Multi-View Deformable Convolution Meets Visual Mamba for Coronary Artery Segmentation
This paper tackles coronary artery segmentation from CTA images, a challenging task due to slender tubular morphology and severe class imbalance. The authors propose MDSVM-UNet, a two-stage framework that combines multidirectional snake convolution (MDSConv)—extending deformable convolution to three anatomical planes—with residual visual Mamba (RVM) for linear-complexity long-range dependency modeling. The approach aims to capture both local geometric priors of vessels and global inter-slice context while maintaining computational efficiency suitable for clinical deployment.
The paper presents a technically sound combination of recent advances (topology-aware deformable convolutions and state space models) applied to a clinically important problem. The two-stage coarse-to-fine strategy effectively addresses the tension between global context and fine detail preservation. Quantitative results on ImageCAS show meaningful improvements over the dataset baseline (5.41% Dice gain), though the lack of statistical significance testing and runtime benchmarks weakens the strength of these claims.
The multi-directional extension of snake convolution to three orthogonal anatomical planes (sagittal, coronal, axial) is well-motivated for 3D tubular structures, and the ablation study validates that MDSConv provides a 4.17% Dice improvement over the baseline. The use of RVM in the decoder for linear-complexity long-range modeling addresses a genuine limitation of transformer-based alternatives. The two-stage pipeline design—using coarse segmentation solely for block extraction guidance rather than direct incorporation into final outputs—is a principled approach that reduces false positives. The experimental comparison includes relevant baselines (DSU-Net, SwinUnet, LightM-UNet) on a large-scale public dataset.
The paper lacks critical implementation details necessary for reproduction, including the specific block extraction strategy (overlap handling, merging algorithm) and data augmentation protocols. While the authors claim 'linear computational complexity' advantages over transformers, no runtime, FLOPs, or memory benchmarks are provided to substantiate this. The two-stage pipeline effectively doubles inference time compared to single-stage methods, yet this computational overhead is not discussed as a limitation. The ablation study does not isolate whether the three-directional aspect of MDSConv is necessary versus single-direction snake convolution, nor does it explore alternative fusion strategies for the multi-view features.
The quantitative comparison against ImageCAS baseline and recent methods appears fair, with consistent evaluation metrics (DSC, HD, AHD) and dataset splits. The two-stage MDSVM-UNet achieves DSC 0.8365 versus 0.7824 for the ImageCAS baseline. However, the paper lacks statistical significance testing (p-values, confidence intervals) to validate whether these improvements are meaningful given the 250-test-sample size. The comparison with LightM-UNet (which uses similar Mamba components) shows superior performance (DSC 0.8365 vs 0.8079), suggesting the multi-directional convolution provides value beyond standard Mamba architectures.
Reproducibility is partially addressed but insufficient. While the paper specifies PyTorch 2.1.1, hardware (RTX 3090), learning rates, and optimizer, it omits batch size, data augmentation details, and—critically—the algorithm for extracting and merging 64×64×64 blocks in the two-stage pipeline. No code repository is mentioned. The architectural description of MDSConv lacks specific dimensional details (e.g., how the four feature branches are concatenated and fused). The loss function (Dice loss) is standard, but the paper does not report random seed settings or cross-validation results to assess variance.
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..
Pick a starting point or write your own. Challenges run in the background, so you can keep reading while the AI investigates.
No challenges yet. Disagree with the review? Ask the AI to revisit a specific claim.