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arxiv: 2605.23118 · v1 · pith:MCWNELFDnew · submitted 2026-05-22 · 💻 cs.CV · cs.AI· cs.LG

Exploiting Longitudinal Context in Clinician-Verified Interactive Lesion Tracking

Yannick Kirchhoff , Maximilian Rokuss , Daniel Philipp Mertens , David F\"uller , Benjamin Hamm , Andreas Schreyer , Oliver Ritter , Klaus Maier-Hein This is my paper

Pith reviewed 2026-05-25 05:18 UTC · model grok-4.3

classification 💻 cs.CV cs.AIcs.LG
keywords lesion trackinglongitudinal CTverified trackingsynthetic pretraininginteractive segmentationtumor segmentationpancreatic cancer
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The pith

A clinician-verified prompt fused with baseline lesion appearance and temporal differences improves CT tumor tracking accuracy.

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

Existing trackers either automate fully without correction options or allow verification but ignore prior lesion data, creating a gap in handling ambiguous cases across serial scans. The paper proposes a Verified Tracking setup where a clinician checks a registration prompt, then the model uses that input together with the lesion's earlier appearance to guide segmentation. A single framework performs early spatial prompt fusion and latent temporal difference weighting to capture longitudinal context. Large-scale synthetic pretraining turns out to be required for the longitudinal gains, delivering up to 4.5 Dice points over scratch training and first place on the autoPET IV challenge. A new pancreatic benchmark called PanTrack is released to test generalization beyond the training distribution.

Core claim

The central claim is that early spatial prompt fusion combined with latent temporal difference weighting, when initialized via large-scale synthetic pretraining, lets a segmentation model exploit longitudinal context once a clinician has verified a registration-proposed prompt. This produces higher Dice scores than prior automatic or decoupled methods in both fully automatic and interactive verified-tracking regimes.

What carries the argument

early spatial prompt fusion with latent temporal difference weighting

If this is right

  • The model outperforms prior automatic trackers and decoupled registration-segmentation pipelines in both fully automatic and clinician-verified settings.
  • Synthetic pretraining yields up to 4.5 Dice point gains over training from scratch on real data.
  • The method placed first in the MICCAI autoPET IV challenge.
  • The released PanTrack dataset enables measurement of out-of-distribution performance on pancreatic lesions.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Clinicians could adopt the verified mode as a low-effort safeguard that still captures most of the automation benefit.
  • The same fusion and weighting steps might apply to other serial-imaging tasks such as monitoring brain metastases or liver lesions.
  • Public release of code, weights, and the PanTrack benchmark could accelerate comparison of prompt-based longitudinal methods across institutions.

Load-bearing premise

Large-scale synthetic pretraining will transfer the ability to exploit longitudinal context when the model later sees real clinical CT data.

What would settle it

Training the same architecture from scratch on the real longitudinal data and finding no Dice loss relative to the synthetically pretrained version in the verified-tracking evaluation.

Figures

Figures reproduced from arXiv: 2605.23118 by Andreas Schreyer, Benjamin Hamm, Daniel Philipp Mertens, David F\"uller, Klaus Maier-Hein, Maximilian Rokuss, Oliver Ritter, Yannick Kirchhoff.

Figure 1
Figure 1. Figure 1: Overview of our framework. The registration proposes a candidate follow￾up prompt which the clinician verifies or corrects. A shared-weight encoder processes both (image, prompt) pairs and in latent space a Difference Weighting Block fuses their features by explicitly attending to temporal change before the decoder produces the longitudinally-informed follow-up segmentation. Prior, the model is pretrained … view at source ↗
Figure 2
Figure 2. Figure 2: Qualitative comparison on autoPET IV (top) and PanTrack (bottom). Single-timepoint baselines struggle with ambiguous lesion volumes. In the top row, a shrinking lesion borders the colon; without the baseline appearance as context, compet￾ing models fail to isolate the correct structure when prompted near the organ boundary. PanTrack DSC), substantially outperforming prior promptable models. This margin iso… view at source ↗
read the original abstract

Tracking tumor lesions across serial CT scans is essential for oncological response assessment. Existing automated methods face a fundamental trade-off: end-to-end trackers achieve high automation but offer no opportunity to correct silent tracking failures, while decoupled registration-segmentation pipelines permit user verification yet discard the lesion's prior appearance, limiting accuracy in ambiguous cases. In this work, we propose a Verified Tracking paradigm: a clinician verifies a registration-proposed prompt, which the model leverages alongside the baseline lesion appearance to resolve segmentation ambiguities. We present a unified framework combining early spatial prompt fusion with latent temporal difference weighting for longitudinally-informed segmentation. To address data scarcity, we leverage large-scale synthetic pretraining, proving essential for exploiting longitudinal context, improving performance by up to 4.5 Dice points over training from scratch. Our approach secured first place in the MICCAI autoPET IV challenge. We further curate and release PanTrack, a new longitudinal pancreatic cancer benchmark, to assess out-of-distribution generalization. Experiments show that our model outperforms prior work in both fully automatic and the proposed verified tracking setting offering a clinically safe middle ground between automation and control. Code, model and dataset will be released at https://github.com/MIC-DKFZ/LongiSeg

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

0 major / 2 minor

Summary. The paper introduces a Verified Tracking paradigm for tumor lesion segmentation across serial CT scans. A clinician verifies a registration-proposed prompt that, together with the baseline lesion appearance, is fed to a model using early spatial prompt fusion and latent temporal difference weighting. Large-scale synthetic pretraining is presented as essential for exploiting longitudinal context, delivering up to 4.5 Dice-point gains over training from scratch. The method placed first in the MICCAI autoPET IV challenge; the authors also release the PanTrack pancreatic-cancer benchmark for out-of-distribution evaluation and commit to releasing code and models.

Significance. If the reported gains and challenge result hold under rigorous evaluation, the work supplies a practical, clinician-controllable middle ground between fully automatic trackers and decoupled registration-segmentation pipelines. The combination of prompt fusion, temporal-difference weighting, and synthetic pretraining, together with the public release of code, model, and a new longitudinal benchmark, constitutes a concrete, falsifiable contribution to longitudinal medical image analysis.

minor comments (2)
  1. The abstract states specific numerical gains (4.5 Dice points) and a challenge win but does not reference the corresponding experimental tables, baseline definitions, or statistical tests; the full manuscript should make these cross-references explicit in the results section.
  2. The claim that synthetic pretraining is 'proving essential' for longitudinal context exploitation would benefit from an ablation that isolates the contribution of the temporal-difference weighting when pretraining is removed.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for the positive evaluation of the Verified Tracking paradigm, the reported gains from synthetic pretraining, the challenge result, and the release of PanTrack. The minor_revision recommendation is noted; no major comments appear in the report, so we have no specific points requiring rebuttal or revision at this stage.

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The paper contains no equations, derivations, or parameter-fitting steps that could reduce predictions to inputs by construction. All performance claims (Dice gains, challenge ranking, longitudinal context exploitation) are presented as empirical outcomes validated on external benchmarks (MICCAI autoPET IV) and a newly released dataset (PanTrack), with no self-citation chains or self-definitional loops invoked to justify core components. The unified framework and synthetic pretraining are described as design choices whose value is demonstrated through independent testing rather than assumed or fitted internally.

Axiom & Free-Parameter Ledger

2 free parameters · 2 axioms · 0 invented entities

The central claim depends on the effectiveness of synthetic pretraining for real data generalization and on the clinical feasibility of prompt verification; no invented physical entities or mathematical axioms beyond standard deep learning assumptions.

free parameters (2)
  • synthetic pretraining hyperparameters
    Scale and generation parameters of the large-scale synthetic data are chosen to address data scarcity and enable longitudinal context exploitation.
  • temporal difference weighting parameters
    Latent temporal difference weighting coefficients are learned or set during training to resolve segmentation ambiguities.
axioms (2)
  • domain assumption Clinician verification of the registration-proposed prompt is accurate and available in the workflow
    The verified tracking paradigm structurally requires reliable clinician input to resolve ambiguities.
  • domain assumption Synthetic data distribution sufficiently matches real clinical CT scans for pretraining transfer
    The abstract states synthetic pretraining is essential, implying this transfer assumption underpins the performance gains.

pith-pipeline@v0.9.0 · 5774 in / 1609 out tokens · 29928 ms · 2026-05-25T05:18:23.827654+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

23 extracted references · 23 canonical work pages

  1. [1]

    CA: a cancer journal for clinicians 74(3), 229–263 (2024)

    Bray, F., Laversanne, M., Sung, H., Ferlay, J., Siegel, R.L., Soerjomataram, I., Je- mal, A.: Global cancer statistics 2022: Globocan estimates of incidence and mor- tality worldwide for 36 cancers in 185 countries. CA: a cancer journal for clinicians 74(3), 229–263 (2024)

    work page 2022

  2. [2]

    Medical Image Analysis102, 103525 (2025).https://doi.org/https: //doi.org/10.1016/j.media.2025.103525,https://www.sciencedirect.com/ science/article/pii/S1361841525000738

    de Grauw, M., Scholten, E., Smit, E., Rutten, M., Prokop, M., van Gin- neken, B., Hering, A.: The uls23 challenge: A baseline model and bench- mark dataset for 3d universal lesion segmentation in computed tomogra- phy. Medical Image Analysis102, 103525 (2025).https://doi.org/https: //doi.org/10.1016/j.media.2025.103525,https://www.sciencedirect.com/ scien...

    work page doi:10.1016/j.media.2025.103525 2025

  3. [3]

    In: International MICCAI Brainlesion Workshop

    Denner, S., Khakzar, A., Sajid, M., Saleh, M., Spiclin, Z., Kim, S.T., Navab, N.: Spatio-temporal learning from longitudinal data for multiple sclerosis lesion seg- mentation. In: International MICCAI Brainlesion Workshop. pp. 111–121. Springer (2020)

    work page 2020

  4. [4]

    Advances in Neural Information Processing Systems 37, 110746–110783 (2024)

    Du, Y., Bai, F., Huang, T., Zhao, B.: Segvol: Universal and interactive volumetric medical image segmentation. Advances in Neural Information Processing Systems 37, 110746–110783 (2024)

    work page 2024

  5. [5]

    European journal of cancer45(2), 228–247 (2009) 10 Y

    Eisenhauer, E.A., Therasse, P., Bogaerts, J., Schwartz, L.H., Sargent, D., Ford, R., Dancey, J., Arbuck, S., Gwyther, S., Mooney, M., et al.: New response evaluation criteria in solid tumours: revised recist guideline (version 1.1). European journal of cancer45(2), 228–247 (2009) 10 Y. Kirchhoff, M. Rokuss et al

    work page 2009

  6. [6]

    In: Medical Imaging with Deep Learning

    Hering, A., Peisen, F., Amaral, T., Gatidis, S., Eigentler, T., Othman, A., Moltz, J.H.: Whole-body soft-tissue lesion tracking and segmentation in longitudinal ct imaging studies. In: Medical Imaging with Deep Learning. pp. 312–326. PMLR (2021)

    work page 2021

  7. [7]

    International Jour- nal of Computer Assisted Radiology and Surgery19(9), 1689–1697 (2024)

    Hering, A., Westphal, M., Gerken, A., Almansour, H., Maurer, M., Geisler, B., Kohlbrandt, T., Eigentler, T., Amaral, T., Lessmann, N., et al.: Improving as- sessment of lesions in longitudinal ct scans: a bi-institutional reader study on an ai-assisted registration and volumetric segmentation workflow. International Jour- nal of Computer Assisted Radiolog...

    work page 2024

  8. [8]

    Isensee, F., Rokuss, M., Krämer, L., Dinkelacker, S., Ravindran, A., Stritzke, F., Hamm, B., Wald, T., Langenberg, M., Ulrich, C., Deissler, J., Floca, R., Maier- Hein, K.: nninteractive: Redefining 3d promptable segmentation (2025),https: //arxiv.org/abs/2503.08373

    work page arXiv 2025

  9. [9]

    In: proceedings of Medical Image Computing and Computer As- sisted Intervention – MICCAI 2024

    Isensee, F., Wald, T., Ulrich, C., Baumgartner, M., Roy, S., Maier-Hein, K., Jäger, P.F.: nnU-Net Revisited: A Call for Rigorous Validation in 3D Medical Image Segmentation . In: proceedings of Medical Image Computing and Computer As- sisted Intervention – MICCAI 2024. vol. LNCS 15009. Springer Nature Switzerland (October 2024)

    work page 2024

  10. [10]

    Radiology: Imaging Cancer3(5), e200160 (2021)

    Jacobs, C., Schreuder, A., van Riel, S.J., Scholten, E.T., Wittenberg, R., Wille, M.M.W., de Hoop, B., Sprengers, R., Mets, O.M., Geurts, B., et al.: Assisted versus manual interpretation of low-dose ct scans for lung cancer screening: impact on lung-rads agreement. Radiology: Imaging Cancer3(5), e200160 (2021)

    work page 2021

  11. [11]

    In: International Conference on Medical Image Computing and Computer-Assisted Intervention

    Kovacs, B., Netzer, N., Baumgartner, M., Eith, C., Bounias, D., Meinzer, C., Jäger, P.F., Zhang, K.S., Floca, R., Schrader, A., et al.: Anatomy-informed data aug- mentation for enhanced prostate cancer detection. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 531–540. Springer (2023)

    work page 2023

  12. [12]

    Küstner, T., Peisen, F., Gatidis, S., Wagner, A., Megne, O., Othman, A., San- ner, A., Loß au, T., Moltz, J.H., Kohlbrandt, T., Hering, A.: Longitudinal- ct.https://fdat.uni-tuebingen.de/records/qwsry-7t837(Mar 2025).https: //doi.org/10.57754/FDAT.qwsry-7t837, version v1, Published March 16, 2025

    work page doi:10.57754/fdat.qwsry-7t837 2025

  13. [13]

    Neuro-oncology23(9), 1560–1568 (2021)

    Lu, S.L., Xiao, F.R., Cheng, J.C.H., Yang, W.C., Cheng, Y.H., Chang, Y.C., Lin, J.Y., Liang, C.H., Lu, J.T., Chen, Y.F., et al.: Randomized multi-reader evalu- ation of automated detection and segmentation of brain tumors in stereotactic radiosurgery with deep neural networks. Neuro-oncology23(9), 1560–1568 (2021)

    work page 2021

  14. [14]

    arXiv preprint arXiv:2507.19230 (2025)

    Rocholl, N., Smit, E., Prokop, M., Hering, A.: Unstable prompts, unreli- able segmentations: A challenge for longitudinal lesion analysis. arXiv preprint arXiv:2507.19230 (2025)

    work page arXiv 2025

  15. [15]

    In: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)

    Rokuss, M., Kirchhoff, Y., Akbal, S., Kovacs, B., Roy, S., Ulrich, C., Wald, T., Rotkopf, L.T., Schlemmer, H.P., Maier-Hein, K.: Lesionlocator: Zero-shot universal tumor segmentation and tracking in 3d whole-body imaging. In: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR). pp. 30872–30885 (June 2025)

    work page 2025

  16. [16]

    In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (June 2026)

    Rokuss, M., Langenberg, M., Kirchhoff, Y., Isensee, F., Hamm, B., Ulrich, C., Regnery,S.,Bauer,L.,Katsigiannopulos,E.,Norajitra,T.,Maier-Hein,K.:Voxtell: Free-text promptable universal 3d medical image segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (June 2026)

    work page 2026

  17. [17]

    In: International Conference on Medical Image Computing and Computer-Assisted Intervention

    Rokuss, M.R., Kirchhoff, Y., Roy, S., Kovacs, B., Ulrich, C., Wald, T., Zenk, M., Denner, S., Isensee, F., Vollmuth, P., Kleesiek, J., Maier-Hein, K.: Longitudinal Longitudinal Context in Verified Lesion Tracking 11 segmentation of ms lesions via temporal difference weighting. In: International Conference on Medical Image Computing and Computer-Assisted I...

    work page 2024

  18. [18]

    Medical Image Analysis83, 102675 (2023)

    Szeskin, A., Rochman, S., Weiss, S., Lederman, R., Sosna, J., Joskowicz, L.: Liver lesion changes analysis in longitudinal cect scans by simultaneous deep learning voxel classification with simu-net. Medical Image Analysis83, 102675 (2023)

    work page 2023

  19. [19]

    In: International Conference on Medical Image Computing and Computer-Assisted Intervention

    Tian, L., Greer, H., Kwitt, R., Vialard, F.X., San José Estépar, R., Bouix, S., Rushmore, R., Niethammer, M.: unigradicon: A foundation model for medical im- age registration. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 749–760. Springer (2024)

    work page 2024

  20. [20]

    In: International Conference on Medical Image Computing and Computer-Assisted Intervention

    Vizitiu, A., Mohaiu, A.T., Popdan, I.M., Balachandran, A., Ghesu, F.C., Comani- ciu, D.: Multi-scale self-supervised learning for longitudinal lesion tracking with optional supervision. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 573–582. Springer (2023)

    work page 2023

  21. [21]

    Wong, H.E., Rakic, M., Guttag, J., Dalca, A.V.: Scribbleprompt: Fast and flexible interactive segmentation for any biomedical image (2024),https://arxiv.org/ abs/2312.07381

    work page arXiv 2024

  22. [22]

    In: International conference on medical image computing and computer-assisted intervention

    Wu, Y., Wu, Z., Shi, H., Picker, B., Chong, W., Cai, J.: Coactseg: Learning from heterogeneous data for new multiple sclerosis lesion segmentation. In: International conference on medical image computing and computer-assisted intervention. pp. 3–13. Springer (2023)

    work page 2023

  23. [23]

    IEEE Transactions on Medical Imaging41(10), 2658–2669 (2022)

    Yan, K., Cai, J., Jin, D., Miao, S., Guo, D., Harrison, A.P., Tang, Y., Xiao, J., Lu, J., Lu, L.: Sam: Self-supervised learning of pixel-wise anatomical embeddings in radiological images. IEEE Transactions on Medical Imaging41(10), 2658–2669 (2022)

    work page 2022