Pith. sign in

REVIEW 2 major objections 2 minor 52 references

OccluNet: Spatio-Temporal Deep Learning for Occlusion Detection on DSA

T0 review · 2 major / 2 minor · reviewed 2026-08-05 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read Spatio-temporal model detects stroke occlusions in DSA with 89% precision

desk verdict This submission is a structural mismatch: the abstract describes OccluNet, but the full text is an unrelated paper on tactile graphics, so the central claim is unverifiable from this artifact. read the letter →

arxiv 2508.14286 v1 pith:XABRE3IL submitted 2025-08-19 cs.CV cs.AI

Anushka A. Kore , Frank G. te Nijenhuis , Matthijs van der Sluijs , Wim van Zwam , Charles Majoie , Geert Lycklama à Nijeholt , Danny Ruijters , Frans Vos
show 3 more authors
Sandra Cornelissen Ruisheng Su Theo van Walsum
This is my paper · ORCID
classification cs.CVcs.AI
keywords occlusiondetectiondigitalsubtractionangiographyspatio-temporaldeeplearningtransformerattentionYOLOXendovascularthrombectomyacuteischemicstrokeMRCLEANRegistry
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper proposes OccluNet, a deep-learning model that detects vascular occlusions in digital subtraction angiography (DSA) sequences by combining the YOLOX object detector with transformer-based temporal attention. The authors claim that using the temporal structure of the image sequence improves detection over analyzing single frames, and they report precision of 89.02% and recall of 74.87% on the MR CLEAN Registry. They compare against YOLOv11 baselines trained on individual frames or minimum intensity projections, and find OccluNet significantly better. The practical goal is to support endovascular thrombectomy (EVT) decision-making, where rapid, accurate identification of blockages is critical. A reader should care because an automated detector that works on real clinical angiography could reduce time-to-treatment in acute ischemic stroke.

What carries the argument

OccluNet's core is a spatio-temporal attention pipeline: YOLOX produces frame-level detection features, and a transformer encoder with temporal attention aggregates them across the DSA sequence, so the model can reward detections that are temporally consistent and suppress flicker. The divided space-time variant factorizes attention into spatial and temporal steps; the pure temporal variant applies attention only across time. The machinery's job is to turn a set of per-frame detections into a sequence-level decision, which is what the paper claims outperforms single-frame detection.

What would settle it

Train OccluNet and a YOLOv11 frame-wise baseline on the same MR CLEAN Registry data, but with a strict patient-level train/test split; if the precision-recall gap shrinks to less than a few points, the temporal-attention advantage is an artifact of information leakage. Also, if a YOLOv11 trained on the identical frames with identical augmentation and test-time settings matches or beats OccluNet, the claim that sequence modeling helps would be refuted.

Watch

Extended reading notes

Core claim

On the paper's terms, the central discovery is that adding a transformer-based temporal attention module to a single-stage detector (YOLOX) lets the model exploit consistency across DSA frames, improving occlusion detection beyond what the same detector sees in a single frame or a minimum-intensity projection. Two spatio-temporal variants—pure temporal attention and divided space-time attention—achieve similar performance, with the best configuration reaching 89.02% precision and 74.87% recall on MR CLEAN Registry data. The authors interpret this as evidence that temporal modeling, rather than the specific attention factorization, is what drives the gain, and that OccluNet is a viable automa

Load-bearing premise

That the MR CLEAN Registry ground-truth labels are correct and that the reported 89%/75% numbers come from a fair split, not from patient leakage, class imbalance, or tuning on the test set.

Editorial extensions

If this is right

  • If OccluNet's performance holds, EVT teams could get an automated second reader that flags occlusions from the full sequence rather than single frames.
  • The finding that both attention variants perform similarly suggests a simpler pure-temporal model may suffice, reducing compute cost in practice.
  • The reported precision-recall balance implies OccluNet could be used as a screening tool, with false negatives at roughly 25% warranting human review.
  • The success on MR CLEAN Registry data supports testing on prospective, multi-center DSA datasets to confirm clinical usefulness.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The paper likely understates the risk of patient-level leakage: if frames from the same patient appear in both train and test, the temporal attention could memorize patient-specific anatomy rather than generalize. A patient-stratified cross-validation would be the natural check.
  • The model's ability to use temporal consistency may extend beyond occlusions to other time-varying contrast phenomena in angiography, such as collateral flow grading.
  • A direct comparison could be made against a 3D-CNN or video transformer baseline to see how much of the gain is from attention specifically versus any temporal encoder.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

2 major / 2 minor

Summary. The manuscript's abstract claims a novel deep learning system, OccluNet, for automated vascular occlusion detection in digital subtraction angiography (DSA) sequences, reporting precision of 89.02% and recall of 74.87% on the MR CLEAN Registry, with two spatio-temporal attention variants significantly outperforming YOLOv11 baselines. However, the full text submitted is a completely different paper on tactile graphics for blind and low-vision users (“They Aren’t Built For Me”), with no mention of OccluNet, DSA, YOLOX, MR CLEAN, or any occlusion detection experiments. Thus, the central claim of the abstract is entirely unsupported by the body of the manuscript.

Significance. If the claimed result were substantiated, OccluNet would be a practically valuable contribution to endovascular thrombectomy workflows, offering a spatio-temporal detector that leverages temporal consistency across angiography sequences. The comparison with strong baselines such as YOLOv11 and the two attention variants would also be informative. However, the submitted manuscript contains none of the architecture, training protocol, data split, evaluation methodology, or statistical comparisons that would support this claim. Consequently, the significance cannot be assessed from the current submission, because the purported evidence is absent.

major comments (2)
  1. [Full text (all sections after abstract)] The submitted full text is an entirely unrelated paper on tactile graphics, titled “They Aren’t Built For Me”, with its own abstract about a Cleveland-McGill replication with blind participants. It contains no OccluNet architecture, no DSA dataset, no YOLOX/YOLOv11 baselines, no training or inference details, and no occlusion detection evaluation. The abstract’s claims about precision/recall and significant improvement over baselines therefore have no supporting methods or results anywhere in the manuscript. This is a load-bearing mismatch that invalidates the central claim; it cannot be remedied by minor edits.
  2. [Abstract vs. full text] The abstract announces source code availability and two spatio-temporal attention variants (pure temporal and divided space-time), but none of these artifacts appear in the full text. No dataset split, patient-level or frame-level unit of analysis, confidence threshold, ground-truth labeling procedure, or statistical test is described. Even the self-reported limitations in Section 7.2 of the full text concern the tactile graphics study, not OccluNet. The manuscript therefore provides no basis to verify the abstract’s quantitative claims or to assess potential leakage, class imbalance, or test-set overfitting.
minor comments (2)
  1. [Metadata] The arXiv identifier in the full text (2508.14289) does not match the submitted identifier (2508.14286), and the title/author list of the full text differs from that implied by the abstract. These inconsistencies should be resolved if the submission is ever corrected.
  2. [Full-text formatting] The full text is a camera-ready paper for IEEE VIS 2025 with its own acknowledgments and references; it is clearly a distinct document. If the intended submission were actually OccluNet, the manuscript body, figures, and references would need to be replaced entirely.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity identified: the submission contains no derivational chain to reduce, so any performance claim is an unverified assertion, not a circular one.

full rationale

The submitted text pairs an abstract for OccluNet (arXiv:2508.14286) with a full manuscript on tactile graphics (arXiv:2508.14289). The OccluNet abstract reports precision/recall of 89.02%/74.87% and superiority over baselines, but no OccluNet methods, equations, training protocol, dataset split, loss function, inference procedure, or statistical tests appear in the provided full text. There is therefore no derivation chain to walk and no equation or fitted parameter that can be shown to reduce to its own input by construction. The performance numbers are unsupported by the supplied text — an evidentiary gap that could mask leakage, threshold tuning, or evaluation-guided model selection — but unsupported is not the same as circular. No load-bearing self-citation, imported uniqueness theorem, ansatz smuggled via citation, or renamed empirical pattern is present in the supplied material. The closest manuscript-internal limitations (small sample, generalizability, swell-paper resolution) belong to the tactile-graphics paper and do not bear on the OccluNet claim. Under the hard rule that circularity must be exhibited by quotation and reduction, no circular step can be identified, so the appropriate score is 0.

Assumptions & free parameters 0 free parameters · 0 assumptions · 0 invented entities

No methods or equations are available in this submission, so no free parameters, axioms, or invented entities can be enumerated for OccluNet. The full text belongs to an unrelated study.

how reviews work

0 comments
Cite this review

Pith. "Pith review of OccluNet: Spatio-Temporal Deep Learning for Occlusion Detection on DSA." pith.science (2026). https://pith.science/paper/XABRE3IL

@misc{pith2026250814286,
  author       = {Pith},
  title        = {Pith review of: OccluNet: Spatio-Temporal Deep Learning for Occlusion Detection on DSA},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/XABRE3IL}},
  note         = {Machine review of arXiv:2508.14286}
}
read the original abstract

Accurate detection of vascular occlusions during endovascular thrombectomy (EVT) is critical in acute ischemic stroke (AIS). Interpretation of digital subtraction angiography (DSA) sequences poses challenges due to anatomical complexity and time constraints. This work proposes OccluNet, a spatio-temporal deep learning model that integrates YOLOX, a single-stage object detector, with transformer-based temporal attention mechanisms to automate occlusion detection in DSA sequences. We compared OccluNet with a YOLOv11 baseline trained on either individual DSA frames or minimum intensity projections. Two spatio-temporal variants were explored for OccluNet: pure temporal attention and divided space-time attention. Evaluation on DSA images from the MR CLEAN Registry revealed the model's capability to capture temporally consistent features, achieving precision and recall of 89.02% and 74.87%, respectively. OccluNet significantly outperformed the baseline models, and both attention variants attained similar performance. Source code is available at https://github.com/anushka-kore/OccluNet.git

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

52 extracted references · 47 canonical work pages

  1. [1]

    Ahmetovic, C

    D. Ahmetovic, C. Bernareggi, J. Guerreiro, S. Mascetti, and A. Capi- etto. Audiofunctions. web: Multimodal exploration of mathematical function graphs. In Proceedings of the 16th International Web for All Conference, pp. 1–10, 2019. 3

    work page 2019

  2. [2]

    Alliance

    V . Alliance. United states’ older population and vision loss: A brief- ing. https://visionservealliance.org, 2022. Prepared by: The Ohio State University, College of Optometry. 10

    work page 2022

  3. [3]

    H. K. Ault, J. W. Deloge, R. W. Lapp, M. J. Morgan, and J. R. Barnett. Evaluation of long descriptions of statistical graphics for blind and low vision web users. In K. Miesenberger, J. Klaus, and W. Zagler, eds., Computers Helping People with Special Needs, pp. 517–526. Springer Berlin Heidelberg, Berlin, Heidelberg, 2002. 1

    work page 2002

  4. [5]

    X. Chen, W. Zeng, Y . Lin, H. M. Ai-Maneea, J. Roberts, and R. Chang. Composition and configuration patterns in multiple-view visualiza- tions. IEEE Transactions on Visualization and Computer Graphics, 27(2):1514–1524, 2020. 9

    work page 2020

  5. [6]

    Chundury, Y

    P. Chundury, Y . Reyazuddin, J. B. Jordan, J. Lazar, and N. Elmqvist. Tactualplot: spatializing data as sound using sensory substitution for touchscreen accessibility. IEEE Transactions on Visualization and Computer Graphics, 30(1):836–846, 2023. 3

    work page 2023

  6. [7]

    W. S. Cleveland and R. McGill. Graphical perception: Theory, experi- mentation, and application to the development of graphical methods. Journal of the American statistical association, 79(387):531–554, 1984. 2, 4, 5, 10

    work page 1984

  7. [8]

    Davis, X

    R. Davis, X. Pu, Y . Ding, B. D. Hall, K. Bonilla, M. Feng, M. Kay, and L. Harrison. The risks of ranking: Revisiting graphical perception to model individual differences in visualization performance. IEEE Transactions on Visualization and Computer Graphics, 30(3):1756– 1771, 2022. 5, 6

    work page 2022

  8. [9]

    Ebermann and M

    J. Ebermann and M. Keck. From sight to touch: Designing tactile data physicalizations for non-sighted users. In 2024 1st Workshop on Accessible Data Visualization (AccessViz), pp. 9–13. IEEE, 2024. 10

    work page 2024

Show all 52 references
  1. [10]

    Engel and G

    C. Engel and G. Weber. Analysis of tactile chart design. In Proceed- ings of the 10th International Conference on PErvasive Technologies Related to Assistive Environments, PETRA ’17, 4 pages, p. 197–200. Association for Computing Machinery, New York, NY , USA, 2017. doi: 10.11...

    2017

  2. [11]

    Engel and G

    C. Engel and G. Weber. User study: A detailed view on the effective- ness and design of tactile charts. In Human-Computer Interaction– INTERACT 2019: 17th IFIP TC 13 International Conference, Paphos, Cyprus, September 2–6, 2019, Proceedings, Part I 17 , pp. 63–82. Springer, 20...

    2019

  3. [12]

    D. Fan, A. F. Siu, W.-S. A. Law, R. R. Zhen, S. O’Modhrain, and S. Follmer. Slide-tone and tilt-tone: 1-dof haptic techniques for convey- ing shape characteristics of graphs to blind users. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems, CHI ’2...

    2022

  4. [13]

    D. Fan, A. F. Siu, W.-S. A. Law, R. R. Zhen, S. O’Modhrain, and S. Follmer. Slide-tone and tilt-tone: 1-dof haptic techniques for con- veying shape characteristics of graphs to blind users. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems, pp. 1–...

    2022

  5. [14]

    G. A. Fink, C. L. North, A. Endert, and S. Rose. Visualizing cyber security: Usable workspaces. In 2009 6th international workshop on visualization for cyber security, pp. 45–56. IEEE, 2009. 9

    2009

  6. [15]

    Fusco and V

    G. Fusco and V . S. Morash. The tactile graphics helper: providing audio clarification for tactile graphics using machine vision. In Proceedings of the 17th international ACM SIGACCESS conference on computers & accessibility, pp. 97–106, 2015. 3

    2015

  7. [16]

    Goncu and K

    C. Goncu and K. Marriott. Gravvitas: generic multi-touch presentation of accessible graphics. In Human-Computer Interaction–INTERACT 2011: 13th IFIP TC 13 International Conference, Lisbon, Portugal, September 5-9, 2011, Proceedings, Part I 13 , pp. 30–48. Springer,

    2011

  8. [17]

    Goncu, K

    C. Goncu, K. Marriott, and J. Hurst. Usability of accessible bar charts. In A. K. Goel, M. Jamnik, and N. H. Narayanan, eds., Diagrammatic Representation and Inference, pp. 167–181. Springer Berlin Heidelberg, Berlin, Heidelberg, 2010. 2

    2010

  9. [18]

    Heer and M

    J. Heer and M. Bostock. Crowdsourcing graphical perception: using mechanical turk to assess visualization design. In Proceedings of the SIGCHI conference on human factors in computing systems , pp. 203–212, 2010. 4, 5

    2010

  10. [19]

    J. Kim, A. Srinivasan, N. W. Kim, and Y .-S. Kim. Exploring chart question answering for blind and low vision users. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems, pp. 1–15, 2023. 10

    2023

  11. [20]

    N. W. Kim, G. Ataguba, S. C. Joyner, C. Zhao, and H. Im. Beyond alternative text and tables: Comparative analysis of visualization tools and accessibility methods. In Computer graphics forum, vol. 42, pp. 323–335. Wiley Online Library, 2023. 2

    2023

  12. [21]

    N. W. Kim, S. C. Joyner, A. Riegelhuth, and Y . Kim. Accessible visu- alization: Design space, opportunities, and challenges. In Computer graphics forum, vol. 40, pp. 173–188. Wiley Online Library, 2021. 2

    2021

  13. [22]

    T. Le, C. Aragon, H. J. Thompson, and G. Demiris. Elementary graphical perception for older adults: a comparison with the general population. Perception, 43(11):1249–1260, 2014. 2

    2014

  14. [23]

    Lundgard and A

    A. Lundgard and A. Satyanarayan. Accessible visualization via natural language descriptions: A four-level model of semantic content. IEEE transactions on visualization and computer graphics, 28(1):1073–1083,

  15. [24]

    M. C. McDonnall and Z. Sui. Employment and unemployment rates of people who are blind or visually impaired. Journal of Visual Impair- ment & Blindness, 113(3):245–254, 2019. 1

    2019

  16. [25]

    Panavas, A

    L. Panavas, A. E. Worth, T. Crnovrsanin, T. Sathyamurthi, S. Cordes, M. A. Borkin, and C. Dunne. Juvenile graphical perception: A com- parison between children and adults. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems, pp. 1–14, 2022. 2

    2022

  17. [26]

    Paneels and J

    S. Paneels and J. C. Roberts. Review of designs for haptic data visual- ization. IEEE Transactions on Haptics, 3(2):119–137, 2009. 2

    2009

  18. [27]

    Perkins and A

    C. Perkins and A. G. and. Real world map reading strategies. The Cartographic Journal , 40(3):265–268, 2003. doi: 10.1179/ 000870403225012970 2

    2003

  19. [28]

    I. P. Pineros, A. Satyanarayan, J. Zong, et al. Tactile vega-lite: Rapidly prototyping tactile charts with smart defaults. arXiv preprint arXiv:2503.00149, 2025. 1

    2025 arXiv

  20. [29]

    Prakash, A

    Y . Prakash, A. Kolgar Nayak, S. Jayarathna, H.-N. Lee, and V . Ashok. Understanding low vision graphical perception of bar charts. In Pro- ceedings of the 26th International ACM SIGACCESS Conference on Computers and Accessibility, ASSETS ’24, article no. 59, 10 pages. Associa...

    2024

  21. [30]

    Reinders, M

    S. Reinders, M. Butler, I. Zukerman, B. Lee, L. Qu, and K. Marriott. When refreshable tactile displays meet conversational agents: Investi- gating accessible data presentation and analysis with touch and speech. arXiv preprint arXiv:2408.04806, 2024. 10

    2024 arXiv

  22. [31]

    J. C. Roberts. State of the art: Coordinated & multiple views in exploratory visualization. In Fifth international conference on coordi- nated and multiple views in exploratory visualization (CMV 2007), pp. 61–71. IEEE, 2007. 9

    2007

  23. [32]

    Rosenberg

    N. Rosenberg. What does flattening the curve look like?, 2021. 1

    2021

  24. [33]

    Rowell and S

    J. Rowell and S. Ungar. The world of touch: an international survey of tactile maps. part 1: production. British Journal of Visual Impairment, 21(3):98–104, 2003. doi: 10.1177/026461960302100303 3

    2003 doi

  25. [34]

    Rowell and S

    J. Rowell and S. Ungar. Feeling our way: tactile map user requirements- a survey. In International Cartographic Conference, La Coruna, vol. 152, 2005. 3

    2005

  26. [35]

    Sakhardande, A

    P. Sakhardande, A. Joshi, C. Jadhav, and M. Joshi. Comparing user performance on parallel-tone, parallel-speech, serial-tone and serial- speech auditory graphs. In Human-Computer Interaction–INTERACT 2019: 17th IFIP TC 13 International Conference, Paphos, Cyprus, September 2–6...

    2019

  27. [36]

    J. Seo, Y . Xia, B. Lee, S. Mccurry, and Y . J. Yam. Maidr: Making statistical visualizations accessible with multimodal data representation. In Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems, pp. 1–22, 2024. 3

    2024

  28. [37]

    Sharif, A

    A. Sharif, A. M. Zhang, K. Reinecke, and J. O. Wobbrock. Understand- ing and improving drilled-down information extraction from online data visualizations for screen-reader users. In Proceedings of the 20th International Web for All Conference, pp. 18–31, 2023. 2

    2023

  29. [38]

    Shneiderman

    B. Shneiderman. The eyes have it: A task by data type taxonomy for information visualizations. In The craft of information visualization, pp. 364–371. Elsevier, 2003. 8

    2003

  30. [39]

    A. Siu, G. S-H Kim, S. O’Modhrain, and S. Follmer. Supporting acces- sible data visualization through audio data narratives. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems, CHI ’22, article no. 476, 19 pages. Association for Computing Ma- chine...

    2022

  31. [40]

    D. A. Szafir. Modeling color difference for visualization design. IEEE transactions on visualization and computer graphics, 24(1):392–401,

  32. [41]

    S. Tabrik. Neural mechanisms underlying cross-modal object catego- rization: Visual and tactile senses. 2022. 1

    2022

  33. [42]

    Thunström, S

    L. Thunström, S. C. Newbold, D. Finnoff, M. Ashworth, and J. F. Shogren. The benefits and costs of using social distancing to flatten the curve for covid-19. Journal of Benefit-Cost Analysis, 11(2):179–195,

  34. [43]

    E. R. Tufte and P. R. Graves-Morris. The visual display of quantitative information, vol. 2. Graphics press Cheshire, CT, 1983. 8

    1983

  35. [44]

    midas touch problem

    B. Velichkovsky, A. Sprenger, and P. Unema. Towards gaze-mediated interaction: Collecting solutions of the “midas touch problem”. In Human-Computer Interaction INTERACT’97: IFIP TC13 International Conference on Human-Computer Interaction, 14th–18th July 1997, Sydney, Australia...

    1997

  36. [45]

    B. N. Walker and L. M. Mauney. Universal design of auditory graphs: A comparison of sonification mappings for visually impaired and sighted listeners. ACM Transactions on Accessible Computing (TACCESS), 2(3):1–16, 2010. 2

    2010

  37. [46]

    Watanabe and N

    T. Watanabe and N. Inaba. Textures suitable for tactile bar charts on capsule paper. Transactions of the Virtual Reality Society of Japan , 23(1):13–20, 2018. doi: 10.18974/tvrsj.23.1_13 2

    2018 doi

  38. [47]

    Watanabe and H

    T. Watanabe and H. Mizukami. Effectiveness of tactile scatter plots: Comparison of non-visual data representations. In K. Miesenberger and G. Kouroupetroglou, eds., Computers Helping People with Special Needs, pp. 628–635. Springer International Publishing, Cham, 2018. 2, 8

    2018

  39. [48]

    B. Wong. Points of view: Gestalt principles (part 1). nature methods, 7(11):863, 2010. 8

    2010

  40. [49]

    K. Wu, E. Petersen, T. Ahmad, D. Burlinson, S. Tanis, and D. A. 11 This is the author’s version of the article that will be presented at the IEEE Visualization Conference in Vienna, November 2025. No DOI has been alotted at this point, as of August 19, 2025. Szafir. Understand...

    2025

  41. [50]

    Z. Xu, K. Williams, and E. Wall. Let’s get vysical: Perceptual accuracy in visual & tactile encodings. In 2023 IEEE Visualization and Visual Analytics (VIS), pp. 16–20. IEEE, 2023. 1, 3

    2023

  42. [51]

    Y . Yang, K. Marriott, M. Butler, C. Goncu, and L. Holloway. Tactile presentation of network data: Text, matrix or diagram? In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, CHI ’20, 12 pages, p. 1–12. Association for Computing Machinery, New Yor...

    2020

  43. [52]

    Zhang, J

    Z. Zhang, J. R. Thompson, A. Shah, M. Agrawal, A. Sarikaya, J. O. Wobbrock, E. Cutrell, and B. Lee. Charta11y: Designing accessible touch experiences of visualizations with blind smartphone users. In Proceedings of the 26th International ACM SIGACCESS Conference on Computers a...

    2024

  44. [53]

    J. Zong, C. Lee, A. Lundgard, J. Jang, D. Hajas, and A. Satyanarayan. Rich screen reader experiences for accessible data visualization. In Computer Graphics Forum, vol. 41, pp. 15–27. Wiley Online Library,

Pith tools

Reviewed August 5, 2026 · model on record in the stance chip above.