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arXiv 2603.21829cs.CV

多视角可变形卷积与视觉Mamba相结合用于冠状动脉分割

Multi-View Deformable Convolution Meets Visual Mamba for Coronary Artery Segmentation

Sichuan Agricultural University（四川农业大学）

Xiaochan Yuan, Pai Zeng

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AI总结：

本文提出MDSVM-UNet框架，结合多方向蛇卷积与残差视觉Mamba，解决冠状动脉分割中的长距离依赖和类别不平衡问题，提升分割精度与效率。

摘要：

准确分割CTA图像中的冠状动脉对于心血管疾病的诊断和治疗计划具有关键临床意义。然而，由于血管的固有多分支和细长管状形态，加上前景血管与背景组织之间的严重类别不平衡，冠状动脉分割仍具挑战性。传统CNN方法难以捕捉空间远处血管结构的长距离依赖，而基于Vision Transformer (ViT)的方法因计算开销过大而限制了在资源受限的临床环境中的部署。受最近状态空间模型 (SSMs) 在高效建模长距离序列依赖方面的成功启发，我们提出MDSVM-UNet，一种新的两阶段冠状动脉分割框架，协同整合多方向蛇卷积 (MDSConv) 与残差视觉Mamba (RVM)。在编码阶段，我们引入MDSConv，一种可变形卷积模块，学习沿三个正交解剖平面（矢状面、冠状面和轴面）的自适应偏移，从而实现全面的多视角特征融合，忠实捕捉冠状动脉的细长和曲折几何形态。在解码阶段，我们设计基于RVM的上采样解码器块，利用选择性状态空间机制建模跨切片的长距离依赖，同时保持线性计算复杂度。此外，我们提出一种渐进的两阶段分割策略：第一阶段进行粗略全图分割以引导智能块提取，第二阶段进行细粒度块级分割以恢复血管细节并抑制假阳性。

英文摘要：

Accurate segmentation of coronary arteries from computed tomography angiography (CTA) images is of paramount clinical importance for the diagnosis and treatment planning of cardiovascular diseases. However, coronary artery segmentation remains challenging due to the inherent multi-branching and slender tubular morphology of the vasculature, compounded by severe class imbalance between foreground vessels and background tissue. Conventional convolutional neural network (CNN)-based approaches struggle to capture long-range dependencies among spatially distant vascular structures, while Vision Transformer (ViT)-based methods incur prohibitive computational overhead that hinders deployment in resource-constrained clinical settings. Motivated by the recent success of state space models (SSMs) in efficiently modeling long-range sequential dependencies with linear complexity, we propose MDSVM-UNet, a novel two-stage coronary artery segmentation framework that synergistically integrates multidirectional snake convolution (MDSConv) with residual visual Mamba (RVM). In the encoding stage, we introduce MDSConv, a deformable convolution module that learns adaptive offsets along three orthogonal anatomical planes -- sagittal, coronal, and axial -- thereby enabling comprehensive multi-view feature fusion that faithfully captures the elongated and tortuous geometry of coronary vessels. In the decoding stage, we design an RVM-based upsampling decoder block that leverages selective state space mechanisms to model inter-slice long-range dependencies while preserving linear computational complexity. Furthermore, we propose a progressive two-stage segmentation strategy: the first stage performs coarse whole-image segmentation to guide intelligent block extraction, while the second stage conducts fine-grained block-level segmentation to recover vascular details and suppress false positives..

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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