Paper Citation Record · LEDGER

Geometric Feature Prompting of Image Segmentation Models

As of 17 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 0 inbound Pith citation observations for arXiv:2505.21644.

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

pith.paper-citation-record.v1
2505.21644 v1

Coverage vector

measured 39 of 39 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:30:22.889638Z

measured 39 of 39 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+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

39 of 39 outbound references displayed

verified exact2
verified fuzzy26
unresolved11
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 556a88e2-a88a-471a-a7c3-5aa2a46c55f8 · outbound

This paper cites Multiscale vessel enhancement filtering,.

Geometric Feature Prompting of Image Segmentation Models Multiscale vessel enhancement filtering,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:30.441684Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation b30174b4-b145-46c4-b3ae-4276d8e7c042 · outbound

This paper cites an unresolved cited work.

Geometric Feature Prompting of Image Segmentation Models Unresolved cited work

Reference 2

Resolution: unresolved; raw_fallback, observed 2026-08-07T13:30:30.278599Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 9437b876-c761-4ece-87f6-400f961063e6 · outbound

This paper cites Watersheds in digital spaces: an efficient algorithm based on immersion simulations,.

Geometric Feature Prompting of Image Segmentation Models Watersheds in digital spaces: an efficient algorithm based on immersion simulations,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:30.092334Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation c3f31377-d692-4663-bc9e-b2c0da84ae2b · outbound

This paper cites The persistent morse complex segmenta- tion of a 3-manifold,.

Geometric Feature Prompting of Image Segmentation Models The persistent morse complex segmenta- tion of a 3-manifold,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:29.922402Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 6411e0e5-4a52-4a77-9cbd-a0c762ae14a8 · outbound

This paper cites Semantic segmentation using regions and parts,.

Geometric Feature Prompting of Image Segmentation Models Semantic segmentation using regions and parts,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:29.722695Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation b5badeb9-a4fd-4a4a-af08-d3c01fdd21c1 · outbound

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

Geometric Feature Prompting of Image Segmentation Models U-net: Convolutional networks for biomedical image segmentation,

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-07T13:30:19.236371Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c02d11cc-f58b-4d4a-a45e-58e0c858ae03 · outbound

This paper cites Attention is all you need,.

Geometric Feature Prompting of Image Segmentation Models Attention is all you need,

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-07T13:30:19.527999Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 84b72668-05d2-4439-a6a1-a8ed8b11aab7 · outbound

This paper cites A Review on Deep Learning Techniques Applied to Semantic Segmentation.

Geometric Feature Prompting of Image Segmentation Models A Review on Deep Learning Techniques Applied to Semantic Segmentation

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-07T13:30:19.449617Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9ebad890-55ae-4e2e-82ba-454cc6a5106f · outbound

This paper cites Segment Anything.

Geometric Feature Prompting of Image Segmentation Models Segment Anything

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-07T13:30:19.794986Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4b87f727-a957-4411-9c04-307da711fe64 · outbound

This paper cites On the Opportunities and Risks of Foundation Models.

Geometric Feature Prompting of Image Segmentation Models On the Opportunities and Risks of Foundation Models

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T13:30:19.674767Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1656d6cd-b7c3-4cd4-a76d-da2248ddd37e · outbound

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

Geometric Feature Prompting of Image Segmentation Models SAM 2: Segment Anything in Images and Videos

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-07T13:30:20.054752Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6edb09d9-53ee-4171-81ee-db13c2f48e0a · outbound

This paper cites Edge detection and ridge detection with automatic scale selection,.

Geometric Feature Prompting of Image Segmentation Models Edge detection and ridge detection with automatic scale selection,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:29.509872Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 27083021-6a6a-44d5-95dd-5dbf2ee7ed71 · outbound

This paper cites Algorithms for the topo- logical watershed,.

Geometric Feature Prompting of Image Segmentation Models Algorithms for the topo- logical watershed,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:29.065601Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 37069527-bf2a-4dbf-89d0-1c9a60591a84 · outbound

This paper cites Topological gray-scale watershed trans- formation,.

Geometric Feature Prompting of Image Segmentation Models Topological gray-scale watershed trans- formation,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:29.296437Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation ccadbe11-418a-45a2-8b4f-cb67b082bdaf · outbound

This paper cites Topology-Aware Segmentation Using Discrete Morse Theory.

Geometric Feature Prompting of Image Segmentation Models Topology-Aware Segmentation Using Discrete Morse Theory

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-07T13:30:20.674758Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 39a429f9-2247-4799-bf33-e3ba79fc1042 · outbound

This paper cites Comprehensive multimodal segmentation in medical imaging: Combining yolov8 with sam and hq- sam models,.

Geometric Feature Prompting of Image Segmentation Models Comprehensive multimodal segmentation in medical imaging: Combining yolov8 with sam and hq- sam models,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:28.691630Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation fa00ebbc-7e7f-430d-8597-0cde4d5b89a9 · outbound

This paper cites Edelsbrunner and J.

Geometric Feature Prompting of Image Segmentation Models Edelsbrunner and J

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:28.803964Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation fa8a6d7a-64c0-4f97-aa51-0311d3288edd · outbound

This paper cites an unresolved cited work.

Geometric Feature Prompting of Image Segmentation Models Unresolved cited work

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-07T13:30:19.355887Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4e26e159-b9d8-4df8-8d0e-e157c90dfd8e · outbound

This paper cites Applications and limitations of rhizotrons and minirhizotrons,.

Geometric Feature Prompting of Image Segmentation Models Applications and limitations of rhizotrons and minirhizotrons,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:28.244426Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 9327c513-fe6d-4d68-b285-25a5209d57c8 · outbound

This paper cites Topological Data Analysis Guided Segment Anything Model Prompt Optimization for Zero-Shot Segmentation in Biological Imaging.

Geometric Feature Prompting of Image Segmentation Models Topological Data Analysis Guided Segment Anything Model Prompt Optimization for Zero-Shot Segmentation in Biological Imaging

Reference 20

Resolution: verified exact; local_arxiv, observed 2026-08-07T13:30:23.584905Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 7be83943-9f48-45d1-bb29-d1bc0b8f9b71 · outbound

This paper cites The rhizotron as a tool for root research,.

Geometric Feature Prompting of Image Segmentation Models The rhizotron as a tool for root research,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:28.556473Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation ad7f3cd6-87ea-428a-8d9b-b4159583a443 · outbound

This paper cites Spruce manual minirhizotron images from experimental plots beginning in 2013,.

Geometric Feature Prompting of Image Segmentation Models Spruce manual minirhizotron images from experimental plots beginning in 2013,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:27.185992Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation e86beded-6edc-44a6-955e-af87da783864 · outbound

This paper cites Advancing the use of minirhizotrons in wetlands,.

Geometric Feature Prompting of Image Segmentation Models Advancing the use of minirhizotrons in wetlands,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:27.904930Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 45299370-1f30-444a-b21f-6b0e5176fbef · outbound

This paper cites Minirhizotrons in modern root studies,.

Geometric Feature Prompting of Image Segmentation Models Minirhizotrons in modern root studies,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:27.617717Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation b57839be-b1e2-4410-8692-81fdbc141503 · outbound

This paper cites Measuring root turnover using the minirhizotron technique,.

Geometric Feature Prompting of Image Segmentation Models Measuring root turnover using the minirhizotron technique,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:26.186480Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 40f9f10f-4da2-42ff-b1d7-e93f92ac2276 · outbound

This paper cites A device for the observation of root growth in the soil,.

Geometric Feature Prompting of Image Segmentation Models A device for the observation of root growth in the soil,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:26.884846Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 5fa3fd31-c866-49a2-acd9-17d921489fd1 · outbound

This paper cites Observation of plant roots in situ,.

Geometric Feature Prompting of Image Segmentation Models Observation of plant roots in situ,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:26.435036Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation a64cba4b-8c49-42f6-b426-b39de22ccad0 · outbound

This paper cites Segroot: A high throughput segmentation method for root image analysis,.

Geometric Feature Prompting of Image Segmentation Models Segroot: A high throughput segmentation method for root image analysis,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:25.295332Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 02c6857b-3a98-482f-8c24-e6e32e757d28 · outbound

This paper cites Automatic discrimination of fine roots in minirhizotron images,.

Geometric Feature Prompting of Image Segmentation Models Automatic discrimination of fine roots in minirhizotron images,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:25.935031Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 69844654-b98c-4492-86ec-6932d8f26aa7 · outbound

This paper cites High- throughput in situ root image segmentation based on the improved deeplabv3+ method,.

Geometric Feature Prompting of Image Segmentation Models High- throughput in situ root image segmentation based on the improved deeplabv3+ method,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:25.675407Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation e9e22cbe-3d9a-4eab-a43c-fb2ac465c4b7 · outbound

This paper cites As good as human experts in detecting plant roots in minirhizotron images but efficient and reproducible: the convolutional neural network “rootdetector.

Geometric Feature Prompting of Image Segmentation Models As good as human experts in detecting plant roots in minirhizotron images but efficient and reproducible: the convolutional neural network “rootdetector

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:24.377319Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 3aa5701a-e687-46d1-bb30-49c32956d126 · outbound

This paper cites Segmenta- tion of roots in soil with u-net,.

Geometric Feature Prompting of Image Segmentation Models Segmenta- tion of roots in soil with u-net,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:24.978009Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 8c6e4fa7-04c7-401c-a075-d1794ac92cb5 · outbound

This paper cites Fully-automated root image analysis (faria),.

Geometric Feature Prompting of Image Segmentation Models Fully-automated root image analysis (faria),

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:24.724751Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation fc98a217-086f-404c-882c-ed608500a6cc · outbound

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

Geometric Feature Prompting of Image Segmentation Models AutoSAM: Adapting SAM to Medical Images by Overloading the Prompt Encoder

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-07T13:30:22.746386Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c5d35125-eb11-43b9-8770-604b34f7c8b8 · outbound

This paper cites Semantic segmentation of plant roots from rgb (mini-) rhizotron images—generalisation potential and false positives of established methods and advanced deep-learning models,.

Geometric Feature Prompting of Image Segmentation Models Semantic segmentation of plant roots from rgb (mini-) rhizotron images—generalisation potential and false positives of established methods and advanced deep-learning models,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:24.239138Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation e9a6ecff-c621-4c18-a8cc-28005300b187 · outbound

This paper cites Overcoming small minirhizotron datasets using transfer learning,.

Geometric Feature Prompting of Image Segmentation Models Overcoming small minirhizotron datasets using transfer learning,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:24.075903Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation b3f67cb8-ddb3-41c2-81c4-e8a5e9727042 · outbound

This paper cites PRMI: A Dataset of Minirhizotron Images for Diverse Plant Root Study.

Geometric Feature Prompting of Image Segmentation Models PRMI: A Dataset of Minirhizotron Images for Diverse Plant Root Study

Reference 38

Resolution: verified exact; local_arxiv, observed 2026-08-07T13:30:23.218020Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation d135e68d-2611-46cd-9ad7-64c7e1ddba4e · outbound

This paper cites Active learning literature survey,.

Geometric Feature Prompting of Image Segmentation Models Active learning literature survey,

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-07T13:30:22.889638Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1dbbd1ed-59f9-438d-936c-65a33e51e050 · outbound

This paper cites Springer, 2005, pp.

Geometric Feature Prompting of Image Segmentation Models Springer, 2005, pp

Reference 2005

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:30:28.909096Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Pith citing papers

No inbound Pith citation observations are available.