segment Them All: Efficient Data Augmentation for CT and MRI Cross-Domain 3D Spine Segmentation

As of 6 August 2026, this Paper Citation Record lists 23 of 23 outbound references and 0 inbound Pith citation observations for arXiv:2605.03098.

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

pith.paper-citation-record.v1
2605.03098 v1

Coverage vector

measured 23 of 23 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-08T18:40:31.464005Z

measured 23 of 23 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-05T06:32:48.257954+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

23 of 23 outbound references displayed

verified exact3
verified fuzzy18
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch2

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 00d754e3-3fca-4ef6-b409-9f9db670147a · outbound

This paper cites Medical image analysis86, 102789 (2023).

One Sequence to Segment Them All: Efficient Data Augmentation for CT and MRI Cross-Domain 3D Spine Segmentation Medical image analysis86, 102789 (2023)

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T04:21:41.578941Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 25a6cd9b-5379-4a38-b40c-ae4778f17667 · outbound

This paper cites MONAI: An open-source framework for deep learning in healthcare.

One Sequence to Segment Them All: Efficient Data Augmentation for CT and MRI Cross-Domain 3D Spine Segmentation MONAI: An open-source framework for deep learning in healthcare

Reference 2

Resolution: metadata mismatch; arxiv_id, observed 2026-05-13T22:54:28.721435Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 05ae0d06-49d2-414e-aa37-81ee6baa3c0c · outbound

This paper cites Journal of medical imaging and radiation oncology65(5), 545–563 (2021).

One Sequence to Segment Them All: Efficient Data Augmentation for CT and MRI Cross-Domain 3D Spine Segmentation Journal of medical imaging and radiation oncology65(5), 545–563 (2021)

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T04:21:41.596425Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 388fbe0a-e8ce-47fc-98b6-5a7817367c15 · outbound

This paper cites IEEE transactions on Image Processing20(5), 1249–1261 (2010).

One Sequence to Segment Them All: Efficient Data Augmentation for CT and MRI Cross-Domain 3D Spine Segmentation IEEE transactions on Image Processing20(5), 1249–1261 (2010)

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T04:21:41.592675Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 61277a33-3854-49a1-96f2-da8338f0edcc · outbound

This paper cites Artificial intelligence review56(11), 12561–12605 (2023).

One Sequence to Segment Them All: Efficient Data Augmentation for CT and MRI Cross-Domain 3D Spine Segmentation Artificial intelligence review56(11), 12561–12605 (2023)

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T04:21:41.586356Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation ef8773d5-8b95-42ec-b11d-09793784e0a5 · outbound

This paper cites Scientific Data 11(1), 264 (2024).

One Sequence to Segment Them All: Efficient Data Augmentation for CT and MRI Cross-Domain 3D Spine Segmentation Scientific Data 11(1), 264 (2024)

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T04:21:41.589245Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation a8fdcb14-344c-4502-91c3-15abe4756fdb · outbound

This paper cites In: Medical Imaging with Deep Learning (2024).

One Sequence to Segment Them All: Efficient Data Augmentation for CT and MRI Cross-Domain 3D Spine Segmentation In: Medical Imaging with Deep Learning (2024)

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T04:21:41.600062Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 89029dad-792a-40a4-baed-636e4312678f · outbound

This paper cites European Radiology Experimental7(1), 70 (2023).

One Sequence to Segment Them All: Efficient Data Augmentation for CT and MRI Cross-Domain 3D Spine Segmentation European Radiology Experimental7(1), 70 (2023)

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T04:21:41.582953Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 5d897561-452a-4b29-adef-38dfdcc9d138 · outbound

This paper cites European Radiology Experimental9(1), 93 (2025) 10 Molinier and Möller et al.

One Sequence to Segment Them All: Efficient Data Augmentation for CT and MRI Cross-Domain 3D Spine Segmentation European Radiology Experimental9(1), 93 (2025) 10 Molinier and Möller et al

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T04:21:41.574665Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation dd92913a-8749-4f49-85e9-5d5ccc1847ab · outbound

This paper cites Nature methods18(2), 203–211 (2021).

One Sequence to Segment Them All: Efficient Data Augmentation for CT and MRI Cross-Domain 3D Spine Segmentation Nature methods18(2), 203–211 (2021)

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T04:21:41.539114Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation c99877e8-bde0-493a-8247-a696c3499da1 · outbound

This paper cites Journal of imaging9(4), 81 (2023).

One Sequence to Segment Them All: Efficient Data Augmentation for CT and MRI Cross-Domain 3D Spine Segmentation Journal of imaging9(4), 81 (2023)

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T04:21:41.534378Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation ddc47244-2687-44b8-8168-1525b6545f7f · outbound

This paper cites Panoptica -- instance-wise evaluation of 3D semantic and instance segmentation maps.

One Sequence to Segment Them All: Efficient Data Augmentation for CT and MRI Cross-Domain 3D Spine Segmentation Panoptica -- instance-wise evaluation of 3D semantic and instance segmentation maps

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-05-09T06:15:39.049579Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 2a2ccb18-c64f-465b-90ad-f35389d62b60 · outbound

This paper cites IEEE transactions on medical imaging 34(10), 1993–2024 (2014).

One Sequence to Segment Them All: Efficient Data Augmentation for CT and MRI Cross-Domain 3D Spine Segmentation IEEE transactions on medical imaging 34(10), 1993–2024 (2014)

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T04:21:41.558791Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 17caff17-112c-4c7b-a57e-f952a1eab7af · outbound

This paper cites IEEE Transactions on Medical Imaging42(4), 1095–1106 (2022).

One Sequence to Segment Them All: Efficient Data Augmentation for CT and MRI Cross-Domain 3D Spine Segmentation IEEE Transactions on Medical Imaging42(4), 1095–1106 (2022)

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T04:21:41.566329Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 1ba284b4-7ce6-4bc5-97d7-d8da91965d6c · outbound

This paper cites Informatics in medicine unlocked47, 101504 (2024).

One Sequence to Segment Them All: Efficient Data Augmentation for CT and MRI Cross-Domain 3D Spine Segmentation Informatics in medicine unlocked47, 101504 (2024)

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T04:21:41.549754Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 1ff970da-7842-4622-97fb-2e461a69f5e9 · outbound

This paper cites an unresolved cited work.

One Sequence to Segment Them All: Efficient Data Augmentation for CT and MRI Cross-Domain 3D Spine Segmentation Unresolved cited work

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-05-08T18:49:26.936870Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation dfc61863-546e-4709-8c30-1fe9fafc2507 · outbound

This paper cites In: 2023 IEEE Intelligent Vehicles Symposium (IV).

One Sequence to Segment Them All: Efficient Data Augmentation for CT and MRI Cross-Domain 3D Spine Segmentation In: 2023 IEEE Intelligent Vehicles Symposium (IV)

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T04:21:41.546310Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 941ba7ad-e16e-448d-a102-5a66f1b8fa92 · outbound

This paper cites In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.

One Sequence to Segment Them All: Efficient Data Augmentation for CT and MRI Cross-Domain 3D Spine Segmentation In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T04:21:41.571169Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 253e57de-c7b3-43fa-82ab-b1c345e9a46f · outbound

This paper cites In: International work- shop on simulation and synthesis in medical imaging.

One Sequence to Segment Them All: Efficient Data Augmentation for CT and MRI Cross-Domain 3D Spine Segmentation In: International work- shop on simulation and synthesis in medical imaging

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T04:21:41.553637Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 76f95a4f-ee94-4f89-b3d9-134468a78ce5 · outbound

This paper cites IEEE transactions on medical imag- ing29(6), 1310–1320 (2010).

One Sequence to Segment Them All: Efficient Data Augmentation for CT and MRI Cross-Domain 3D Spine Segmentation IEEE transactions on medical imag- ing29(6), 1310–1320 (2010)

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T04:21:41.542719Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 7081b080-cc39-4e0c-bf5f-63e3cc989a3f · outbound

This paper cites ResearchGate preprint (2025), https://www.researchgate.net/publication/389881289_TotalSpineSeg_Robust_ Spine_Segmentation_with_Landmark-Based_Labeling_in_MRI.

One Sequence to Segment Them All: Efficient Data Augmentation for CT and MRI Cross-Domain 3D Spine Segmentation ResearchGate preprint (2025), https://www.researchgate.net/publication/389881289_TotalSpineSeg_Robust_ Spine_Segmentation_with_Landmark-Based_Labeling_in_MRI

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-05-09T06:15:39.046613Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 81ffb458-c7f0-48ef-8f63-b1ff9849899f · outbound

This paper cites Computerized Medical Imaging and Graphics p.

One Sequence to Segment Them All: Efficient Data Augmentation for CT and MRI Cross-Domain 3D Spine Segmentation Computerized Medical Imaging and Graphics p

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T04:21:41.562450Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 38f16ee0-ba1e-412e-9ede-94bd7017ebf8 · outbound

This paper cites Robust and Generalizable Visual Representation Learning via Random Convolutions.

One Sequence to Segment Them All: Efficient Data Augmentation for CT and MRI Cross-Domain 3D Spine Segmentation Robust and Generalizable Visual Representation Learning via Random Convolutions

Reference 23

Resolution: metadata mismatch; arxiv_id, observed 2026-05-09T06:15:39.052020Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Pith citing papers

No inbound Pith citation observations are available.