A Neighborhood Attention Transformer Network for Enhanced 3D Segmentation of the Left Anterior Descending Artery

Ahmed I. Ghanem; Chengyin Li; Dongxiao Zhu; Hassan Bagher-Ebadian; Joshua P. Kim; Justine Cunningham; Kundan S. Thind; Rafi Ibn Sultan; Yiannos Demetriou

REVIEW 4 major objections 5 minor 50 references

A Neighborhood Attention Transformer Network for Enhanced 3D Segmentation of the Left Anterior Descending Artery

T0 review · 4 major / 5 minor · reviewed 2026-08-16 · deepseek-v4-flash

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Pith's one-line read NA-UNETR, a 3D transformer built on Neighborhood Attention, is claimed to segment the left anterior descending artery from non-contrast CT more accurately than the compared CNN and transformer baselines.

desk verdict A competent transformer segmentation study whose ImageCAS gains hold up, but the LAD-SEG claim is within label noise and not statistically significant. read the letter →

arxiv 2608.12274 v1 pith:FKSQTWBS submitted 2026-08-12 cs.CV cs.AI

Rafi Ibn Sultan , Chengyin Li , Yiannos Demetriou , Ahmed I. Ghanem , Joshua P. Kim , Justine Cunningham , Hassan Bagher-Ebadian , Dongxiao Zhu

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Kundan S. Thind

This is my paper · ORCID

classification cs.CVcs.AI

keywords leftanteriordescendingarteryneighborhoodattentioncoronarysegmentationnon-contrastCTradiotherapycardiacsparingtransformeruncertainty-weightedlossLoRAfine-tuning

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

This paper tries to establish that a 3D transformer segmentation model built on Neighborhood Attention can outline the left anterior descending artery (LAD) on non-contrast, free-breathing CT better than established CNN and transformer baselines. The proposed NA-UNETR is pretrained on 1,000 contrast-enhanced coronary CT angiography volumes and then fine-tuned on only 20 planning CT scans using low-rank adapters, with a Dice-Focal plus Hausdorff loss balanced by learned uncertainty. On the institutional LAD-SEG set the paper reports 45.64% Dice, 38.16 mm HD95, and 10.01 mm ASD, the best among the compared models, with gains of 3.10 Dice points over nnU-Net and a 2.96 mm HD95 reduction over Swin UNETR. If the claim holds, it offers a computationally modest path toward cardiac substructure contours for thoracic radiotherapy planning, where the LAD is rarely segmented because manual delineation is so uncertain. The paper itself notes that expert manual contours overlap by only 10 to 53 percent on this task, so the reported margin sits within label variability.

What carries the argument

The load-bearing mechanism is the Neighborhood Attention (NA) block: attention in which each query attends only to keys and values in a local $k \times k \times k$ window, plus Dilated NA (DiNA), which samples that window at increasing dilation. Alternating NA and DiNA in NAT blocks inside a UNETR-style encoder gives a local spatial inductive bias and an expanding receptive field without the cost of global attention, and the paper's ablations tie the best result to this pairing together with residual convolutions, variable kernel sizes, and an uncertainty-weighted Dice-Focal plus Hausdorff loss that turns overlap and boundary objectives into one adaptive training signal.

What would settle it

Ask two additional expert readers to re-contour the same 20 LAD-SEG scans, form a consensus mask, and score NA-UNETR versus nnU-Net against the second reader and against the consensus; the claim of superiority is falsified if the margin disappears or flips sign on either the alternative observer or the consensus mask.

Watch

Extended reading notes

Core claim

On its own terms, the discovery is that alternating Neighborhood Attention (NA) with Dilated Neighborhood Attention (DiNA) inside a UNETR-style transformer lets a segmentation model track a vessel that occupies a tiny fraction of the volume: local windows preserve the tubular contour, dilation expands the receptive field to follow the vessel course, and the combination with an uncertainty-weighted Dice-Focal plus Hausdorff loss produces the best overlap, centerline, and boundary scores among all compared models. The paper states this as an architecture-and-training result, not a claim that LAD segmentation is solved: it reports 45.64% Dice on non-contrast CT, notes that manual contours themselves range from 0.10 to 0.53 Dice, and explicitly says the approach is not yet suitable for clinical deployment.

Load-bearing premise

The whole comparison rests on treating one physician's LAD contour as ground truth, yet the paper cites inter-observer Dice of 0.10 to 0.53 on this imaging type, so the model's 3.10-point improvement over the runner-up is smaller than the disagreement between experts.

Editorial extensions

If this is right

A radiotherapy planning workflow could obtain LAD contours from non-contrast CT in a single forward pass, since NA-UNETR runs at 1.33 seconds per volume with 4.17 GB peak GPU memory.
Pretraining on contrast CTA followed by LoRA fine-tuning becomes a transfer recipe for small-data cardiac substructure tasks: training on the 20 LAD scans alone drops Dice from 45.64% to 36.39%.
The dilated variant's higher centerline Dice (44.39% versus 43.45%) suggests the vessel trajectory is preserved better, which matters more than volumetric overlap alone for estimating dose to the artery.
The same architecture reaches 79.49% Dice on a high-contrast 1,000-case coronary benchmark with statistically significant gains, indicating that the local-global attention benefit is not limited to low-contrast CT.
The reported compute stays near Swin UNETR in parameters and FLOPs and below UNETR, so the accuracy gain is not bought with a substantially larger model.

Reading between the lines

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

Because manual LAD contours on non-contrast CT overlap by only 10 to 53 percent, the reported 3.10-point Dice advantage over nnU-Net could measure which observer's contour style the model learned rather than true anatomical accuracy; a multi-observer consensus test is the direct way to separate style matching from accuracy.
The same NA/DiNA alternating pattern should transfer to other thin, low-contrast tubular targets such as the esophagus, coronary veins, or small airways, and a controlled test across several such structures would show whether the mechanism or the preprocessing pipeline carries the gain.
Boundary errors on LAD-SEG remain large even for the best model, with HD95 above 38 mm, so a centerline-aware loss or topology-preserving postprocessing may be a higher-leverage next step than further architectural changes.
Because the model is pretrained on CTA and fine-tuned on non-contrast CT, explicit modality alignment such as intensity normalization or domain adaptation might close the remaining boundary gap without requiring more annotated LAD scans.

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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

First thing you should know: this is a competent engineering study, not a breakthrough. The genuinely new bit is the evaluation of a transformer-based LAD segmentation pipeline on non-contrast CT, which nobody has done before. The architecture is a 3D adaptation of Neighborhood Attention Transformer with a UNETR-style decoder, plus LoRA fine-tuning and an uncertainty-weighted Dice-Focal-Hausdorff loss. Each piece exists; the combination is new, and the paper gives it a fair test against seven baselines.

The ImageCAS results are solid. NA-UNETR gets 79.49% Dice, beating the next best by about 1.2 points, and the Mann-Whitney U test says the differences are significant. The ablations are careful and mostly confirm the design choices. Computational cost is reasonable. The paper is honestly written in the body, at least. The Discussion explicitly says the achieved Dice on LAD-SEG lies within expert variability.

Now the soft spots. The central clinical claim, that NA-UNETR improves LAD delineation on non-contrast CT, is statistically weak. It rests on 20 scans with a single annotator. The 3.10 point Dice gain over nnU-Net (45.64 vs 42.54) is within the 0.10–0.53 inter-observer Dice range that the paper itself cites. Section IV.B reports p>0.05 for all model differences on LAD-SEG. So the ranking may just reflect which model happens to match the annotator's contouring style. The abstract does not mention this, which is a problem: it states the improvements without the caveat.

Second, the 5-fold validation was used both for hyperparameter selection (LoRA rank, NAT depth, kernel sizes) and for final performance reporting. That is optimistic bias. There is no independent test set.

Third, the Gaussian noise term added to the Hausdorff loss is ad hoc. It is not ablated separately, just lumped into the boundary loss. Either justify it or drop it.

My overall take: this is a useful contribution to the medical image segmentation literature, especially the ImageCAS result and the careful comparisons. But the LAD-SEG claim needs to be reframed as provisional, with confidence intervals and a clear statement that the differences are not statistically significant. If the authors can get multi-observer labels or at least a separate validation set, that would help. As it stands, I would send it to peer review with major revisions requested. The paper is coherent, the experiments are described in enough detail to reproduce, and the authors are not overselling in the body. The abstract needs work.

Recommendation: engage with it seriously, but do not let the LAD-SEG claim stand as-is.

Referee Report

4 major / 5 minor

Summary. The manuscript proposes NA-UNETR, a 3D encoder-decoder transformer that replaces global attention with Neighborhood Attention and Dilated Neighborhood Attention, and combines Dice-Focal with a noise-perturbed Hausdorff loss weighted by learnable homoscedastic uncertainties. The model is pretrained on 1,000 ImageCAS CTA volumes and fine-tuned with LoRA on 20 non-contrast LAD-SEG scans. On LAD-SEG it reports the highest Dice (45.64%), clDice (44.39%), and lowest HD95 (38.16 mm) among eight baselines, with ASD 10.01 mm; on ImageCAS it reports 79.49% Dice and 1.02 mm ASD. Ablations address architecture depth, kernel sizes, residual blocks, LoRA rank, loss components, preprocessing, and postprocessing. The LAD-SEG improvements are reported as not statistically significant (p>0.05).

Significance. The clinical problem is well motivated and the proposed system is computationally efficient, with 19.6M trainable parameters, 314.1B FLOPs, and a public code release. The baseline comparison is comprehensive, the ImageCAS gains are reported as statistically significant, and the paper is appropriately cautious about clinical deployment. If the LAD-SEG result held under independent validation, the work would be a useful contribution to cardiac substructure segmentation in radiotherapy planning. However, the primary evidence on the in-house dataset is weak: n=20, p>0.05 for all model differences, and the reported gains fall within the inter-observer variability range cited by the authors. The contribution therefore currently rests mainly on the ImageCAS results and the architectural/ablation analysis rather than on a demonstrated LAD-SEG superiority.

major comments (4)

[Section IV.B, Table 1; Section V] The central claim that NA-UNETR improves LAD delineation on LAD-SEG is not supported by the reported statistics. The 3.10 percentage point Dice advantage over nnU-Net (45.64 vs 42.54) and the 2.96 mm HD95 advantage over Swin UNETR are accompanied by Mann-Whitney p>0.05 for the differences, and the sample is only n=20 with per-case standard deviations of 4-8% Dice. The paper itself cites inter-observer Dice of 0.10-0.53 on non-contrast CT, so the observed differences lie entirely within the label-variability floor. The authors should report effect sizes and confidence intervals, pre-specify the primary comparison, and ideally evaluate on an independent test set or with multi-observer labels; otherwise the LAD-SEG superiority claim should be reframed as a preliminary observation.
[Section IV.C, Tables 3-5; Section III.B.1] The same 5-fold split is used both to select hyperparameters (NAT depth, kernel sizes, LoRA rank, and loss variants in Tables 3-5) and to report the final LAD-SEG performance in Table 1. No separate held-out test set or nested cross-validation is described, so the reported numbers are optimistically biased by model selection on the validation folds. The authors should fix all hyperparameters before evaluating on an untouched test split, or use nested cross-validation, or explicitly present the reported LAD-SEG numbers as internal-validation results rather than as final test performance.
[Section II.C.1; Section V] The evaluation uses a single physician-delineated contour set per scan, despite the paper's own citation of inter-observer Dice ranging from 0.10 to 0.53 on non-contrast CT. Under such label variability, a model's ranking may reflect which annotator's contouring style it happens to approximate rather than anatomical accuracy. The comparison is internally consistent, but it cannot establish that NA-UNETR is anatomically superior to the baselines. The authors should provide multi-observer labels or a consensus ground truth for at least a subset of scans, or explicitly restrict all LAD-SEG superiority claims to 'matches the reference contours in this dataset'.
[Section III.D, Eq. (11)] Equation (11) does not define the standard HD95 metric. The formula HD95 = quantile95%(max_{x in boundary of prediction} min_{y in boundary of ground truth} ||x-y||) takes a 95th percentile of a single maximum, which is not meaningful, and it is one-sided rather than symmetric. Standard HD95 is the 95th percentile of the set of all directed nearest-neighbor distances from both boundaries to the other. The authors should correct the definition and confirm that the reported HD95 values were computed with the standard symmetric percentile distance; if the implementation matches the printed formula, the HD95 results should be recomputed.

minor comments (5)

[Table 5] Table 5 contains two rows with very similar labels for standard preprocessing, with values 43.12 and 39.98 for DSC; the text states that standard preprocessing reduces NA-UNETR from 45.64 to 43.12 and nnU-Net from 42.54 to 39.98, but the table does not clearly identify which model the second 'Standard Preprocessing Only' row refers to. The row labels should be corrected.
[Section II.E.3, Eq. (8)] The Gaussian noise term epsilon with variance sigma_n^2 added to the Hausdorff loss is never specified; the paper should report the exact variance used and ideally ablate this noise term separately from the homoscedastic weighting, since the loss design is presented as one of the contributions.
[Section II.A, Eqs. (1)-(2)] Equation (2) introduces a learnable relative positional bias b(i,j) that is absent from Equation (1); the authors should state whether standard NA also uses this bias or whether its introduction in DiNA is a deliberate difference.
[Section II.C.1] The sentence reporting Levene's test says a single statistic of 0.1619 with p=0.8511 was computed 'across three key attributes', but it is unclear how one test combines voxel intensity, artery size, and boundary complexity. This should be clarified or the sentence removed.
[Section IV.B] The Mann-Whitney results on LAD-SEG are reported only as p>0.05; actual p-values and a statement about multiple comparisons would allow readers to assess the evidence, and if no correction is used this should be acknowledged.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the reported Dice and HD95 figures are measured benchmark outcomes, not quantities derived from the model's own assumptions.

full rationale

The paper's central claim is an empirical performance comparison on LAD-SEG and ImageCAS, with metrics computed on held-out folds and reported as measured values (Section IV.B, Tables 1 and 2). No equation in the paper defines the reported Dice, HD95, or ASD in terms of the model's fitted parameters or loss weights, so there is no derivation that reduces to its own inputs. Architectural hyperparameters such as NAT block depths and kernel sizes are explicitly attributed to the external Neighborhood Attention Transformer work (Ref. 38) and then tested in ablations; the LoRA rank and loss-balancing variances are trained or selected on data, but they are not relabeled as predictions. The paper's self-citations (Refs. 27, 35, 36, 37) appear only as background motivation for transformer-based modeling and are not load-bearing for the reported results. The Discussion itself notes that the achieved LAD-SEG DSC lies within the reported inter-observer variability range, which is a limitation of the benchmark and of clinical interpretability, not a circularity. The use of the same 5-fold validation for hyperparameter selection and final reporting is a potential optimism-bias concern, but it does not make the measured outcome equivalent to the input by construction. The derivation chain is therefore self-contained with respect to circularity, and the appropriate finding is no significant circularity.

Assumptions & free parameters 6 free parameters · 4 assumptions · 0 invented entities

The central claim rests on a modest number of hand-tuned hyperparameters (focal parameters, loss weights, LoRA rank, postprocessing thresholds) and on domain assumptions about label reliability and CTA-to-non-contrast transfer. No new physical entities are introduced. The main structural risk is that the ground truth itself is ambiguous, and most tuning is done on the same 20-case dataset used for evaluation.

free parameters (6)

Uncertainty log-variances (log sigma1, log sigma2) = learned during training
Used in Eq. 8 to balance Dice-Focal and Hausdorff losses; fit on the training folds, affecting the segmentation output.
Loss weights lambda1, lambda2 = 1, 1
Chosen empirically in Section III.B; alternative values tested (0.5/1, 1/0.5, 2/1) did not improve DSC or HD95.
Focal loss alpha, gamma, class weights = alpha=0.8, gamma=2, class weights 0.1/0.9
Hand-set in Section III.B to counter class imbalance; no sensitivity analysis beyond the stated values.
LoRA rank r = 8
Selected via ablation on the 20-case LAD-SEG set (Table 4); ranks 2, 4, 8, 16 tested.
Postprocessing thresholds = largest component, remove <64 voxels, fill holes
Hand-set in Section II.F; no ablation for the 64-voxel threshold specifically.
Gaussian noise variance on Hausdorff loss = small, unspecified
Added in Section II.E.3 to slow convergence; the variance is not reported and no ablation isolates its effect.

assumptions (4)

domain assumption Manual LAD contours on non-contrast CT are reliable enough to serve as ground truth for training and evaluation.
The paper cites inter-observer Dice of 0.10 to 0.53 (Section I, ref 18); if labels are highly variable, the measured model differences may reflect label noise rather than anatomical accuracy.
domain assumption Pretraining on CTA coronary artery masks transfers to non-contrast CT LAD segmentation.
The method relies on ImageCAS pretraining followed by LoRA fine-tuning on 20 non-contrast scans; Table 5 shows a 9.25 Dice gain from pretraining, but the domain gap remains large.
domain assumption The largest-connected-component postprocessing assumption preserves true LAD anatomy.
Section II.F keeps only the largest foreground component; fragmented or discontinuous LAD ground truth would be penalized, and this rule is applied uniformly to all models.
ad hoc to paper Adding low-variance Gaussian noise to the Hausdorff loss improves generalization.
Section II.E.3 introduces the noise term to slow convergence and encourage exploration, but no reference, ablation, or variance value supports this component.

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Cite this review

Pith. "Pith review of A Neighborhood Attention Transformer Network for Enhanced 3D Segmentation of the Left Anterior Descending Artery." pith.science (2026). https://pith.science/paper/FKSQTWBS

@misc{pith2026260812274,
  author       = {Pith},
  title        = {Pith review of: A Neighborhood Attention Transformer Network for Enhanced 3D Segmentation of the Left Anterior Descending Artery},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/FKSQTWBS}},
  note         = {Machine review of arXiv:2608.12274}
}

read the original abstract

Background: Accurate segmentation of the Left Anterior Descending (LAD) artery in 3D free-breathing, non-contrast CT is critical for cardiac dose sparing in thoracic radiotherapy. The LAD is extremely small, has poor soft-tissue contrast, and varies substantially across patients; even manual contours show limited inter-observer agreement, underscoring the ambiguity of the vessel boundaries. Purpose: To develop a transformer-based framework that improves LAD delineation in low-contrast, imbalanced CT through local-global context modeling and uncertainty-guided optimization. Methods: We propose NA-UNETR, a 3D transformer-based segmentation model whose Neighborhood Attention (NA) and Dilated NA (DiNA) blocks jointly capture fine structural detail and long-range context. Given the scarcity of annotated LAD data, the model is pretrained on 1,000 CTA volumes of general coronary anatomy and fine-tuned with LoRA-based parameter-efficient adaptation on 20 free-breathing institutional CT scans. A composite Dice-Focal and Hausdorff loss, dynamically balanced via homoscedastic uncertainty, improves overlap and boundary accuracy. Results: NA-UNETR reached 45.64% Dice, 38.16 mm HD95, and 10.01 mm ASD, improving Dice by 3.10 percentage points over nnU-Net and reducing HD95 by 2.96 mm relative to Swin UNETR, with the strongest boundary accuracy among all models and improved centerline stability. On ImageCAS it achieved 79.49% Dice, 8.89 mm HD95, and 1.02 mm ASD. Ablations confirmed that residual blocks, variable kernels, and uncertainty-weighted loss each contributed. Conclusions: NA-UNETR balances local precision and global context for thin, low-contrast LAD structures, offering a computationally efficient framework for substructure-level cardiac segmentation in radiotherapy planning.

Figures

Figures reproduced from arXiv: 2608.12274 by the authors.

**Figure 1.** Overall architecture of the proposed NA-UNETR model. The encoder side is divided into four encoder stages, each containing multiple NAT blocks preceded by a residual convolution layer. Skip connections link encoder stages to the corresponding decoder stages, which use residual blocks and upsampling to produce the final segmentation output. Each NAT block can be configured to include DiNA in addition to standard NA, … view at source ↗

**Figure 2.** Overview of preprocessing, postprocessing, and augmentation used to address severe class imbalance and enhance vessel-tobackground contrast in non-contrast CT scans. specifically designed for coronary artery segmentation from computed tomography angiography (CTA) images. ImageCAS consists of 1,000 highˆa€‘resolution 3D CTA volumes acquired using Siemens 128ˆa€‘slice dualˆa€‘source scanners at Guangdong Provinc… view at source ↗

**Figure 3.** LAD artery segmentation without preprocessing (left) and with preprocessing (right). Each shows a low-contrast CT patch and its predicted mask overlaid on the ground truth (yellow). Red boxes highlight regions where preprocessing improves vessel edge clarity and continuity. LAD intensity statistics in Section II.C.1. show that vessel voxels lie primarily within a narrow soft-tissue range, with most values between… view at source ↗

Figures from the paper (3 more)

**Figure 4.** Figure 4: Overview of the two experimental approaches: the top section shows pretraining on ImageCAS followed by fine-tuning on LADSEG, while the bottom section shows direct training on ImageCAS. The common setup for both is as follows: All models in all experimental settings w…

**Figure 5.** Figure 5: Representative qualitative results on randomly selected slices from the LAD-SEG and ImageCAS validation datasets. Each row corresponds to a randomly chosen slice from a randomly selected case. The first column shows the CT image, followed by the ground truth annotation…

**Figure 6.** Figure 6: Representative axial, coronal, and sagittal views with zoomed-in ROI insets (yellow boxes) highlighting the segmented LAD region across Ground Truth, NA-UNETR, and nnU-Net predictions. On LAD-SEG, CNN-based models achieved limited accuracy, with Dice scores between 35…

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Pith tools

Reviewed August 16, 2026 · model on record in the stance chip above.

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