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Multi-Class Segmentation of Aortic Branches and Zones in Computed Tomography Angiography: The AortaSeg24 Challenge

T0 review · 4 major / 5 minor · reviewed 2026-08-08 · deepseek-v4-flash

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Pith's one-line read The AortaSeg24 challenge releases the first open dataset of 100 CTA volumes with 23 expert-annotated aortic branches and zones, and shows top algorithms reach about 0.78 average Dice.

desk verdict AortaSeg24 delivers the first open multi-class aortic dataset and a solid challenge benchmark; the annotation-consistency gap and unquantified CIS-UNet comparison are the two things to fix in review. read the letter →

arxiv 2502.05330 v1 pith:HMSJF6NU submitted 2025-02-07 eess.IV cs.AIcs.CVcs.LG

Muhammad Imran , Jonathan R. Krebs , Vishal Balaji Sivaraman , Teng Zhang , Amarjeet Kumar , Walker R. Ueland , Michael J. Fassler , Jinlong Huang
show 55 more authors
Xiao Sun Lisheng Wang Pengcheng Shi Maximilian Rokuss Michael Baumgartner Yannick Kirchhof Klaus H. Maier-Hein Fabian Isensee Shuolin Liu Bing Han Bong Thanh Nguyen Dong-jin Shin Park Ji-Woo Mathew Choi Kwang-Hyun Uhm Sung-Jea Ko Chanwoong Lee Jaehee Chun Jin Sung Kim Minghui Zhang Hanxiao Zhang Xin You Yun Gu Zhaohong Pan Xuan Liu Xiaokun Liang Markus Tiefenthaler Enrique Almar-Munoz Matthias Schwab Mikhail Kotyushev Rostislav Epifanov Marek Wodzinski Henning Muller Abdul Qayyum Moona Mazher Steven A. Niederer Zhiwei Wang Kaixiang Yang Jintao Ren Stine Sofia Korreman Yuchong Gao Hongye Zeng Haoyu Zheng Rui Zheng Jinghua Yue Fugen Zhou Bo Liu Alexander Cosman Muxuan Liang Chang Zhao Gilbert R. Upchurch Jr. Jun Ma Yuyin Zhou Michol A. Cooper Wei Shao
This is my paper · ORCID
classification eess.IVcs.AIcs.CVcs.LG
keywords aorticsegmentationcomputedtomographyangiographymulti-classAortaSeg24challengeSVS/STSzonesdissectionbenchmarkdatasetmedicalimage
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 reports the AortaSeg24 challenge, built around the first publicly released dataset of 100 computed tomography angiography (CTA) volumes with expert annotations of 23 aortic branches and SVS/STS zones. The authors' claim is that this resource turns multi-class aortic segmentation from a private, binary-label exercise into a standard, openly comparable task. That matters because aortic dissection treatment planning requires per-zone and per-branch diameters and volumes, which binary whole-aorta segmentation cannot provide. The paper evaluates 16 algorithms on 40 hidden test volumes using overlap and boundary metrics, and makes the data, evaluation code, and top methods publicly available.

What carries the argument

The argument runs on three coupled objects. The first is the annotation protocol: annotators first traced the aorta and its thirteen branches, then applied a logical-operator division of the aorta into ten SVS/STS zones defined by branch landmarks, producing a single coherent 23-label map in which zones and branches are geometrically coupled. The second is the evaluation metric pair, DSC for volumetric overlap and NSD with a 2 mm tolerance for boundary alignment, which together penalize both missing volume and surface drift. The third is the rank-then-aggregate ranking procedure, which ranks every team on every test case before averaging, making the leaderboard robust to a few extreme outlier cases. These pieces convert an anatomical vocabulary into a reproducible measurement task.

What would settle it

Independent re-annotation of a random subset of the 100 volumes by two vascular surgeons, followed by measuring inter-observer DSC on the 23 labels, would settle the benchmark's validity; if the inter-observer agreement is comparable to the 0.01–0.03 DSC gaps that separate the top algorithms, the leaderboard could be annotation noise rather than algorithmic skill.

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Extended reading notes

Core claim

The paper's central claim is that a 23-class labeling of the aorta—thirteen branches plus the ten SVS/STS zones whose boundaries are defined by branch origins—can be established as an open benchmark for computed tomography angiography. On 40 hidden test volumes, the best of 16 submitted algorithms achieved an average Dice Similarity Coefficient of 0.782 and Normalized Surface Distance of 0.817, with the top five methods statistically indistinguishable from each other but clearly ahead of the rest. The authors present this as evidence that multi-class aortic segmentation is clinically and technically feasible, and they release the 100 annotated volumes, evaluation code, and winning implementations so that future work can be measured against the same yardstick.

Load-bearing premise

The load-bearing premise is that the ground-truth labels are accurate enough to rank algorithms; the paper reports a single expert's final edit as truth and gives no quantitative inter-observer variability analysis, while later acknowledging that inter-observer variability may have introduced inconsistencies.

Editorial extensions

If this is right

  • Future multi-class aortic segmentation papers can be compared directly on the same 40 test volumes instead of on private datasets.
  • Because zone boundaries are defined by branch origins, predicted 23-class masks directly enable automated per-zone diameter and volume measurement, the quantities used for stent-graft sizing and rupture-risk assessment.
  • The public release lets clinical centers retrain or fine-tune the top models on their own CTA protocols and test generalization.
  • The evaluation code and rank-then-aggregate procedure give other anatomical segmentation challenges a ready-made template for stable leaderboards.

Reading between the lines

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

  • What the paper does not do is validate the predicted masks against direct clinical measurements, so an immediate next step is to compute zone-wise diameters from the top models' outputs and compare them with manual vascular measurements on the same 40 test cases; that would test the clinical usability claim directly.
  • Because the dataset is limited to uncomplicated type B dissections, the reported scores are disease-specific; extrapolating to type A dissections, aneurysms, or intramural hematoma requires a new benchmark built on those pathologies.
  • The branch-first-then-zones annotation order means any systematic error in branch termination propagates into zone labels; a sensitivity analysis that perturbs branch endpoints and re-derives zones would show how much of the ranking depends on this geometric coupling.
  • The small performance gaps among the top five algorithms suggest that a clinically weighted metric, weighting small branches such as renal arteries more heavily, might produce a more decision-relevant leaderboard than global DSC.
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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 / 5 minor

Summary. The paper describes AortaSeg24, a MICCAI 2024 challenge for multi-class segmentation of the aorta into 23 branches and SVS/STS zones. The authors introduce a dataset of 100 CTA volumes from patients with uncomplicated type B aortic dissections, annotated via a multi-stage process culminating in expert surgeon review. Sixteen teams submitted Docker containers evaluated on a hidden 40-case test set with Dice Similarity Coefficient (DSC) and Normalized Surface Distance (NSD, tau=2), using a rank-then-aggregate ranking method. The paper presents the top five algorithms' technical approaches, final rankings, qualitative results, and statistical analyses of ranking stability (significance maps and bootstrap). The dataset, evaluation code, and leading methods are made publicly available.

Significance. If the dataset's annotations are of sufficient quality, AortaSeg24 addresses a genuine gap: no publicly available dataset currently offers multi-class labeling of the entire aorta with branches and zones. The challenge design is rigorous: a hidden test set, metric selection (DSC and NSD), rank-then-aggregate ranking, and stability analyses (Wilcoxon/Holm and bootstrap) are appropriate and largely transparent. The public release of evaluation code and top team implementations further supports reproducibility. The main risks are that ground-truth annotation consistency is unmeasured, and the paper's more ambitious claims (state-of-the-art surpassing) are not backed by quantitative comparisons. With additional validation, this could become a standard benchmark for aortic segmentation.

major comments (4)
  1. [§3.2.3, §5.4] The ground-truth annotations are the yardstick for all reported DSC/NSD scores, yet the paper provides no inter-observer variability or annotation-consistency analysis. Section 3.2.3 describes a pipeline in which four trainees split the 100 volumes and a single expert vascular surgeon performed final edits, but no quantitative agreement statistics are reported. The paper itself concedes in §5.4 that 'inter-observer variability and difficulties in segmenting smaller aortic branches may have introduced inconsistencies.' This is load-bearing because systematic label noise, especially in small branches such as the renal arteries, could alter the algorithm ranking and the dataset's clinical utility. The authors should add a reproducibility sub-study (e.g., re-annotation of a subset by an independent expert with per-label DSC/NSD between annotators) or explicitly qualify the dataset as single-annotator with unmeasured variability.
  2. [§4.3, §5.3] The text in §4.3 states that A1 'consistently outperformed all others,' but the significance maps in §5.3 (Figure 7) show 'no significant differences were observed among the top five algorithms.' These statements should be reconciled. The rank-then-aggregate method can legitimately produce an ordering even when pairwise differences are not statistically significant, but the paper should state that the within-top-five order is not statistically supported and avoid language suggesting consistent superiority. This is important because Figure 8 and the narrative present the final rankings as robust.
  3. [Table 4, §4.3] Only label-averaged DSC and NSD are reported. Given the clinical motivation—diameter measurement in specific branches and zones—and the acknowledged difficulty with smaller branches, the benchmark's utility cannot be assessed without per-label performance. This is especially relevant for the renal arteries, superior mesenteric artery, and iliac branches, where errors may be masked by averaging. The paper should include per-label score tables (or a supplementary figure) and, ideally, per-label significance analyses.
  4. [§5.2] The claim that the top-performing teams 'surpass the current state-of-the-art CIS-UNet model' is not quantified. No test-set or cross-validation performance of CIS-UNet is reported anywhere in the paper, and CIS-UNet is the authors' own method (Imran et al., 2024; Krebs et al., 2024). A reader cannot verify this superiority claim. Either provide the CIS-UNet baseline numbers on the AortaSeg24 test set (or an equivalent comparison) or remove this assertion.
minor comments (5)
  1. [§4.5] The sentence 'All methods used the same architecture, nnU-Net' is contradicted by Table 3, which shows A5 uses a SegResNet component. Please rephrase to 'most methods used nnU-Net variants' or describe the architectural differences accurately.
  2. [§3.3] The NSD tolerance is set to tau=2 without rationale. A brief justification (e.g., clinical relevance or prior challenge conventions) would help readers interpret the NSD values.
  3. [§3.2.1] The reported range '512×512×245 to 512×512×962' uses nonstandard formatting; please clarify the intended dimensions. Also, the average slice thickness statement could be clearer given the range of 0.8 to 2.52 mm.
  4. [Figure 7] The significance maps are referenced but not shown in the manuscript text; ensure they are included in the final PDF so that the claims in §5.3 can be inspected.
  5. [§3.1] The dataset is described as 'open' but access requires a signed data use agreement and identity verification. Please clarify the access terms in the abstract or dataset availability statement.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the dataset claim and challenge rankings rest on independent annotation and hidden-test evaluation; the only self-citation is non-load-bearing.

full rationale

The AortaSeg24 paper is a challenge report and dataset release, not a derivation from first principles. Its central claims are (1) that 100 CTA volumes were annotated for 23 clinically relevant aortic branches and zones, (2) that participant algorithms were evaluated on 40 hidden test cases using DSC and NSD, and (3) that the dataset, evaluation code, and leading implementations are public. These claims are grounded in the described annotation pipeline in Section 3.2.3 and in the Grand Challenge evaluation workflow in Section 3.1 and 4.1, where participant Docker images were scored on test cases that 'remained entirely unseen by participants throughout the challenge.' The ground-truth labels were produced by manual expert editing, not by any equation fitted to the benchmark outputs. The challenge ranking is computed from independent participant submissions, so it is not forced by the organizers' own model. The only self-citation appears in Section 5.2, where CIS-UNet (Imran et al., 2024; Krebs et al., 2024) is called the current state-of-the-art and the top-performing teams are said to surpass it. This is a comparative claim made by the authors about their own prior work; it is not load-bearing for the dataset's existence, the annotation procedure, or the ranking computation. The paper explicitly acknowledges in Section 5.4 that inter-observer variability and difficulties in segmenting smaller branches may have introduced inconsistencies; this is a validity limitation, not a circularity. No equation is defined in terms of the result it purports to establish, no fitted parameter is renamed as a prediction, and no uniqueness theorem is imported from the authors' prior work. The benchmark is therefore self-contained against external validation, and there is no specific reduction from an output back to an input that would constitute circularity.

Assumptions & free parameters 1 free parameters · 4 assumptions · 0 invented entities

The paper introduces no new physical or model entities. The only hand-chosen numeric input is the NSD tolerance (tau=2). The central claims rest on clinical and domain assumptions about the SVS/STS zone definitions, the expert annotations being a valid gold standard, representativeness of the single-center test set, and adequacy of the evaluation metrics. No invented entities are introduced.

free parameters (1)
  • NSD tolerance tau = 2 mm
    Hand-selected tolerance for boundary matching in the Normalized Surface Distance metric (Section 3.3); not fitted to data, but it influences NSD scores and therefore the final algorithm rankings.
assumptions (4)
  • domain assumption SVS/STS zone definitions as implemented in Table 2 define the 23 classes.
    Ground truth labels depend on these clinical definitions; if applied inconsistently, labels are wrong. Invoked in Section 3.2.2.
  • domain assumption The expert vascular surgeon's final edited annotations are treated as ground truth.
    Section 3.2.3 describes a single expert's final review, and no inter-observer variability or consensus measurement is reported.
  • domain assumption The 40 hidden test volumes are representative of the target clinical population.
    Test images come from the same single institution and the same uTBAD inclusion criteria as training and validation; Section 5.4 acknowledges limited generalizability.
  • domain assumption DSC and NSD with tau=2 adequately capture clinically relevant segmentation quality.
    These are standard challenge metrics, but no clinical outcome validation links them to treatment decisions; Section 3.3 defines the metrics.

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Cite this review

Pith. "Pith review of Multi-Class Segmentation of Aortic Branches and Zones in Computed Tomography Angiography: The AortaSeg24 Challenge." pith.science (2026). https://pith.science/paper/HMSJF6NU

@misc{pith2026250205330,
  author       = {Pith},
  title        = {Pith review of: Multi-Class Segmentation of Aortic Branches and Zones in Computed Tomography Angiography: The AortaSeg24 Challenge},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/HMSJF6NU}},
  note         = {Machine review of arXiv:2502.05330}
}
read the original abstract

Multi-class segmentation of the aorta in computed tomography angiography (CTA) scans is essential for diagnosing and planning complex endovascular treatments for patients with aortic dissections. However, existing methods reduce aortic segmentation to a binary problem, limiting their ability to measure diameters across different branches and zones. Furthermore, no open-source dataset is currently available to support the development of multi-class aortic segmentation methods. To address this gap, we organized the AortaSeg24 MICCAI Challenge, introducing the first dataset of 100 CTA volumes annotated for 23 clinically relevant aortic branches and zones. This dataset was designed to facilitate both model development and validation. The challenge attracted 121 teams worldwide, with participants leveraging state-of-the-art frameworks such as nnU-Net and exploring novel techniques, including cascaded models, data augmentation strategies, and custom loss functions. We evaluated the submitted algorithms using the Dice Similarity Coefficient (DSC) and Normalized Surface Distance (NSD), highlighting the approaches adopted by the top five performing teams. This paper presents the challenge design, dataset details, evaluation metrics, and an in-depth analysis of the top-performing algorithms. The annotated dataset, evaluation code, and implementations of the leading methods are publicly available to support further research. All resources can be accessed at https://aortaseg24.grand-challenge.org.

Figures

Figures reproduced from arXiv: 2502.05330 by the authors.

Figure 1
Figure 1. Schematic representation of di [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Comparison of various publicly available aortic segmentation datasets [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Overview of the participating teams at each phase. [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figures from the paper (4 more)
Figure 4
Figure 4. Figure 4: Box plot showing the distribution of Dice scores for 16 segmentation [PITH_FULL_IMAGE:figures/full_fig_p008_4.png]
Figure 5
Figure 5. Figure 5: Box plot showing the distribution of NSD scores for 16 segmentation [PITH_FULL_IMAGE:figures/full_fig_p009_5.png]
Figure 6
Figure 6. Figure 6: Qualitative results for a challenging case, showing 3D outputs and sagit [PITH_FULL_IMAGE:figures/full_fig_p009_6.png]
Figure 8
Figure 8. Figure 8: Violin plots for visualizing ranking stability based on bootstrapping in [PITH_FULL_IMAGE:figures/full_fig_p010_8.png]

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    write newline

    " write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 gl...

Pith tools

Reviewed August 8, 2026 · model on record in the stance chip above.