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REVIEW 4 major objections 7 minor 25 references

Enhancing Coronary Artery Calcium Scoring via Multi-Organ Segmentation on Non-Contrast Cardiac Computed Tomography

T0 review · 4 major / 7 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read By segmenting the heart's anatomy before scoring calcium, the paper claims automated CAC scoring reaches the accuracy of human observers.

desk verdict A genuinely interpretable per-vessel CAC pipeline with a real external benchmark result; the internal validation is weaker than the paper claims because the ground truth and the method share a filtering assumption. read the letter →

arxiv 2501.11428 v1 pith:RVM3ZL25 submitted 2025-01-20 cs.CV cs.AIcs.LG

Jakub Nalepa , Tomasz Bartczak , Mariusz Bujny , Jaros{l}aw Gośliński , Katarzyna Jesionek , Wojciech Malara , Filip Malawski , Karol Miszalski-Jamka
show 2 more authors
Patrycja Rewa Marcin Kostur
This is my paper · ORCID
classification cs.CVcs.AIcs.LG
keywords coronaryarterycalciumscoringAgatstonscorenon-contrastCTmulti-organsegmentationinterpretableAIper-vessellabelingorCachallenge
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 argues that the standard recipe for automated coronary artery calcium (CAC) scoring, which treats calcification detection as a direct lesion-segmentation problem, leaves accuracy and trust on the table. It proposes instead an anatomically-informed pipeline that first segments the pericardium, aorta, and coronary arteries in non-contrast cardiac CT, then uses those structures to filter, localize, and label every high-density voxel before computing the Agatston score. On a 534-scan multi-vendor dataset and the orCaScore benchmark, the authors report agreement with manual scoring at the inter-observer level (overall ICC 1.00, Pearson correlation 0.9997) and state that the approach outperforms the previous best orCaScore algorithm. If correct, the method delivers not only accurate total and per-vessel calcium scores but also segmentation maps a clinician can inspect and verify, addressing a known blind spot of black-box CAC models.

What carries the argument

The load-bearing mechanism is an anatomy-first filtering and labeling cascade. A 130 HU threshold produces calcification candidates; segmentation masks of the pericardium and coronary arteries, obtained with nnU-Net models, discard candidates outside the heart or too far from an artery; CNNs localize the aortic valve center and the two coronary ostia; an Euclidean-distance separation plane splits each ostial calcification into aortic and coronary parts; and a skeletonized, graph-organized model of the left coronary tree, traversed with logistic-regression junction classification, assigns each remaining calcification to one of four territories. The coronary artery mask is the central object: both the false-positive filter and the final label assignment are defined by distance to it, so its quality bounds the interpretability and accuracy of the whole pipeline.

What would settle it

Run the pipeline on a test set whose ground truth is produced by manual expert delineation alone, without any automated thresholding or distance-to-coronary-artery seeding during annotation, and compare total and per-vessel Agatston agreement against the paper's reported ICC 1.00 and Pearson 0.9997; a drop below inter-observer agreement would show the reported accuracy depends on the annotation protocol rather than on the anatomy-informed method itself.

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Extended reading notes

Core claim

The central discovery is that precise multi-organ segmentation of the heart, rather than end-to-end calcification detection, is sufficient to bring automated CAC scoring to the accuracy of a human observer while preserving interpretability. The authors claim their pipeline extracts calcification candidates by thresholding at 130 HU, removes false positives using dilated pericardium and coronary-artery masks, splits ostial calcifications on a separation plane between aorta and artery, and labels the remaining lesions as RCA, LM, LAD, or LCx using a skeletonized vessel-tree graph. In their experiments the total Agatston score agrees almost perfectly with expert ground truth and the segmentation metrics equal or exceed those of the reference observer; on the orCaScore challenge tests the method is the best available automated result, reaching inter-observer accuracy.

Load-bearing premise

The ground-truth labels on the authors' own test set were seeded by automated 130 HU thresholding and distance filtering to a coronary-artery mask, then manually corrected; if that seed mask came from the same kind of segmentation the pipeline itself relies on, the test metrics may partly reward the pipeline's own filtering assumptions rather than measuring it against fully independent human judgment.

Editorial extensions

If this is right

  • Total Agatston scores can be computed automatically at near-perfect agreement with expert reading (ICC 1.00, Pearson 0.9997), making objective large-scale screening feasible.
  • Per-vessel labels (RCA, LM, LAD, LCx) turn a single risk number into a territory-resolved calcification map, supporting more targeted treatment decisions.
  • Anatomical filtering removes false positives from pacemaker leads, scanner noise, and aortic or mitral valve calcifications without explicit artifact models.
  • The six-minute end-to-end runtime on a single GPU is compatible with routine clinical use.
  • Visual maps of labeled calcifications let clinicians audit why a score was produced, addressing the opacity of end-to-end deep-learning scorers.

Reading between the lines

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

  • If coronary-artery segmentation quality keeps improving with more training data, the same anatomy-first cascade could plausibly transfer to non-ECG-gated or low-dose chest CT, where vessels are harder to see and current scoring is less reliable.
  • The ostia separation-plane trick is a template for splitting ambiguous calcium at other vessel or valve boundaries, e.g., quantifying mitral annular or aortic valve calcium separately from coronary calcium.
  • The pipeline's explicit dependence on a CA mask suggests a natural testable upgrade: attach an uncertainty estimate to the CA segmentation and flag cases where filtering decisions are made near its boundary, which would tell clinicians when to override the score.
  • A direct observer study could measure whether clinicians catch the pipeline's mistakes more readily with labeled vessel-tree maps than with black-box score outputs, testing the paper's core interpretability argument rather than just its accuracy.
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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 / 7 minor

Summary. This paper proposes a multi-stage, anatomy-guided pipeline for coronary artery calcium (CAC) scoring on non-contrast, ECG-gated cardiac CT. Calcification candidates are obtained by thresholding at 130 HU; nnU-Net models segment the pericardium, aorta, and coronary arteries; CNNs localize the aortic valve center and the two coronary ostia; candidates are filtered using dilated pericardium and coronary-artery masks and then labeled as aortic, RCA, LM, LAD, or LCx using a vessel-tree graph and a logistic-regression classifier; per-vessel and total Agatston scores are computed from the labeled voxels. The method is evaluated on a 534-scan multi-vendor collection (122-scan internal test set with expert ground truth), on the orCaScore Grand Challenge official test set (40 scans via an official submission), and on 24 scans where both the authors' ground truth and the challenge ground truth are available. The authors claim inter-observer-level accuracy and state-of-the-art results on orCaScore, report near-perfect agreement on the internal test set (ICC 1.00, Pearson r = 0.9997), and demonstrate rejection of pacemaker-lead, noise, and valve calcifications.

Significance. If the claims hold, this is a practically relevant contribution: interpretable per-vessel calcification maps and an externally, independently scored benchmark result, which is unusual and should be credited. The official orCaScore Grand Challenge submission anchors the core scoring claim in a way that purely internal evaluations cannot, and the 24-common-case comparison between two ground-truth sets is a good-faith attempt to address reference-standard differences. The multi-vendor 534-scan collection is substantially larger than the original challenge data. The main caveat is that the internal reference standard was built with the same distance-to-coronary-artery heuristic used by the pipeline itself, so the near-perfect internal metrics must be treated with caution until the provenance of the ground-truth coronary-artery masks and the extent of expert-only additions are disclosed. The lack of code or model release and the absence of an observer-variability baseline further temper the reproducibility and the precise wording of the 'inter-observer level' claim.

major comments (4)
  1. [§2.1, §2.2.3] The in-house reference standard is at risk of sharing the method's own filtering assumption. Section 2.1 states that ground-truth calcification proposals were obtained 'using automated thresholding (130 HU) and the distance to CA' and then manually corrected by experts, while Section 2.2.3 filters calcification candidates by intersection with a 3-mm-dilated coronary-artery (CA) mask, using CA segmentations from the same model family referenced in Section 2.2.1 (Bujny et al., 2024b). If the CA masks used in the GT proposal step come from that same segmentation approach, the internal reference standard inherits the pipeline's systematic prior: lesions that the CA model places far from the predicted arteries are underrepresented in the GT, so both false positives and false negatives of the distance-filtering module are underestimated in the internal evaluation (Table 1 ICC near 1.00, Fig. 5 r = 0.9997, Table 2 per-vessel DICE, and the risk-category kappa values). Expert correction mitigates but does not eliminate this bias, because the experts edit a proposal image rather than annotating from scratch. The official orCaScore GC row in Fig. 4(a) is independent and remains a valid supporting pillar, but it covers only 40 scans and does not by itself substantiate the 'more than 500 scans' and 'inter-observer level' claims built on the 122-scan internal set. Please (i) disclose which CA masks were used in the GT proposal step, (ii) report the proportion of GT calcification voxels or lesions added manually by the experts rather than accepted from the automated proposals, and (iii) report GT-to-GT agreement (e.g., per-scan DICE between the authors' GT and the official orCaScore GT on the 24 common cases of Fig. 4(c)) as a direct bound on this bias.
  2. [§3, Fig. 4(a)] The claim of 'inter-observer level accuracy' (abstract and Section 3) is not fully operationalized. The comparison to 'Observer 1' is a comparison to a single manual segmentation, and no observer-observer variability distribution is reported; in the standard usage, a method is at the inter-observer level when its agreement with a reference falls within the spread between pairs of human observers. On the official orCaScore data, the method's count-weighted sensitivity (0.927) is below Observer 1's (0.943), so the claim needs a precise definition and a supporting analysis. Please add a subset of scans annotated by at least two observers and report the distribution of pairwise observer agreement against which the method's agreement can be judged, or temper the claim accordingly.
  3. [§3, Fig. 4(a)] The statement that the technique 'outperforms [the 3rd place method] ... making it the best available' rests, on the official 40-scan test set, on a weighted DICE difference of 0.980 vs 0.979 (Fig. 4(a)), which is within rounding; the material advantages are the count-weighted sensitivity (0.927 vs 0.767) and the weighted sensitivity (0.974 vs 0.970), while the weighted specificity is slightly lower (0.986 vs 0.988). Please report per-case paired differences with bootstrap confidence intervals for the headline metrics on the 40 official scans, and state the date and ranking composition of the 'currently the best' leaderboard entry, since leaderboard claims are time-indexed. Without this, the superiority claim is not yet statistically supported.
  4. [§2.2.2–§2.2.4] Several load-bearing parameters are hand-tuned and their influence on the final scores is not analyzed. Section 2.2.3 describes the pericardium and CA dilation sizes (1 mm and 3 mm) as 'experimentally fine-tuned', and Sections 2.2.2 and 2.2.4 add patch sizes (67×67×67 mm, 25×25×25 mm) and the features of the logistic-regression classifier for LCA subregion labeling without stating which data were used for tuning. Because the 3-mm CA dilation directly determines which calcification candidates survive to scoring, the multi-vendor robustness claim (Section 3) is under-supported: a scanner or protocol for which the CA segmentation is systematically narrower could change scores materially. Please specify the tuning protocol (training-only, with no test-set information) and provide a sensitivity analysis, e.g., sweeping the CA dilation over a plausible range and reporting ICC or per-subject error on a held-out subset.
minor comments (7)
  1. [Abstract] The abstract describes 'an open-source multi-vendor dataset'; the orCaScore data are publicly available for research but are not open-source in the software sense, so please rephrase.
  2. [§1.2, §2.2] The term 'end-to-end' is inaccurate for a pipeline composed of separately trained segmentation networks, regression CNNs, and hand-set geometric rules; 'multi-stage' would be more precise.
  3. [Fig. 4(a)] The '—' entries for Observer 1 and the 3rd place method in the 'our GT' columns are unexplained; the caption should clarify that those metrics are only defined where the corresponding reference standard exists.
  4. [§2.1] The scanner manufacturer 'Cannon' should be 'Canon'.
  5. [§4] The sentence 'Manual analysis of of multi-vendor CTs' contains a duplicated 'of'.
  6. [Table 1, Table 2] The LM region shows markedly lower agreement (ICC 0.85, CI 0.8–0.89; mean DICE 0.730 in Table 2) than the other vessels and than the total Agatston score; the text's 'nearly perfect agreement' should acknowledge this per-vessel degradation and discuss the difficulty of ostial splitting, which is also relevant to the interpretability claim.
  7. [§3] No data or code availability statement is provided; releasing the trained segmentation models, or at least describing the official submission route in enough detail for independent replication, would materially improve reproducibility.

Circularity Check

0 steps flagged · score 2.0 of 10

No load-bearing circularity: the central orCaScore claim is externally benchmarked, and the in-house GT filter overlap is mitigated by manual expert correction.

full rationale

The paper's central claim that the method 'obtain[s] results at the inter-observer level, surpassing the current state of the art' is supported by an official submission to the orCaScore Grand Challenge, where metrics are computed against independent challenge GT (Section 3, Fig. 4 row 'Ours (orCaScore GC GT)', '3rd place'). This external benchmark does not depend on the paper's own annotations or fitted parameters, so the central claim is not circular. The in-house test-set GT was generated by automated thresholding plus distance-to-CA proposals followed by manual expert correction and cardiologist review (Section 2.1), and although the pipeline's filtering step (Section 2.2.3) uses a similar distance-to-CA rule, the final GT is human-corrected rather than defined by the automated proposal filter, so the two are not equivalent by construction. The CA/aorta segmentation modules are cited to the authors' own prior work (Bujny et al. 2024a,b), a self-citation; however, the full pipeline is independently validated on orCaScore data, making these citations non-load-bearing for the main claim. The authors' limitation statement ('we still aim for multi-center validation') concerns generalizability rather than circularity. No enumerated circular step is present.

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

The pipeline depends on several hand-tuned dilation and patch sizes, a learned logistic regression labeling model, and the assumption that contrast-CT-derived coronary artery masks are reliable in NC CT. No new physical or algorithmic entities are invented beyond the procedural combination.

free parameters (5)
  • Pericardium dilation size = 1 mm
    Experimentally fine-tuned dilation size used to filter calcification candidates outside the heart (Section 2.2.3).
  • Coronary artery mask dilation size = 3 mm
    Chosen to compensate for CA segmentation inaccuracy before calcification filtering (Section 2.2.3).
  • Ostia localization patch size = 25 x 25 x 25 mm
    Patch size used when computing the separation plane between aorta and coronary arteries (Section 2.2.4).
  • Aortic valve center refinement patch size = 67 x 67 x 67 mm
    Refinement patch size for the second-stage aortic valve center CNN (Section 2.2.2).
  • Logistic regression classifier for LCA subregion labeling = trained weights
    Trained on manually engineered features (relative angle, thickness, length) to classify vessel-tree nodes as LM, LAD, or LCx (Section 2.2.4).
assumptions (4)
  • domain assumption Calcifications are defined as voxels with HU >= 130 and located near coronary arteries.
    Clinical standard used throughout (Section 2.2.3); the method's filtering and GT generation both depend on this definition.
  • domain assumption Coronary artery segmentation models trained on semi-manual labels from registered contrast CT are accurate enough for NC CT.
    Section 2.2.1: GT for CA segmentation obtained via registration of contrast CT coronary models with NC CT; no quantitative validation of this registration is reported.
  • ad hoc to paper The separation plane maximizing distance to aorta and CA masks approximates the true anatomical aortic/CA boundary.
    Section 2.2.4 introduces this plane as a proxy for an ill-defined anatomical boundary.
  • standard math Skeletonization and DAG traversal of the LCA mask faithfully represent vessel topology.
    Section 2.2.4 relies on Lee et al. skeletonization and assumes thinning preserves topology for labeling.

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Cite this review

Pith. "Pith review of Enhancing Coronary Artery Calcium Scoring via Multi-Organ Segmentation on Non-Contrast Cardiac Computed Tomography." pith.science (2026). https://pith.science/paper/RVM3ZL25

@misc{pith2026250111428,
  author       = {Pith},
  title        = {Pith review of: Enhancing Coronary Artery Calcium Scoring via Multi-Organ Segmentation on Non-Contrast Cardiac Computed Tomography},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/RVM3ZL25}},
  note         = {Machine review of arXiv:2501.11428}
}
read the original abstract

Despite coronary artery calcium scoring being considered a largely solved problem within the realm of medical artificial intelligence, this paper argues that significant improvements can still be made. By shifting the focus from pathology detection to a deeper understanding of anatomy, the novel algorithm proposed in the paper both achieves high accuracy in coronary artery calcium scoring and offers enhanced interpretability of the results. This approach not only aids in the precise quantification of calcifications in coronary arteries, but also provides valuable insights into the underlying anatomical structures. Through this anatomically-informed methodology, the paper shows how a nuanced understanding of the heart's anatomy can lead to more accurate and interpretable results in the field of cardiovascular health. We demonstrate the superior accuracy of the proposed method by evaluating it on an open-source multi-vendor dataset, where we obtain results at the inter-observer level, surpassing the current state of the art. Finally, the qualitative analyses show the practical value of the algorithm in such tasks as labeling coronary artery calcifications, identifying aortic calcifications, and filtering out false positive detections due to noise.

Figures

Figures reproduced from arXiv: 2501.11428 by the authors.

Figure 1
Figure 1. Overview of the proposed method. The colors correspond to specific types of [PITH_FULL_IMAGE:figures/full_fig_p006_1.png] view at source ↗
Figure 2
Figure 2. Graphical overview of the main steps of the proposed algorithm. [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Visual summary of our method to obtain labeled CA calcifications: (a) three [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figures from the paper (3 more)
Figure 4
Figure 4. Figure 4: The experimental results (a) DICE, sensitivity and specificity—mean, and the [PITH_FULL_IMAGE:figures/full_fig_p010_4.png]
Figure 5
Figure 5. Figure 5: Agatston score (AS) correlation plot for our test set: (a) for all samples, (b) for [PITH_FULL_IMAGE:figures/full_fig_p012_5.png]
Figure 6
Figure 6. Figure 6: Visual results for the orCaScore test case with the highest Agatston score [PITH_FULL_IMAGE:figures/full_fig_p013_6.png]

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    author Zhang, N. , author Yang, G. , author Zhang, W. , author Wang, W. , author Zhou, Z. , author Zhang, H. , author Xu, L. , author Chen, Y. , year 2021 . title Fully automatic framework for comprehensive coronary artery calcium scores analysis on non-contrast cardiac-gated ...

    2021

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Reviewed August 10, 2026 · model on record in the stance chip above.