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REVIEW 3 major objections 5 minor 73 references

Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain

T0 review · 3 major / 5 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read With a single labeled medical image and a set of unlabeled test images, Med-PerSAM obtains one-shot segmentation from a frozen Segment Anything Model by warping the reference mask into a dense visual prompt and iteratively refining it…

desk verdict The method is promising and well-ablated, but the headline 'one-shot, training-free' claim is not supported because the warping model trains on the full unlabeled test set and some hyperparameters are tuned on test DICE, making the comparison with inference-only baselines unfair. read the letter →

arxiv 2411.16123 v1 pith:YWPNZJ3V submitted 2024-11-25 cs.CV cs.AI

Hangyul Yoon , Doohyuk Jang , Jungeun Kim , Eunho Yang This is my paper · ORCID
classification cs.CVcs.AI
keywords one-shotsegmentationSegmentAnythingModelvisualprompttuningmedicalimagewarpingdeformableregistrationtest-timeadaptationrefinement
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 tries to show that the Segment Anything Model can be made to segment medical images from a single labeled example without fine-tuning SAM and without manual prompts. The core move is to train a lightweight warping model, using only the reference image, its mask, and the unlabeled test images, that warps the reference mask onto each test image; that warped mask becomes a dense visual prompt from which point and box prompts are derived. SAM's outputs are then used as pseudolabels to retrain the warping model, creating an iterative loop. On five 2D medical datasets this yields DICE scores from 72.9 to 92.0, outperforming both prior one-shot SAM methods and generalist segmentation foundation models. If the claim holds, practical one-shot medical segmentation becomes available to users who cannot craft anatomical prompts.

What carries the argument

The load-bearing object is the warping model $f_\theta$, a lightweight affine-plus-deformable registration network based on NICE-Trans and adapted from 3D to 2D. It is trained with a warping loss $L_{\text{warp}} = L_{\text{img}}(\hat{I}_{\text{ref}\to i}, I_i) + L_{\text{reg}}(\phi_{\text{ref}\to i})$ using SSIM or NCC similarity plus flow smoothness regularization, and an augmentation loss $L_{\text{aug}}$ that adds a DiceCE segmentation term between the warped augmented mask and the augmented mask. The trained model produces the optical flow $\phi_{\text{ref}\to i}$ and the warped mask $\hat{M}_{\text{ref}\to i} = M_{\text{ref}} \circ \phi_{\text{ref}\to i}$, which serves as the mask prompt for SAM. Erosion and dilation of that mask define candidate regions; a class-prototype cosine-similarity map $S_i$ selects $K$ positive points from eroded subregions and $K$ negative points from the dilated-minus-eroded ring, and $g_{\text{box}}$ returns the minimal enclosing rectangle. SAM receives point, box, and mask prompts together, its output becomes a new mask prompt, and after inference the SAM prediction is used as a pseudolabel to retrain the warping model through $L_{\text{retrain}} = L_{\text{train}} + L_{\text{seg}}(\hat{M}_{\text{ref}\to i}, \hat{M}_i)$. Iterating this loop transfers SAM's visual acuity back into the warping model and progressively improves the prompts.

What would settle it

Run Med-PerSAM on a medical dataset with deliberately heterogeneous capture poses or scan ranges, such as spinal CT slices spanning different vertebral levels or chest X-rays with strong rotation and zoom beyond the augmentation range; if the optical flow cannot align the reference mask to the test image, the warped mask prompt will mislead SAM and the reported DICE margin over PerSAM and Matcher should collapse or reverse.

Watch

Extended reading notes

Core claim

The central claim is that the poor performance of one-shot SAM in medical imaging comes from placing point prompts through pixel- or feature-similarity, which misplaces points in grayscale images where organs share intensities and clusters them around confident regions. Med-PerSAM claims to remove this bottleneck by learning the optical flow $f_\theta$ that registers the single reference image to each test image, warping the reference mask accordingly to produce a dense mask prompt, and extracting positive and negative point prompts from eroded and dilated subregions of that mask plus a box prompt. Using these automatically generated visual prompts, the frozen SAM reaches DICE scores of 92.0, 87.8, 74.2, 91.3, and 72.9 on Shenzhen, OdontoAI, CAMUS, JSRT, and BUU, outperforming PerSAM, Matcher, and every other compared foundation model on all five datasets without any fine-tuning of SAM or human intervention.

Load-bearing premise

The load-bearing assumption is that one reference image can be warped, pixel by pixel, into each unlabeled test image so that the reference mask rides along and lands on the target organ; when capture pose or scan range varies too much for such warping, the prompt misaligns and the whole chain breaks.

Editorial extensions

If this is right

  • Frozen SAM can be personalized for a medical dataset from one labeled image plus unlabeled images, so no task-specific fine-tuning of the 641M-parameter model is needed.
  • Dense mask prompts derived from registration are more reliable than point prompts placed by pixel similarity; the subregion-based point extraction removes the clustering and misplacement problems that hurt PerSAM and Matcher.
  • Iterative retraining with SAM outputs as pseudolabels gives the framework a self-correction capacity; when the initial mask prompt is deliberately perturbed by a factor of 0.2, repeated retraining largely closes the performance gap.
  • The method is relatively robust to domain shift: changing the reference sample to one from a different chest X-ray dataset barely changes DICE, and test-sample perturbations degrade Med-PerSAM less than the strongest baselines.
  • Extension to multi-class segmentation is direct via one-hot mask prompts per foreground class, so the same warping-plus-prompt loop applies beyond binary organs.

Reading between the lines

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

  • Beyond the paper: because the warping model is trained on the unlabeled test set, this is a test-time adaptation scheme rather than a zero-shot predictor; deploying it on a single arriving image without access to the rest of the test set would require a different training procedure.
  • Beyond the paper: the registration bottleneck suggests a concrete extension where an explicit spatial transformer or a small atlas of multiple reference samples is used to rescue the mask prompt when pose or scan-range variation is extreme, a variant that could be tested directly on the failure cases the paper acknowledges.
  • Beyond the paper: if the warping-to-prompt loop is the true source of the gain, swapping the 2D warper for a 3D one should extend the same mechanism to volumetric medical data, an open direction the paper lists as future work.
  • Beyond the paper: the comparison with PerSAM and Matcher depends on the reference-sample selection protocol, so an independent re-run across many random reference samples, rather than the ten-sample average reported, would clarify how much of the advantage is robust to reference choice.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 5 minor

Summary. The paper introduces Med-PerSAM, a one-shot visual prompt tuning framework for SAM in medical image segmentation. Given a single reference image and its mask plus an unlabeled test set, a lightweight warping model (about 19M parameters, based on NICE-Trans) is trained to align the reference to each test image, producing a warped mask that serves as a mask prompt; point and box prompts are then extracted via morphological operations and similarity-based subregion selection, and SAM's iterative predictions are used to refine prompts and to retrain the warping model with pseudolabels. Experiments on five 2D medical datasets report large DICE gains over PerSAM, Matcher, and other foundation-model baselines, with additional ablations, cross-dataset tests, and perturbation robustness studies. The central claim is that Med-PerSAM achieves superior performance 'without additional fine-tuning of SAM or manual visual prompts' in a one-shot setting.

Significance. If the reported gains were obtained under the claimed protocol, the paper would make a useful contribution: automated visual-prompt generation for SAM in medical imaging, a lightweight prompt-tuning mechanism, and an iterative refinement loop that appears to improve robustness. The manuscript is also commendably thorough in its ablations, including multiple reference samples (Table 2), cross-dataset reference swaps (Table 5), perturbation studies (Tables 6 and 15), and mask-prompt perturbation experiments (Fig. 6). However, the headline claim is not currently supported because the method trains on the unlabeled test set (transductive) and tunes key hyperparameters on test DICE, whereas the main baselines are inference-only. The comparison in Table 1 therefore conflates test-time adaptation and test-set selection with one-shot, training-free adaptation. With a corrected evaluation protocol, the method could still be valuable, but it would more accurately be characterized as a transductive/test-time-adaptation approach rather than a standard one-shot inference method.

major comments (3)
  1. [Sec. 3.1, Eq. (3); Sec. 4.3; Sec. 4.4] The warping model fθ is trained on the unlabeled test images before SAM inference. Section 3.1 states that 'training takes place between the reference sample and the test set, similar to the setting of test-time training/adaptation', and Eq. (3) minimizes Ltrain over the test images I_i. Consequently, every test image is seen as a training example, whereas PerSAM and Matcher are pure inference baselines that never train on the test set. The large margins in Table 1 therefore reflect an asymmetric comparison: Med-PerSAM receives test-time adaptation of a 19M-parameter model, while the baselines do not. To support the claim of one-shot, training-free superiority, the authors should either evaluate on a held-out split with a separate validation set, compare against transductive/test-time-adaptation baselines under the same protocol, or explicitly re-frame the method and claims as test-time adaptation rather than inference-only prompting.
  2. [Appendix C.2, Fig. 7; Sec. 4.3; Appendix B.1, Tables 12-13; Appendix C.1] Several hyperparameters are selected using the test set itself. Appendix C.2 says the number of point prompts K was chosen by varying K and measuring DICE on test images (Fig. 7), and Section 4.3 reports different K values per dataset. In addition, loss coefficients (Table 12), augmentation ranges (Table 13), and morphological kernel sizes (Appendix C.1) are dataset-specific. On small test cohorts such as CAMUS, JSRT, and BUU, even a small number of such choices can inflate reported scores. The authors should demonstrate that the reported performance is stable under a fixed hyperparameter configuration, or use a held-out validation set for selection, clearly separated from the final test evaluation.
  3. [Sec. 3.3; Sec. 4.7; Fig. 6] The iterative retraining loop uses SAM's own predictions as pseudolabels to retrain the warping model, after which those same SAM predictions are refined again. Since the final metrics are computed against external ground truth, this is not a logical circularity, but it creates a risk that the loop reinforces SAM's inductive biases rather than learning genuinely better prompts. The paper should report results with the retraining component disabled (i.e., using only the initially trained warping model and prompt generation, with and without prompt refinement) to quantify how much of the final gain is attributable to the pseudolabel loop, and ideally verify on a held-out set that the improvements from retraining generalize rather than reflecting adaptation to the specific test images.
minor comments (5)
  1. [Sec. 4.4] There are repeated typos: 'signficantly' should be 'significantly', and the sentence 'our model model consistently achieves reliable results' contains a duplicated word.
  2. [Sec. 4.5, Table 4] The heading 'Hopkin's statistics' should be 'Hopkins' statistics'. Also, the exclusion of Matcher from Table 4 is justified by different point numbers, but the comparison of clustering tendency would be more informative if Matcher were evaluated with the same number of points as the other methods.
  3. [Fig. 7] The x-axis label 'N umber of Points' contains an awkward space; it should read 'Number of Points'.
  4. [Abstract; Sec. 4.3] The phrase 'without additional fine-tuning' is ambiguous: SAM itself is indeed frozen, but the 19M-parameter warping model is trained on the test set. The wording should be clarified in the abstract and throughout, e.g., 'without fine-tuning SAM' rather than 'without additional training of the pretrained SAM or human intervention', which suggests no training occurs at all.
  5. [Sec. 4.3; Appendix B.4] The statement that replacing SAM with SAM2 yields 'similar performance' is supported by Table 14, but the discussion would benefit from reporting whether the differences are statistically significant given the small per-dataset differences.

Circularity Check

0 steps flagged · score 0.0 of 10

No circular derivation: the claimed segmentation gains are benchmarked against external ground truth, and the self-referential retraining loop is not equivalent to its inputs.

full rationale

Med-PerSAM's central claim is an empirical performance claim. Table 1 reports DICE and mIoU against public ground-truth labels from Shenzhen, OdontoAI, CAMUS, JSRT, and BUU, none of which appear in any training loss, so the reported scores cannot be reduced to the method's inputs by construction. The warping model is trained with Ltrain (Eq. 3) on the unlabeled test images, which is transductive test-time adaptation rather than a fitted parameter being renamed as a prediction; the final SAM masks are external outputs, not the warping loss targets. The pseudolabel retraining loop (Sec. 3.3) uses SAM's own predictions as supervision, so it is self-referential in mechanism, but its claimed improvements are independently checked against ground truth (Fig. 6, Table 15), so the improvement claim is not forced by definition. Test-set hyperparameter selection (App. C.2, Tables 12 and 13) is a benchmark-fairness and soundness limitation that can inflate absolute scores, but it does not make any predicted quantity equal to its fitting target. The only author self-citation (ref. [26] in Related Works) is not load-bearing for any result. Overall, no step in the paper's derivation chain is circular under the standards in the hard rules.

Assumptions & free parameters 5 free parameters · 4 assumptions · 0 invented entities

The method has no invented physical entities. Its central claim rests on several per-dataset hyperparameters listed above, on the domain assumption that inter-patient registration is feasible, and on assumptions about SAM's behavior (mask-only prompts malfunction, prototype similarity is informative). The most consequential assumption is the transductive use of the unlabeled test set for training the warping model.

free parameters (5)
  • Number of point prompts per image = 10 for bilateral organs, 5 for unilateral organs
    Chosen by measuring DICE on the test set for each candidate count (Appendix C.2, Fig. 7), then using the best values in all main experiments.
  • Loss coefficients λreg and λaug = λreg 0.6-0.9, λaug 0.1-0.5 depending on dataset
    Tuned per dataset (Table 12); directly controls warping smoothness and augmentation weight, which shape the mask prompts.
  • Morphological kernel sizes Ke and Kd = 7×7 (Shenzhen, JSRT), 5×5 (CAMUS), 3×3 (OdontoAI, BUU)
    Ad hoc per-dataset choice (Appendix C.1) that defines the candidate regions for point prompts; not justified by a principled criterion.
  • Number of retraining rounds and prompt refinement iterations = 5 retrains, 1 refinement
    Main experiment fixes these; ablation (Table 9, Fig. 6) shows refinement beyond 1 can hurt on some datasets, so the choice is dataset-specific.
  • Initial training epochs = 30 (most), 20 (CAMUS), 10 (Shenzhen perturbed)
    Per-dataset training schedules (Appendix B.4, D.1); affects the quality of the initial warped mask.
assumptions (4)
  • domain assumption Inter-patient deformable registration can align the reference image to test images sufficiently for mask transfer.
    Load-bearing premise for generating mask prompts; cited to prior registration work and acknowledged as fragile in the Conclusion for heterogeneous images.
  • domain assumption A SAM mask prompt used alone causes malfunctions, so point and box prompts are required.
    They cite a GitHub issue and this motivates the point/box extraction pipeline; no experimental verification in this paper.
  • domain assumption The class prototype vector (average SAM foreground feature of the reference) is a reliable indicator of target-object similarity in test images.
    Used for point-prompt selection via cosine similarity (Eq. 8-9); can fail if appearance differs greatly.
  • domain assumption SAM's output masks are accurate enough to serve as pseudolabels for retraining the warping model.
    Self-training loop (Section 3.3); if SAM is confidently wrong, retraining consolidates the error.

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Cite this review

Pith. "Pith review of Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain." pith.science (2026). https://pith.science/paper/YWPNZJ3V

@misc{pith2026241116123,
  author       = {Pith},
  title        = {Pith review of: Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/YWPNZJ3V}},
  note         = {Machine review of arXiv:2411.16123}
}
read the original abstract

Leveraging pre-trained models with tailored prompts for in-context learning has proven highly effective in NLP tasks. Building on this success, recent studies have applied a similar approach to the Segment Anything Model (SAM) within a ``one-shot" framework, where only a single reference image and its label are employed. However, these methods face limitations in the medical domain, primarily due to SAM's essential requirement for visual prompts and the over-reliance on pixel similarity for generating them. This dependency may lead to (1) inaccurate prompt generation and (2) clustering of point prompts, resulting in suboptimal outcomes. To address these challenges, we introduce \textbf{Med-PerSAM}, a novel and straightforward one-shot framework designed for the medical domain. Med-PerSAM uses only visual prompt engineering and eliminates the need for additional training of the pretrained SAM or human intervention, owing to our novel automated prompt generation process. By integrating our lightweight warping-based prompt tuning model with SAM, we enable the extraction and iterative refinement of visual prompts, enhancing the performance of the pre-trained SAM. This advancement is particularly meaningful in the medical domain, where creating visual prompts poses notable challenges for individuals lacking medical expertise. Our model outperforms various foundational models and previous SAM-based approaches across diverse 2D medical imaging datasets.

Figures

Figures reproduced from arXiv: 2411.16123 by the authors.

Figure 1
Figure 1. Comparison of point prompts from PerSAM [ [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Overall Framework of Med-PerSAM. (1) Initially, the warping model is trained with warping ( [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Overview of our point prompting strategy. (1) A proto [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figures from the paper (10 more)
Figure 4
Figure 4. Figure 4: Example of prompt refinement. The predicted outcome is [PITH_FULL_IMAGE:figures/full_fig_p005_4.png]
Figure 5
Figure 5. Figure 5: A qualitative comparison of the results from our model and other baseline models. Additional visualized examples of the main [PITH_FULL_IMAGE:figures/full_fig_p007_5.png]
Figure 6
Figure 6. Figure 6: DICE scores of the inferences using the original and perturbed mask prompts. A count of 0 on the x-axis indicates the performance [PITH_FULL_IMAGE:figures/full_fig_p008_6.png]
Figure 7
Figure 7. Figure 7: The relationship between the number of point prompts [PITH_FULL_IMAGE:figures/full_fig_p015_7.png]
Figure 8
Figure 8. Figure 8: Comparative examples of point prompts from PerSAM [ [PITH_FULL_IMAGE:figures/full_fig_p018_8.png]
Figure 9
Figure 9. Figure 9: Additional examples of iterative prompt refinement. The predicted mask output is indicated in yellow, while the positive and [PITH_FULL_IMAGE:figures/full_fig_p019_9.png]
Figure 10
Figure 10. Figure 10: Illustrative examples of the iterative warping model retraining process. The results are presented for (1) the model without [PITH_FULL_IMAGE:figures/full_fig_p020_10.png]
Figure 11
Figure 11. Figure 11: Further qualitative analysis of baseline and our proposed models. [PITH_FULL_IMAGE:figures/full_fig_p021_11.png]
Figure 12
Figure 12. Figure 12: Another qualitative analysis of baseline and our proposed models. [PITH_FULL_IMAGE:figures/full_fig_p022_12.png]
Figure 13
Figure 13. Figure 13: Inference results of SegGPT and our model, with examples of large differences in test samples taken from the BUU dataset. (a) [PITH_FULL_IMAGE:figures/full_fig_p023_13.png]

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Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.