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REVIEW 4 major objections 4 minor 58 references

A Self-training Framework for Semi-supervised Pulmonary Vessel Segmentation and Its Application in COPD

T0 review · 4 major / 4 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read Iterative self-training lifts pulmonary vessel segmentation precision on COPD CT scans to 90.3%, and the resulting vessel metrics separate GOLD severity levels.

desk verdict A decent applied extension of ST++ to pulmonary vessel segmentation, undermined by a missing ablation that leaves the precision-gain mechanism unproven. read the letter →

arxiv 2507.19074 v1 pith:JQJ2KPPS submitted 2025-07-25 eess.IV cs.CV

Shuiqing Zhao , Meihuan Wang , Jiaxuan Xu , Jie Feng , Wei Qian , Rongchang Chen , Zhenyu Liang , Shouliang Qi
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Yanan Wu
This is my paper · ORCID
classification eess.IVcs.CV
keywords semi-supervisedlearningself-trainingpulmonaryvesselsegmentationCOPDCTimagingpseudo-labelselectionteacher-studentmodelquantification
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 aims to show that a semi-supervised teacher-student loop can segment pulmonary vessels from CT scans of COPD patients almost as well as full supervision while using far fewer human annotations. The central claim is that two rounds of self-training—called Semi2—raise segmentation precision from 88.0% to 90.3% on a 10-patient test set, with Dice overlap nearly unchanged, by selecting pseudo-labels that the teacher's checkpoints agree on and that pass a precision threshold. If true, the practical payoff is a precise vessel segmenter built from only 12 labeled scans plus unlabeled ones, and vessel measurements that distinguish disease severity. The paper also reports that vessel metrics computed from Semi2 outputs, including a drop in BV5/TBV from 0.5701 to 0.5213 as GOLD grade rises, are statistically different across severity groups, which it offers as evidence of clinical usefulness.

What carries the argument

The load-bearing mechanism is the pseudo-label reliability filter, not the network architecture itself. For each unlabeled CT scan, the teacher's $K$ saved checkpoints each output a pseudo-mask, and the paper computes a stability score $s_i = \sum_{j=1}^{K-1}\operatorname{meanIoU}(M_{ij}; M_{iK})$ between checkpoint predictions; scans whose pseudo-labels pass a mean-precision threshold are declared reliable and enter the student's training set. The student is trained on the 12 labeled scans plus the selected pseudo-labeled scans with weak or strong augmentation and a hard-region-adaptation loss that concentrates cross-entropy on difficult vessel borders, then the student becomes the teacher for the next iteration. Choosing by precision rather than Dice is what suppresses false-positive vessel voxels, which the paper argues matters most for downstream COPD quantification.

What would settle it

On the 10 fully annotated test scans, have two radiologists independently count false-positive vessel fragments in the fully supervised, Semi1, and Semi2 outputs; if the 2.3-point precision gain is not matched by a drop in these fragment counts, the reported improvement is an artifact of the precision-based pseudo-label filter rather than a real reduction in false vessels.

Watch

Extended reading notes

Core claim

On its own terms, the paper establishes that precision-filtered self-training is the active ingredient behind a 2.3-percentage-point precision gain in pulmonary vessel segmentation. A teacher model trained on 12 labeled non-contrast CT scans produces pseudo-labels for 103 unlabeled scans; for each scan, the teacher's saved checkpoints yield a stability score, and only the 40 pseudo-labels with mean precision above 0.9 (first round) or mean precision above 0.95 and mean Dice above 0.85 (second round) are kept. After two iterative retraining rounds, the student reaches 90.3% precision on the 10-scan test set versus 88.0% for the fully supervised teacher, while Dice moves only from 0.855 to 0.850 and IoU from 0.747 to 0.740. The same segmentations feed a vessel-quantification step, producing total blood volume, surface area, BV5/TBV, and radius-binned segment counts; these show statistically significant differences between GOLD 1-4 groups, including a fall in the small-vessel fraction BV5/TBV and a rise in the count of the largest radius bin R(3-4) as severity increases.

Load-bearing premise

The load-bearing premise is that pseudo-labels selected because they agree with the teacher's own checkpoints and pass a precision threshold are reliable enough to teach a student that will also perform better on human-annotated test images.

Editorial extensions

If this is right

  • A precision-focused self-training loop can cut annotation cost: 12 labeled CT scans plus unlabeled scans outperform a fully supervised model trained on the same 12 labels in precision (90.3% vs 88.0%) while keeping Dice and IoU nearly flat.
  • Vessel metrics from the improved segmentations separate GOLD severity groups on several parameters, with BV5/TBV falling from 0.5701 (GOLD 1) to 0.5213 (GOLD 4).
  • The count of the largest vessels, radius bin R(3-4), rises with severity from 7 to 17 segments and is significantly different at GOLD 4, pointing to vascular remodeling rather than only pruning.
  • Because the selection criterion targets precision, the same scheme is a template for tubular-structure segmentation tasks where false positives are the dominant clinical cost.

Reading between the lines

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

  • Because the selection threshold (mean precision) is the same quantity reported as the gain, part of the reported improvement may be the model optimizing the selection metric rather than learning anatomically better vessels; a skeleton-based precision metric or radiologist review of false-positive fragments would separate the two.
  • The fall in sensitivity from 0.832 to 0.804 across iterations shows the precision gain is partly a precision-recall tradeoff; a future version could add a per-scan recall floor to protect the smallest distal vessels.
  • The same stability-score selection could be applied to other branching structures, such as airways or coronary arteries, since the filter does not encode vessel-specific anatomy.
  • The BV5/TBV decline across GOLD grades could be amplified by segmentation bias, because emphysematous low-density regions change the error pattern; external datasets or pathology correlation would test whether the biomarker is real or an artifact of the segmenter.
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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

4 major / 4 minor

Summary. The paper proposes a semi-supervised self-training framework, Semi2, for pulmonary vessel segmentation in non-contrast CT images of COPD patients. A teacher model is first trained on 12 interactively annotated CT scans; it then generates pseudo-labels for 103 unlabeled scans, from which 40 are selected per iteration using a precision-based criterion (Mean Precision > 0.9 in iteration 1; Mean Precision > 0.95 and Mean Dice > 0.85 in iteration 2). The student is retrained on labeled plus selected pseudo-labeled data, and the process is repeated once more. Using nnFormer as the backbone and HRA loss, the authors report that Semi2 improves precision from 0.880 (fully supervised) to 0.903 on a 10-patient test set, with Dice and IoU remaining nearly unchanged. The paper also applies the segmentation to quantify pulmonary vessel parameters across GOLD stages 1–4, reporting statistically significant differences in several metrics such as TBV, BV5/TBV, and radius-bin counts. The code is promised to be released.

Significance. If the reported precision gain is real and caused by the proposed pseudo-label selection strategy, the work has practical value for reducing false positives in pulmonary vessel segmentation, which is clinically relevant for COPD quantification. The interactive annotation pipeline and the downstream vessel-parameter analysis across GOLD stages are useful contributions. The paper also makes a machine-checkable claim (precision 0.903 vs 0.880) and commits to code release, which aids reproducibility. However, the central mechanism claim is not yet supported by the experimental design: no control isolates the effect of precision-based selection from simply adding more pseudo-labeled data or performing more training iterations. The statistical evidence is also thin, as the test set comprises only 10 patients and no uncertainty or significance testing is reported for the segmentation metrics.

major comments (4)
  1. [Section 3.5 and Table 2] The claimed benefit of precision-based pseudo-label selection is not isolated. The comparison is Fully (12 labeled images) versus Semi1 (12 + 40 selected pseudo-labeled) versus Semi2 (12 + 80 selected). Without controls that train on 40 or 80 randomly selected pseudo-labeled images, on all 103 unlabeled images, or on images selected by a different criterion (e.g., Dice only), the precision improvement from 0.880 to 0.903 could be due simply to additional training data or to the extra iteration, rather than to the precision-based selection strategy. Add such ablations with matched amounts of pseudo-labeled data.
  2. [Abstract and Table 2] The abstract's word 'significantly' is not backed by statistical testing. The segmentation metrics are reported only as point estimates on a 10-patient test set, with no standard deviations, confidence intervals, or significance tests. Provide per-patient results (e.g., box plots) and a paired statistical test (e.g., Wilcoxon signed-rank) comparing Fully and Semi2 for precision, Dice, IoU, and sensitivity.
  3. [Algorithm 1 and Section 3.5] There is an inconsistency between the pseudocode and the text. Algorithm 1 assigns D_u2 = D_u - D_u1 and then trains the student on (D_l ∪ D_u1 ∪ D_u2), implying that all 63 remaining unlabeled images are used in the second iteration. Section 3.5 states that only 40 reliable pseudo-labels are selected in the second iteration with specified Mean Precision and Mean Dice thresholds. Clarify whether the remaining 23 images are discarded, and modify the pseudocode to include the selection step for both iterations.
  4. [Section 3.5 and Discussion] The pseudo-label selection is self-referential: 'reliable' is defined by agreement among the teacher's own checkpoints and by precision computed against the teacher's best-model prediction, which is the same precision metric later reported as the gain. A teacher with systematic false positives could select images where those errors are consistent across checkpoints, and the student could inherit them. Although the final test evaluation is against human annotations, this confound limits the mechanistic claim that precision-based selection reduces false positives. Add an analysis that validates the selected pseudo-labels against human annotations on a subset, or explicitly discuss this limitation.
minor comments (4)
  1. [Section 3.4] Equation (1) is typeset imprecisely; the summation limits and the definition of the index n are unclear. Please rewrite the equation and define all symbols.
  2. [Section 5.4] There are typos, including 'Bonfroni' instead of 'Bonferroni' and 'GOLD' without a grade number in the ANOVA sentence ('GOLD 1, GOLD 2, GOLD, and GOLD 4').
  3. [Figure 6] The caption mentions 'a, b, and c' but the figure legend does not clearly explain these labels; please annotate the figure or explain the subprocesses in the caption.
  4. [Section 4.5] The vessel parameter calculation is said to be based on VesselVio, but the paper does not specify which segmentation model (Fully, Semi1, or Semi2) generates the vessel trees used for the COPD analysis. Please state this explicitly, as the parameter values may depend on the model.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the reported precision gain is measured on held-out human-refined test labels, not on the teacher-generated pseudo-labels used for selection.

full rationale

The central claim (Semi2 precision 0.903 vs 0.880) is evaluated in Table 2 on a held-out test set of 10 CT scans with human-refined annotations, using the standard Precision definition in Eq. (6). The pseudo-label selection in Section 3.5 is applied only to the unlabeled training images: the teacher’s checkpoints generate pseudo-labels, and “reliable” pseudo-labels are chosen by high consistency/precision with the teacher’s best model before being added to the student’s training set. This selection does not define the test-set precision, because the test labels are independent human annotations rather than the teacher’s predictions. The teacher-student retraining loop is a standard self-training design, not a derivation in which the output metric is an input by construction. The one in-house self-citation (CE-NC-VesselSegNet, [42]) is used to initialize coarse annotations, but the final labels are manually corrected by a radiologist, so it is not load-bearing for the claimed Semi2 improvement. The absence of an ablation with randomly selected pseudo-labels is a possible experimental confound for attributing the gain to the selection strategy, and the word “significantly” is not backed by a reported significance test; these are correctness and statistical-reporting concerns, not circularity. Overall, the evaluation chain is self-contained with respect to the human-labeled test set.

Assumptions & free parameters 4 free parameters · 3 assumptions · 0 invented entities

The central claim rests on hand-set thresholds for pseudo-label selection, an assumption that self-agreement equals label quality, and a small test set treated as representative. No free physical constants or new entities are involved.

free parameters (4)
  • HRA loss threshold T = 0.1
    Set by hand in Section 3.4 to define hard regions; controls which voxels contribute to the loss.
  • Pseudo-label precision and Dice thresholds = Precision > 0.9 (iteration 1); Precision > 0.95 and Dice > 0.85 (iteration 2)
    Set manually in Section 3.5 to select reliable pseudo-labels; directly shape the pseudo-labeled training set.
  • Number of selected pseudo-labels per iteration = 40
    Fixed count chosen by hand in Section 3.5; determines how many unlabeled scans are added each round.
  • Checkpoint interval = every 100 rounds
    Section 3.5 saves checkpoints every 100 rounds and picks the best by validation Dice; affects pseudo-label stability scores.
assumptions (3)
  • domain assumption The interactively annotated labels are treated as ground truth for training and testing.
    Section 3.2 describes manual refinement by a radiologist, and these labels are used as the gold standard without inter-observer variability or error analysis.
  • ad hoc to paper Agreement among a model's own checkpoints is a valid proxy for pseudo-label reliability.
    Equation (2) defines stability by mean IoU among checkpoints; this assumes self-consistency indicates correctness, which may fail for systematic biases.
  • domain assumption A 10-patient test set is representative enough to estimate segmentation performance.
    Section 4.3 uses 10 CT scans for testing; no confidence intervals or cross-validation are reported, so the estimate assumes this small sample is sufficient.

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Pith. "Pith review of A Self-training Framework for Semi-supervised Pulmonary Vessel Segmentation and Its Application in COPD." pith.science (2026). https://pith.science/paper/JQJ2KPPS

@misc{pith2026250719074,
  author       = {Pith},
  title        = {Pith review of: A Self-training Framework for Semi-supervised Pulmonary Vessel Segmentation and Its Application in COPD},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/JQJ2KPPS}},
  note         = {Machine review of arXiv:2507.19074}
}
read the original abstract

Background: It is fundamental for accurate segmentation and quantification of the pulmonary vessel, particularly smaller vessels, from computed tomography (CT) images in chronic obstructive pulmonary disease (COPD) patients. Objective: The aim of this study was to segment the pulmonary vasculature using a semi-supervised method. Methods: In this study, a self-training framework is proposed by leveraging a teacher-student model for the segmentation of pulmonary vessels. First, the high-quality annotations are acquired in the in-house data by an interactive way. Then, the model is trained in the semi-supervised way. A fully supervised model is trained on a small set of labeled CT images, yielding the teacher model. Following this, the teacher model is used to generate pseudo-labels for the unlabeled CT images, from which reliable ones are selected based on a certain strategy. The training of the student model involves these reliable pseudo-labels. This training process is iteratively repeated until an optimal performance is achieved. Results: Extensive experiments are performed on non-enhanced CT scans of 125 COPD patients. Quantitative and qualitative analyses demonstrate that the proposed method, Semi2, significantly improves the precision of vessel segmentation by 2.3%, achieving a precision of 90.3%. Further, quantitative analysis is conducted in the pulmonary vessel of COPD, providing insights into the differences in the pulmonary vessel across different severity of the disease. Conclusion: The proposed method can not only improve the performance of pulmonary vascular segmentation, but can also be applied in COPD analysis. The code will be made available at https://github.com/wuyanan513/semi-supervised-learning-for-vessel-segmentation.

Figures

Figures reproduced from arXiv: 2507.19074 by the authors.

Figure 6
Figure 6. The pipeline of semi-supervised learning method in the study. 4. Experiments 4.1 Datasets All participants willingly provided their informed consent, adhering to the ethical principles outlined in the Declaration of Helsinki (2000). The medical ethics committee of the First Affiliated Hospital of Guangzhou Medical University thoroughly reviewed and approved the study, ensuring that it met the highest standards of et… view at source ↗
Figure 9
Figure 9. Statistical differences in vessel metrics across GOLD 1-4. 6. Discussion In this work, a semi-supervised learning framework is proposed that markedly enhances the segmentation of the full pulmonary vessel. The proposed method focuses on capturing the intricacies of smaller vessels. The high-quality pulmonary vessel labels with interaction annotation and robust segmentation model provide a prerequisite for quantifyin… view at source ↗

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Works this paper leans on

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Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.