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

• URL: http://arxiv.org/abs/2608.12274v1
• Date: Wed, 12 Aug 2026 17:13:59 GMT
• Title: A Neighborhood Attention Transformer Network for Enhanced 3D Segmentation of the Left Anterior Descending Artery
• Abstract summary: 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.<n>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.<n>A composite Dice-Focal and Hausdorff loss, dynamically balanced via homoscedastic uncertainty, improves overlap and boundary accuracy.
• Score: 5.486895610501164
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Anatomy-Change-Aware Bidirectional Selective State-Space Memory for Clinically Deployed Thoracic Radiotherapy Auto-Contouring [0.6439594960617159]
  DAMM-Net++ is a 2.5D architecture for thoracic radiotherapy auto-contouring.<n>It addresses inter-slice surface incoherence, systematic failure on small low-contrast targets, and the absence of per-case reliability signals.<n>It achieves a mean Dice of 0.955 and HD95 of 3.78 mm, with the largest gains on low-contrast organs-at-risk.
  arXiv  Detail & Related papers  (2026-09-11T11:13:06Z)
• Test-time Adaptation of Pelvic Bone Segmentation Models via Dynamic Reliability-Guided [62.10276009853225]
  Reliable pelvic bone segmentation from CT is essential for robot-assisted pelvic trauma surgery.<n>Existing methods show limited effectiveness for PBS.<n>We propose a novel closed-loop dynamic Reliability-Guided framework (ReGA) for PBS.
  arXiv  Detail & Related papers  (2026-08-01T08:13:20Z)
• Post-Operative Glioma Segmentation via Loss Stabilization, Normalization and Subspace Attention [46.03321798937855]
  We show that the standard Generalized Dice Loss (GDL) is unstable under domain shift.<n>We propose a Subspace-Aware Class Attention (SACA) module that re-calibrates the bottleneck features and raises Enhancing Tumor (ET) sensitivity by 8%.
  arXiv  Detail & Related papers  (2026-07-23T09:59:30Z)
• Parameter-Efficient Adaptation of SAM 3 for Automated ITV Generation from 4DCT Images [0.0]
  Four-dimensional computed tomography (4DCT) captures the full respiratory cycle of thoracic anatomy.<n>Current Internal Target Volume contouring process each phase in isolation, discarding temporal coherence and leaving vulnerable to phase-specific artifacts.<n>We present a framework that applies parameter-efficient fine-tuning to the Segment Anything Model 3 (SAM 3) via low-rank adaptation (LoRA)<n>With only seven annotated volumes, the framework retains over 95% of full-data accuracy, and the entire pipeline is trainable on a single consumer-grade GPU.
  arXiv  Detail & Related papers  (2026-06-14T05:12:57Z)
• Efficient Transformer-Based Localized Patch Sampling for Choroid Plexus Segmentation in Multiple Sclerosis [26.831715552066694]
  The lateral ventricle choroid plexus (LVCP) is gaining recognition as a key imaging biomarker for multiple sclerosis (MS) related to physical disability and neuroinflammation.<n>This research aims to develop a SwinUNETR-driven pipeline that automatically segment the LVCP in MS from both standalone and multi-modal MRI inputs.
  arXiv  Detail & Related papers  (2026-06-02T12:32:43Z)
• TriALS: Triphasic-Aided Liver Lesion Segmentation Benchmark in Non-Contrast CT [49.93685997940356]
  We describe the TriALS challenge for automated liver lesion segmentation under contrast-limited conditions.<n>The top-performing method achieved a mean venous-phase Dice of 0.754, consistent with human-level performance, yet dropped to 0.57 on NCCT.<n>Algorithm performance was most strongly predicted by training data scale and pre-training strategy.
  arXiv  Detail & Related papers  (2026-05-15T19:23:35Z)
• Overcoming data scarcity through multi-center federated learning for organs-at-risk segmentation in pediatric upper abdominal radiotherapy [0.0]
  Developing robust pediatric-specific models is hindered by data scarcity and fragmentation across centers.<n> Federated learning (FL) enables privacy-preserving collaborative training without the need for data sharing.<n>We evaluated the feasibility and performance of FL for developing pediatric-specific OAR segmentation models across two European medical centers.
  arXiv  Detail & Related papers  (2026-05-07T18:21:43Z)
• DMD-augmented Unpaired Neural Schrödinger Bridge for Ultra-Low Field MRI Enhancement [45.02203416553436]
  As paired 64 mT - 3 T scans are scarce, we propose an unpaired 64 mT $rightarrow$ 3 T translation framework.<n>Our method builds upon the Unpaired Neural Schrdinge Bridge (UNSB) with multi-step refinement.
  arXiv  Detail & Related papers  (2026-03-04T06:26:52Z)
• TempDiffReg: Temporal Diffusion Model for Non-Rigid 2D-3D Vascular Registration [14.97788368971466]
  The proposed method consistently outperforms state-of-the-art (SOTA) methods in both accuracy and anatomical plausibility.<n>It has the potential to assist less-experienced clinicians in safely performing complex TACE procedures.
  arXiv  Detail & Related papers  (2026-01-26T05:40:45Z)
• Anatomy Aware Cascade Network: Bridging Epistemic Uncertainty and Geometric Manifold for 3D Tooth Segmentation [13.650721192161079]
  We propose the Anatomy Aware Cascade Network (AACNet) to resolve boundary ambiguity and maintain global structural consistency.<n>We show that AACNet achieves a Dice Similarity Coefficient of 90.17 % and a 95% Hausdorff Distance of 3.63 mm, significantly outperforming state-of-the-art methods.
  arXiv  Detail & Related papers  (2026-01-12T12:53:27Z)
• EqDiff-CT: Equivariant Conditional Diffusion model for CT Image Synthesis from CBCT [43.92108185590778]
  Cone-beam computed tomography (CBCT) is widely used for imageguided radiotherapy (IGRT)<n>We propose a novel diffusion-based conditional generative model, coined EqDiff-CT, to synthesize high-quality CT images from CBCT.
  arXiv  Detail & Related papers  (2025-09-26T05:51:59Z)
• A Novel Attention-Augmented Wavelet YOLO System for Real-time Brain Vessel Segmentation on Transcranial Color-coded Doppler [49.03919553747297]
  We propose an AI-powered, real-time CoW auto-segmentation system capable of efficiently capturing cerebral arteries.<n>No prior studies have explored AI-driven cerebrovascular segmentation using Transcranial Color-coded Doppler (TCCD)<n>The proposed AAW-YOLO demonstrated strong performance in segmenting both ipsilateral and contralateral CoW vessels.
  arXiv  Detail & Related papers  (2025-08-19T14:41:22Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.