BiM-GeoAttn-Net: Linear-Time Depth Modeling with Geometry-Aware Attention for 3D Aortic Dissection CTA Segmentation

Yuan Zhang; Lei Liu; Jialin Zhang; Ya-Nan Zhang; Ling Wang; Nan Mu

eess.IVFeb 27, 2026

BiM-GeoAttn-Net: Linear-Time Depth Modeling with Geometry-Aware Attention for 3D Aortic Dissection CTA Segmentation

Authors: Yuan Zhang, Lei Liu, Jialin Zhang, Ya-Nan Zhang, Ling Wang, Nan Mu

Organizations: College of Computer Science Sichuan Normal University Chengdu, Sichuan 610068, China · Zhejiang University & Ant Group Hangzhou, Zhejiang, China · State Key Laboratory of Public Big Data Guizhou University Guiyang, Guizhou 550025, China

Abstract

Accurate segmentation of aortic dissection (AD) lumens in CT angiography (CTA) is essential for quantitative morphological assessment and clinical decision-making. However, reliable 3D delineation remains challenging due to limited long-range context modeling, which compromises inter-slice coherence, and insufficient structural discrimination under low-contrast conditions. To address these limitations, we propose BiM-GeoAttn-Net, a lightweight framework that integrates linear-time depth-wise state-space modeling with geometry-aware vessel refinement. Our approach is featured by Bidirectional Depth Mamba (BiM) to efficiently capture cross-slice dependencies and Geometry-Aware Vessel Attention (GeoAttn) module that employs orientation-sensitive anisotropic filtering to refine tubular structures and sharpen ambiguous boundaries. Extensive experiments on a multi-source AD CTA dataset demonstrate that BiM-GeoAttn-Net achieves a Dice score of 93.35% and an HD95 of 12.36 mm, outperforming representative CNN-, Transformer-, and SSM-based baselines in overlap metrics while maintaining competitive boundary accuracy. These results suggest that coupling linear-time depth modeling with geometry-aware refinement provides an effective, computationally efficient solution for robust 3D AD segmentation.

Figures & tables

Figure 1: Challenges in AD CTA segmentation. Four consecutive axial slices exhibit strong cross-slice continuity, subtle boundary contrast, and morphological variation. Ground-truth masks are in red.

Figure 2: Overall architecture of the proposed BiM-GeoAttn-Net.

Models	Segmentation Performance					Training Cost
Models	Prec. $\uparrow$	Rec. $\uparrow$	Dice $\uparrow$	IoU $\uparrow$	HD95 (mm) $\downarrow$	Time (min/epoch) $\downarrow$	Memory (GB) $\downarrow$
Attention U-Net	89.71	90.76	90.22	82.26	10.77	3.2	8.3
nnU-Net	90.46	91.49	90.84	83.64	18.51	2.8	7.6
Swin-UNet	87.78	91.73	89.62	81.62	20.01	4.1	10.7
SegFormer3D	87.53	88.12	88.43	79.30	24.15	4.6	11.2
Mamba-UNet	88.83	91.52	89.43	83.12	12.20	3.5	8.9

Table 1: Quantitative comparison with state-of-the-art methods on the AD dataset. Best results are shown in bold. (

\uparrow

higher is better;

\downarrow

lower is better.)

Figure 3: Qualitative comparison on representative AD test cases. Yellow indicates correct overlap, blue denotes false positives, and red denotes false negatives. Compared with CNN-, Transformer-, and SSM-based baselines, BiM-GeoAttn-Net achieves sharper boundaries and improved depth-wise continuity.

Models	Dice (%) $\uparrow$	IoU (%) $\uparrow$	Recall (%) $\uparrow$	Precision (%) $\uparrow$	HD95 (mm) $\downarrow$
B	89.77	81.60	92.11	87.73	29.08
B+BiM	91.12	83.69	94.41	89.65	20.35
B+GeoAttn	90.67	83.25	93.17	88.46	25.48
B+BiM+GeoAttn	93.35	87.53	94.85	92.04	12.36

Table 2: Ablation results on the AD dataset. Best results are shown in bold. (

\uparrow

higher is better;

\downarrow

lower is better.)

Figure 4: Qualitative ablation comparison. Yellow indicates correct overlap, blue false positives, and red false negatives. The combined model yields the most consistent continuity and boundary delineation.

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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.

Abstract

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