Direct vascular territory segmentation on cerebral digital subtraction angiography

Aad van der Lugt; Ad van Es; Danny Ruijters; Diederik Dippel; Frank G. te Nijenhuis; Geert Lycklama a Nijeholt; Lotte Strong; Pieter Jan van Doormaal; P. Matthijs van der Sluijs; Ruisheng Su

REVIEW 4 major objections 5 minor 21 references

Direct vascular territory segmentation on cerebral digital subtraction angiography

T0 review · 4 major / 5 minor · reviewed 2026-08-15 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read A trained network maps brain territories invisible on DSA scans

desk verdict The core direction is likely right — a nnUNet can segment ICA/MCA territories on DSA and beat atlas registration — but the reference labels inherit the atlas's provenance, so the absolute accuracy claims are over-stated and the headline numbers need reconciling. read the letter →

arxiv 2508.13821 v1 pith:QM2VT7W4 submitted 2025-08-19 eess.IV

P. Matthijs van der Sluijs , Lotte Strong , Frank G. te Nijenhuis , Sandra Cornelissen , Pieter Jan van Doormaal , Geert Lycklama a Nijeholt , Wim van Zwam , Ad van Es

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Diederik Dippel Aad van der Lugt Danny Ruijters Ruisheng Su Theo van Walsum

This is my paper · ORCID

classification eess.IV

keywords digitalsubtractionangiographyvascularterritorysegmentationdeeplearningnnU-NetischemicstrokeatlasregistrationminimalintensityprojectionautoTICI

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 a deep learning model can directly segment the vascular territories of the internal carotid and middle cerebral arteries from digital subtraction angiography (DSA), even though these territories have no visible borders in the images. Training on 1,224 acquisitions from 361 stroke patients, the model beat the standard atlas-registration approach on overlap, surface distance, per-case success rate, and runtime. If the result holds, physicians performing thrombectomy could see which brain regions are at risk without waiting for atlas registration or extra imaging.

What carries the argument

The load-bearing mechanism is the nnUNet, a self-configuring U-Net architecture adapted to the data, operating on 2D minimal-intensity projections built from each DSA sequence. The training labels come from atlas-registered territory masks that were manually adjusted for size, rotation, and scaling; the ICA mask is reconstructed as the union of the anterior cerebral artery (ACA) and MCA labels. Morphological erosion, connected-component analysis, and dilation clean the predicted masks before evaluation.

What would settle it

Segment the same DSA images with reference labels derived from co-registered CT angiography rather than from a 2D atlas, and compare the model's Dice and surface distance against both label sources; if the advantage over atlas registration shrinks or vanishes against the CTA-based labels, the claimed superiority is an artifact of label bias.

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Extended reading notes

Core claim

The central claim is that an nnUNet trained on minimal-intensity projections of cerebral DSA, with labels that are manually adjusted versions of atlas-based segmentations, can predict the internal carotid artery (ICA) and middle cerebral artery (MCA) territories directly from the image. Compared with a conventional atlas registration method, the segmentation model achieved higher Dice similarity (0.96 vs 0.82 for the ICA territory), lower average surface distance, and a higher qualitative success rate (85% versus 66% on the external test set), while running in about 4 seconds instead of 141 seconds. The model relies most on the capillary phase of contrast passage; non-contrast frames produce substantially worse segmentations.

Load-bearing premise

The reference standard is assumed to be true vascular territory, but it comes from manually adjusted atlas projections, so if the atlas inherited a systematic bias, every comparison metric inherits it too.

Editorial extensions

If this is right

Replacing atlas registration in the existing reperfusion-scoring pipeline would raise the overall per-patient success rate from 66% to about 80% while cutting runtime from more than two minutes to under ten seconds.
Because the model reads all phases but performs best on capillary-phase frames, future versions could select the capillary phase when available or be trained phase-specifically to improve robustness in ICA occlusions.
The same direct-segmentation approach could be extended to other territories with unclear borders, such as the posterior cerebral artery or functional regions like Broca's and Wernicke's areas, and to other X-ray-guided interventions.
Pre-EVT images, especially with proximal ICA occlusions, remain harder than post-EVT images, so larger, more balanced training sets would be needed before relying on the model in the most occluded cases.

Reading between the lines

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

The measured margin over atlas registration may partly reflect the model learning the human editors' corrections of the same atlas labels, so a comparison against labels from an independent modality would be needed to gauge the true anatomical accuracy.
The model's strong dependence on the capillary phase suggests it is reading contrast fill of the parenchyma rather than bone or vessel silhouettes, which is consistent with learning a proxy for perfusion territory.
A testable extension would be to feed the model phase-selected capillary minimal-intensity projections only, which the phase-dependency results predict should improve segmentation quality in pre-EVT ICA-occlusion cases.
If the approach transfers to other anatomical structures, it would offer a general route to soft-tissue visualization during X-ray-guided procedures, where such anatomy is otherwise invisible.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

Colleague,

Read the DSA territory paper. The core result is plausible: a standard nnUNet trained on DSA MinIPs can segment ICA and MCA vascular territories, and it beats the autoTICI atlas registration both in overlap metrics and qualitative scoring. The speed gain is real and clinically relevant (4s vs 141s). The task framing is new—prior DSA segmentation papers go after vessels or aneurysms, not territories—and the evaluation has genuine care: patient-level splits, an external qualitative test, and public code.

The weak spot is the reference standard. The training labels are autoTICI atlases that were manually adjusted, and the comparator is the unadjusted autoTICI atlas. That shared provenance means the model's superiority partly reflects learning the human edits rather than recovering independent anatomy. The authors admit as much in the Discussion and suggest CTA-based references for future work. This does not sink the practical claim—if you want to replace the atlas inside autoTICI, a model trained on corrected atlases is a reasonable approach—but it does mean the words "accurate segmentations" in the conclusions are stronger than the evidence supports. The qualitative success-rate comparison (80% vs 66%) is less affected by this issue, and the fact that the atlas's 66% matches previously reported 64% is a good consistency check.

The reporting has avoidable sloppiness. Abstract says 85% success and ASD 13.8; the body says 80% and Table 1 says 14. Table 1 also lists an atlas ICA DSC IQR of 0.82 [0.62-0.80], which is impossible. These are minor and easily fixed, but they undermine confidence in the headline numbers.

Bottom line: the direction is likely right and the clinical motivation is concrete. Who is this for? Neurointerventional imaging researchers working on automated TICI scoring or real-time anatomical overlays. It deserves a serious referee, but the revision needs to confront the reference-standard provenance head-on and reconcile the numbers. The core experiment is worth redoing with a CTA-derived or independent ground truth before anyone leans too hard on the absolute accuracy.

Referee Report

4 major / 5 minor

Summary. This manuscript reports a deep learning approach, based on nnUNet, to segment internal carotid artery (ICA) and middle cerebral artery (MCA) vascular territories directly from 2D minimal intensity projection (MinIP) cerebral DSA images acquired during endovascular thrombectomy. The model was trained on 1,224 acquisitions from 361 patients whose reference labels were autoTICI atlas segmentations manually adjusted for size, rotation, and scaling. The authors compare the model with an atlas registration baseline (autoTICI) using Dice similarity coefficient, Jaccard index, average surface distance, and Hausdorff distance, and also report a qualitative Likert-scale success-rate comparison on a held-out cohort. The paper reports significantly better metric values for the deep learning model, a higher qualitative success rate, and much lower computational time, and it concludes that direct segmentation can replace atlas registration for vascular territory visualization in DSA.

Significance. If the reported results hold, the paper would offer a practically useful and much faster alternative to atlas-based vascular territory mapping in DSA, with potential value for the autoTICI reperfusion scoring pipeline and for broader visualization during X-ray-guided procedures. The study has several strengths: patient-level stratified splits, a multicenter registry cohort, use of a strong segmentation baseline (nnUNet), public code, and an external qualitative assessment by two raters. The claimed computational speedup (seconds versus over two minutes) is clinically relevant. However, the central comparison is weakened by the shared provenance of the reference labels and the atlas baseline: the labels are manually corrected autoTICI atlas masks and the comparator is the automatic autoTICI atlas registration. This does not invalidate the practical claim that the learned model could replace atlas registration inside the autoTICI pipeline, but it limits the strength of the anatomical-accuracy claim.

major comments (4)

[Methods, Section 2; Discussion, Section 4] The reference standard labels are human-adjusted masks derived from the autoTICI atlas, and the comparator is the automatic autoTICI atlas registration. This shared provenance means the reported DSC, ASD, and success-rate advantages may partly measure the model's ability to mimic human corrections to the same atlas prior, rather than recovery of independently established vascular anatomy. The Discussion acknowledges this possibility, but the manuscript's central claim of 'accurate segmentations' of true vascular territories rests on the reference standard. Please provide a sensitivity analysis on a subset with an independent reference, for example territories delineated on CTA and co-registered to DSA, or at least re-annotate a random sample with a protocol that does not start from the atlas. Without such evidence, the conclusions should be explicitly limited to 'superior to atlas registration within the autoTICI framework'.
[Abstract; Results, Section 3.2; Table 1] Several numbers reported in the abstract and body are mutually inconsistent. The abstract states a success rate of 85% for the segmentation model, whereas the per-patient success rate reported in Section 3.2 is 80%; the abstract reports ICA ASD of 13.8 versus 47.3, while Table 1 gives medians of 14 and 47; and Table 1 reports the atlas ICA DSC as 0.82 [0.62-0.80], which is impossible because the upper IQR bound is below the median. Please correct these inconsistencies and state unambiguously which values are means, medians, per-view, or per-patient.
[Methods, Section 3.2; Abstract] The term 'external test set' in the abstract is misleading. The qualitative comparison was performed on 564 out of 660 patients from a previous study in the same MR CLEAN Registry, excluding patients who overlapped with training or test sets. This is an external cohort in the sense of not being used for training, but it is not an independent acquisition protocol or institution. Please reword to 'held-out cohort' or 'external to training' and clarify the relationship to the registry.
[Results, Section 3.2 (success rate comparison)] The qualitative Likert assessment is central to the success-rate claim, but the methods do not state whether the two raters were blinded to the method producing each segmentation, nor is inter-rater agreement (e.g., Cohen's kappa) reported. If the raters were aware of which output came from the atlas versus the deep learning model, bias in scoring is possible. Please provide blinding details and inter-rater reliability.

minor comments (5)

[Results, Section 3.2 (Model performance)] There is a typo in the text: 'significantly better DCS' should read 'DSC'.
[Methods, Section 2 (Model training)] The phrase 'non-statistical significant different performance' should be reworded, for example to 'no statistically significant difference in performance'.
[Discussion, Section 4] The sentence 'if very few or no vessels are depicted, the might be little use for specific vessel territory assessment' contains a grammatical error ('the might be') and should be revised.
[Table 2] The symbols and asterisks in Table 2 are not fully explained: only some phase-specific comparisons are marked with '***', but the text does not state what the significance markers refer to (presumably comparison with the arterial or non-contrast phase). Please clarify in the table caption or text.
[General / References] Reference 6 is listed as an arXiv preprint although the journal version of the nnU-Net paper is already cited as reference 5; please check whether both are necessary and cite the peer-reviewed version consistently.

Circularity Check

1 steps flagged · score 4.0 of 10

Reference labels are human-adjusted autoTICI atlas masks, so the model's DSC/ASD advantage over the same atlas pipeline is partly built into the label construction; human Likert scoring provides some independent support.

fitted input called prediction [Section 2 Methods, Model; Section 3.1 Data; Section 4 Discussion, Limitations]
"This model was trained using manually segmented vessel territories for the internal carotid artery (ICA) and middle cerebral artery (MCA), derived from autoTICI atlases for pre- and post-EVT DSA MinIPs[16]. The atlases were manually adjusted for size, rotation, and scaling. ... First, the manual annotation of reference standards could have introduced bias, particularly given the difficulty in visually delineating borders due to over-projection. As such, the results may not entirely reflect the true vascular territories."

The reference labels used for training and evaluation are manually adjusted autoTICI atlas masks, while the baseline comparator is the same autoTICI atlas registration pipeline without these manual adjustments. The reported DSC/ASD superiority of the segmentation model is therefore measured against a target that is constructed from the very atlas method it is compared with. The model can learn to imitate the human corrections applied to the atlas, whereas the atlas baseline receives no such correction, so the advantage partly reduces to fitting the evaluation standard rather than recovering independently established anatomy.

full rationale

The paper's central comparison is a supervised segmentation model versus the autoTICI atlas registration method. The model's training labels are 'derived from autoTICI atlases' and 'manually adjusted for size, rotation, and scaling', so the ground truth and the baseline share provenance. This makes the quantitative DSC/ASD comparison partially circular: the model is optimized to reproduce human-edited atlas masks while the atlas baseline is not, so the measured gap partly reflects imitation of manual corrections rather than independent anatomical truth. However, the qualitative success-rate comparison ('80% vs 66%, p < 0.001') is based on independent human Likert scoring of anatomical correctness against the raters' expectations, not on the atlas-derived masks, which gives the central claim some independent content. The paper also honestly acknowledges the reference-standard bias in the Discussion. There are no load-bearing self-citation chains, uniqueness theorems, or ansatz-smuggling steps; the autoTICI citations identify the baseline and label source but do not by themselves force the result. Additional numerical inconsistencies (e.g., abstract 85% vs body per-patient 80% success rate, abstract ASD 13.8 vs Table 1 ASD 14, and an impossible atlas ICA DSC median of 0.82 with IQR [0.62-0.80]) are reporting or correctness concerns, not circularity. Overall, the quantitative head-to-head comparison is partially circular, but the human-rated success outcome and the acknowledged limitation keep the paper from being fully self-referential, so a moderate score of 4 is appropriate.

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

This is an empirical machine learning comparison with no parameter-free derivation and no hidden test-set fitting. The main epistemic burden is the atlas-derived, manually adjusted reference standard, plus the domain assumptions about MinIP projections and post-reperfusion visibility. No new physical or biological entities are introduced.

assumptions (4)

domain assumption Expert manual adjustment of autoTICI atlas masks yields valid reference standard vascular territories.
Entered in Methods under Model and acknowledged as a bias risk in the Discussion; all DSC, ASD, and Likert evaluations depend on this assumption.
domain assumption Post-EVT DSA after successful reperfusion makes the occluded vascular territory visible enough to annotate accurately.
Used as a data inclusion criterion in Section 3.1 to justify annotating pre-EVT territories from post-EVT images.
domain assumption MinIP projections preserve enough spatial information to localize vascular territories from 2D DSA.
The entire method operates on 2D MinIPs, as described in the preprocessing paragraph of Methods.
domain assumption Normal atlas territory definitions, after manual scaling and rotation, approximate the vascular territories of stroke patients with proximal occlusions.
Labels are derived from autoTICI atlases with manual adjustment; the Discussion notes that true borders may not be exactly reflected.

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Cite this review

Pith. "Pith review of Direct vascular territory segmentation on cerebral digital subtraction angiography." pith.science (2026). https://pith.science/paper/QM2VT7W4

@misc{pith2026250813821,
  author       = {Pith},
  title        = {Pith review of: Direct vascular territory segmentation on cerebral digital subtraction angiography},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/QM2VT7W4}},
  note         = {Machine review of arXiv:2508.13821}
}

read the original abstract

X-ray digital subtraction angiography (DSA) is frequently used when evaluating minimally invasive medical interventions. DSA predominantly visualizes vessels, and soft tissue anatomy is less visible or invisible in DSA. Visualization of cerebral anatomy could aid physicians during treatment. This study aimed to develop and evaluate a deep learning model to predict vascular territories that are not explicitly visible in DSA imaging acquired during ischemic stroke treatment. We trained an nnUNet model with manually segmented intracranial carotid artery and middle cerebral artery vessel territories on minimal intensity projection DSA acquired during ischemic stroke treatment. We compared the model to a traditional atlas registration model using the Dice similarity coefficient (DSC) and average surface distance (ASD). Additionally, we qualitatively assessed the success rate in both models using an external test. The segmentation model was trained on 1224 acquisitions from 361 patients with ischemic stroke. The segmentation model had a significantly higher DSC (0.96 vs 0.82, p<0.001) and lower ASD compared to the atlas model (13.8 vs 47.3, p<0.001). The success rate of the segmentation model (85%) was higher compared to the atlas registration model (66%) in the external test set. A deep learning method for the segmentation of vascular territories without explicit borders on cerebral DSA demonstrated superior accuracy and quality compared to the traditional atlas-based method. This approach has the potential to be applied to other anatomical structures for enhanced visualization during X-ray guided medical procedures. The code is publicly available at https://github.com/RuishengSu/autoTICI.

Figures

Figures reproduced from arXiv: 2508.13821 by the authors.

**Figure 2.** ICA segmentations for the phase-specific MinIPs of example test set im [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗

**Figure 3.** Distributions (%) of annotated Likert scores for the segmentation model. [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗

Figures from the paper (3 more)

**Figure 4.** Figure 4: Visualization of the morphological operations — erosion, connected com [PITH_FULL_IMAGE:figures/full_fig_p012_4.png]

**Figure 5.** Figure 5: Flow chart of the included patients. Abbreviations; eTICI, expanded Trombolysis in Cerebral Infarction score [PITH_FULL_IMAGE:figures/full_fig_p012_5.png]

**Figure 6.** Figure 6: Distributions (%) of annotated Likert scores for the atlas registration [PITH_FULL_IMAGE:figures/full_fig_p013_6.png]

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Pith tools

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

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