Paper Citation Record · LEDGER

SafeClick: Error-Tolerant Interactive Segmentation of Any Medical Volumes via Hierarchical Expert Consensus

As of 20 August 2026, this Paper Citation Record lists 28 of 28 outbound references and 2 inbound Pith citation observations for arXiv:2506.18404.

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

pith.paper-citation-record.v1
2506.18404 v1

Coverage vector

measured 28 of 28 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T23:21:39.223261Z

measured 30 of 30 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T17:15:41.241133Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-18T20:22:50.687751Z

Reference resolution

28 of 28 outbound references displayed

verified exact1
verified fuzzy13
unresolved14
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 62dbe9c3-600a-49c4-954d-b4577fe1cfda · outbound

This paper cites An anatomy-aware framework for automatic segmentation of parotid tumor from multimodal mri.

SafeClick: Error-Tolerant Interactive Segmentation of Any Medical Volumes via Hierarchical Expert Consensus An anatomy-aware framework for automatic segmentation of parotid tumor from multimodal mri

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-06T23:21:36.134813Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 31cc91b3-efe8-4af2-ba09-575d75b77917 · outbound

This paper cites Mba-net: Sam-driven bidirec- tional aggregation network for ovarian tumor segmentation.

SafeClick: Error-Tolerant Interactive Segmentation of Any Medical Volumes via Hierarchical Expert Consensus Mba-net: Sam-driven bidirec- tional aggregation network for ovarian tumor segmentation

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:21:45.155048Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation a121c0f3-478a-4c4f-bee6-2c1d7d6d9c2a · outbound

This paper cites WeGA: Weakly-Supervised Global-Local Affinity Learning Framework for Lymph Node Metastasis Prediction in Rectal Cancer.

SafeClick: Error-Tolerant Interactive Segmentation of Any Medical Volumes via Hierarchical Expert Consensus WeGA: Weakly-Supervised Global-Local Affinity Learning Framework for Lymph Node Metastasis Prediction in Rectal Cancer

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-06T23:21:36.376443Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7124e077-bbcb-4ff3-82f6-a61163e2b046 · outbound

This paper cites Interactive medical image segmentation using deep learning with image-specific fine tuning.IEEE transactions on medical imaging, 37(7):1562–1573, 2018.

SafeClick: Error-Tolerant Interactive Segmentation of Any Medical Volumes via Hierarchical Expert Consensus Interactive medical image segmentation using deep learning with image-specific fine tuning.IEEE transactions on medical imaging, 37(7):1562–1573, 2018

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:21:44.904828Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 7404de21-fd67-4aa0-96c4-5acaf7f59ac9 · outbound

This paper cites Mideepseg: Minimally interactive segmentation of unseen objects from medical images using deep learning.Medical image analysis, 72:102102, 2021.

SafeClick: Error-Tolerant Interactive Segmentation of Any Medical Volumes via Hierarchical Expert Consensus Mideepseg: Minimally interactive segmentation of unseen objects from medical images using deep learning.Medical image analysis, 72:102102, 2021

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:21:44.644749Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 80c185c4-6e38-43ee-a1ad-672e5b44556f · outbound

This paper cites SAM 2: Segment Anything in Images and Videos.

SafeClick: Error-Tolerant Interactive Segmentation of Any Medical Volumes via Hierarchical Expert Consensus SAM 2: Segment Anything in Images and Videos

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-06T23:21:36.724835Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 88793101-f121-4905-971b-1daf21007165 · outbound

This paper cites Medical SAM 2: Segment medical images as video via Segment Anything Model 2.

SafeClick: Error-Tolerant Interactive Segmentation of Any Medical Volumes via Hierarchical Expert Consensus Medical SAM 2: Segment medical images as video via Segment Anything Model 2

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-06T23:21:36.815334Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d8ae1b53-930d-46b3-9fe9-fe9de93555b7 · outbound

This paper cites SAM2-Adapter: Evaluating & Adapting Segment Anything 2 in Downstream Tasks: Camouflage, Shadow, Medical Image Segmentation, and More.

SafeClick: Error-Tolerant Interactive Segmentation of Any Medical Volumes via Hierarchical Expert Consensus SAM2-Adapter: Evaluating & Adapting Segment Anything 2 in Downstream Tasks: Camouflage, Shadow, Medical Image Segmentation, and More

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-06T23:21:36.944884Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 99bc48d7-f4ab-40a2-ab68-2427cbe70efb · outbound

This paper cites Sam2-unet: Segment anything 2 makes strong encoder for natural and medical image segmentation.arXiv preprint arXiv:2408.08870, 2024.

SafeClick: Error-Tolerant Interactive Segmentation of Any Medical Volumes via Hierarchical Expert Consensus Sam2-unet: Segment anything 2 makes strong encoder for natural and medical image segmentation.arXiv preprint arXiv:2408.08870, 2024

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-06T23:21:37.086467Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f56926d1-9266-4eeb-b9bb-042ab5479cf2 · outbound

This paper cites Unleashing the Potential of SAM2 for Biomedical Images and Videos: A Survey.

SafeClick: Error-Tolerant Interactive Segmentation of Any Medical Volumes via Hierarchical Expert Consensus Unleashing the Potential of SAM2 for Biomedical Images and Videos: A Survey

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-06T23:21:37.172350Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 81a2854e-03f5-4ed9-a75a-d7132ac0e2a8 · outbound

This paper cites Adaptive Interactive Segmentation for Multimodal Medical Imaging via Selection Engine.

SafeClick: Error-Tolerant Interactive Segmentation of Any Medical Volumes via Hierarchical Expert Consensus Adaptive Interactive Segmentation for Multimodal Medical Imaging via Selection Engine

Reference 11

Resolution: verified exact; local_arxiv, observed 2026-08-06T23:21:39.704742Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 581c0723-4f60-4ef9-a5e3-929325f5f0a6 · outbound

This paper cites Desam: Decoupled segment anything model for generalizable medical image segmentation.

SafeClick: Error-Tolerant Interactive Segmentation of Any Medical Volumes via Hierarchical Expert Consensus Desam: Decoupled segment anything model for generalizable medical image segmentation

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:21:44.293850Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 36d21aa1-8f51-472b-a867-400652edc876 · outbound

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

SafeClick: Error-Tolerant Interactive Segmentation of Any Medical Volumes via Hierarchical Expert Consensus Segment anything model for medical images?Medical Image Analysis, 92:103061, 2024

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:21:43.925252Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation cca2d58f-6223-4de6-ace1-5ac4f36551e4 · outbound

This paper cites ScribblePrompt: Fast and Flexible Interactive Segmentation for Any Biomedical Image.

SafeClick: Error-Tolerant Interactive Segmentation of Any Medical Volumes via Hierarchical Expert Consensus ScribblePrompt: Fast and Flexible Interactive Segmentation for Any Biomedical Image

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-06T23:21:37.612204Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1dcfbdcd-1357-4b3e-86af-a7e996115ee4 · outbound

This paper cites Adaptive mixtures of local experts.Neural computation, 3(1):79–87, 1991.

SafeClick: Error-Tolerant Interactive Segmentation of Any Medical Volumes via Hierarchical Expert Consensus Adaptive mixtures of local experts.Neural computation, 3(1):79–87, 1991

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-06T23:21:37.714749Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 88e08575-e7bc-483b-b4d6-4bd5119e7578 · outbound

This paper cites SA-Med2D-20M Dataset: Segment Anything in 2D Medical Imaging with 20 Million masks.

SafeClick: Error-Tolerant Interactive Segmentation of Any Medical Volumes via Hierarchical Expert Consensus SA-Med2D-20M Dataset: Segment Anything in 2D Medical Imaging with 20 Million masks

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-06T23:21:37.817418Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e8c6114b-6844-430f-9116-a7ef51d65697 · outbound

This paper cites A global benchmark of algorithms for segmenting the left atrium from late gadolinium- enhanced cardiac magnetic resonance imaging.Medical image analysis, 67:101832, 2021.

SafeClick: Error-Tolerant Interactive Segmentation of Any Medical Volumes via Hierarchical Expert Consensus A global benchmark of algorithms for segmenting the left atrium from late gadolinium- enhanced cardiac magnetic resonance imaging.Medical image analysis, 67:101832, 2021

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:21:43.584822Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 45edce4f-692f-4220-a904-a5d8a4d5c042 · outbound

This paper cites Unleashing the Strengths of Unlabeled Data in Pan-cancer Abdominal Organ Quantification: the FLARE22 Challenge.

SafeClick: Error-Tolerant Interactive Segmentation of Any Medical Volumes via Hierarchical Expert Consensus Unleashing the Strengths of Unlabeled Data in Pan-cancer Abdominal Organ Quantification: the FLARE22 Challenge

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-06T23:21:38.076723Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bd020e6c-7cdd-4999-a357-d5a47745f006 · outbound

This paper cites The medical segmentation decathlon.Nature communications, 13(1):4128, 2022.

SafeClick: Error-Tolerant Interactive Segmentation of Any Medical Volumes via Hierarchical Expert Consensus The medical segmentation decathlon.Nature communications, 13(1):4128, 2022

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:21:43.340392Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 21230282-ebee-4c91-86cb-5ba332d77fe4 · outbound

This paper cites Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge.

SafeClick: Error-Tolerant Interactive Segmentation of Any Medical Volumes via Hierarchical Expert Consensus Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:21:42.854863Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 34c7cbcd-3b2e-4833-95df-8c359eb6b60b · outbound

This paper cites Benchmark for algorithms segmenting the left atrium from 3d ct and mri datasets.IEEE transactions on medical imaging, 34(7):1460–1473, 2015.

SafeClick: Error-Tolerant Interactive Segmentation of Any Medical Volumes via Hierarchical Expert Consensus Benchmark for algorithms segmenting the left atrium from 3d ct and mri datasets.IEEE transactions on medical imaging, 34(7):1460–1473, 2015

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:21:42.542773Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 1823b9cc-9316-40bb-87c0-7c63458f1fbd · outbound

This paper cites Evaluation of prostate segmentation algorithms for mri: the promise12 challenge.Medical image analysis, 18(2):359–373, 2014.

SafeClick: Error-Tolerant Interactive Segmentation of Any Medical Volumes via Hierarchical Expert Consensus Evaluation of prostate segmentation algorithms for mri: the promise12 challenge.Medical image analysis, 18(2):359–373, 2014

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-06T23:21:38.568383Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 07383cf9-4534-4a27-9d3f-ce790a811175 · outbound

This paper cites Computer Aided Detection for Pulmonary Embolism Challenge (CAD-PE).

SafeClick: Error-Tolerant Interactive Segmentation of Any Medical Volumes via Hierarchical Expert Consensus Computer Aided Detection for Pulmonary Embolism Challenge (CAD-PE)

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-06T23:21:38.665051Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e5b67501-6641-4bba-8d25-8cf4e67526d5 · outbound

This paper cites an unresolved cited work.

SafeClick: Error-Tolerant Interactive Segmentation of Any Medical Volumes via Hierarchical Expert Consensus Unresolved cited work

Reference 24

Resolution: unresolved; raw_fallback, observed 2026-08-06T23:21:42.296828Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 42d28a96-d546-4dd1-920f-1bc4199f7d1b · outbound

This paper cites Structseg2019 gtv segmentation, 2023.

SafeClick: Error-Tolerant Interactive Segmentation of Any Medical Volumes via Hierarchical Expert Consensus Structseg2019 gtv segmentation, 2023

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:21:41.907935Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 510fe9c7-110d-4a3a-882d-e7ec02db1f07 · outbound

This paper cites Emre Kavur, N.

SafeClick: Error-Tolerant Interactive Segmentation of Any Medical Volumes via Hierarchical Expert Consensus Emre Kavur, N

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:21:41.584747Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 7ef83ee1-d6dd-4882-a556-472716f7dc3f · outbound

This paper cites Crossmoda 2021 challenge: Benchmark of cross-modality domain adap- tation techniques for vestibular schwannoma and cochlea segmentation.Medical Image Analysis, 83:102628, 2023.

SafeClick: Error-Tolerant Interactive Segmentation of Any Medical Volumes via Hierarchical Expert Consensus Crossmoda 2021 challenge: Benchmark of cross-modality domain adap- tation techniques for vestibular schwannoma and cochlea segmentation.Medical Image Analysis, 83:102628, 2023

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:21:41.074750Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation e1b67eb3-0e89-4f35-9d40-1f82efb00080 · outbound

This paper cites Comparing algorithms for automated vessel segmentation in computed tomography scans of the lung: the vessel12 study.Medical image anal- ysis, 18(7):1217–1232, 2014.

SafeClick: Error-Tolerant Interactive Segmentation of Any Medical Volumes via Hierarchical Expert Consensus Comparing algorithms for automated vessel segmentation in computed tomography scans of the lung: the vessel12 study.Medical image anal- ysis, 18(7):1217–1232, 2014

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:21:40.725686Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Pith citing papers

Observation 8f182cee-f7e0-4939-9f78-474897e5d1ea · inbound

LRMR: LLM-Driven Relational Multi-node Ranking for Lymph Node Metastasis Assessment in Rectal Cancer cites this paper.

LRMR: LLM-Driven Relational Multi-node Ranking for Lymph Node Metastasis Assessment in Rectal Cancer SafeClick: Error-Tolerant Interactive Segmentation of Any Medical Volumes via Hierarchical Expert Consensus

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-06T17:15:41.241133Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 94dabdc1-0460-4ba2-bbe2-20924fe1ec73 · inbound

Dino U-Net: Exploiting High-Fidelity Dense Features from Foundation Models for Medical Image Segmentation cites this paper.

Dino U-Net: Exploiting High-Fidelity Dense Features from Foundation Models for Medical Image Segmentation SafeClick: Error-Tolerant Interactive Segmentation of Any Medical Volumes via Hierarchical Expert Consensus

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-05-18T20:22:50.691221Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.