[论文解读] Hepatic vessel segmentation based on 3D swin-transformer with inductive biased multi-head self-attention

Mian Wu, Yinling Qian+3 more|arXiv (Cornell University)|Nov 5, 2021

Advanced Neural Network Applications参考文献 28被引用 7

一句话总结

本文提出IBIMHAV-Net，一种基于3D Swin Transformer并引入归纳偏置多头自注意力机制的网络，用于在CT容积图像中实现精确的肝血管分割。通过结合体素级嵌入、多尺度卷积以及相对位置偏置学习，该方法提升了全局上下文建模能力并保留了局部细节，最终在3DIRCADb数据集上取得了74.8%的Dice系数，优于以往的深度学习方法与图割方法。

ABSTRACT

Purpose: Segmentation of liver vessels from CT images is indispensable prior to surgical planning and aroused broad range of interests in the medical image analysis community. Due to the complex structure and low contrast background, automatic liver vessel segmentation remains particularly challenging. Most of the related researches adopt FCN, U-net, and V-net variants as a backbone. However, these methods mainly focus on capturing multi-scale local features which may produce misclassified voxels due to the convolutional operator's limited locality reception field. Methods: We propose a robust end-to-end vessel segmentation network called Inductive BIased Multi-Head Attention Vessel Net(IBIMHAV-Net) by expanding swin transformer to 3D and employing an effective combination of convolution and self-attention. In practice, we introduce the voxel-wise embedding rather than patch-wise embedding to locate precise liver vessel voxels, and adopt multi-scale convolutional operators to gain local spatial information. On the other hand, we propose the inductive biased multi-head self-attention which learns inductive biased relative positional embedding from initialized absolute position embedding. Based on this, we can gain a more reliable query and key matrix. To validate the generalization of our model, we test on samples which have different structural complexity. Results: We conducted experiments on the 3DIRCADb datasets. The average dice and sensitivity of the four tested cases were 74.8% and 77.5%, which exceed results of existing deep learning methods and improved graph cuts method. Conclusion: The proposed model IBIMHAV-Net provides an automatic, accurate 3D liver vessel segmentation with an interleaved architecture that better utilizes both global and local spatial features in CT volumes. It can be further extended for other clinical data.

研究动机与目标

• 解决由于对比度低和血管结构复杂导致的3D肝脏血管在CT图像中准确分割的挑战。
• 克服基于CNN的模型（如U-Net）依赖局部感受野、难以捕捉长距离依赖关系的局限性。
• 通过在3D体素设置下整合全局自注意力与局部卷积特征学习，提升分割精度。
• 开发一种鲁棒的端到端框架，能够泛化于不同复杂度的血管结构与病理状态。
• 实现自动、精确的分割，以支持手术规划与介入引导，无需人工干预。

提出的方法

• 将2D Swin Transformer扩展至3D，以建模体素化CT数据中的长程空间依赖关系。
• 采用体素级嵌入而非块级嵌入，以保留血管体素的精确空间定位。
• 在并行路径中引入多尺度卷积层，以捕捉细粒度的局部空间特征。
• 提出归纳偏置多头自注意力机制，从初始化的绝对位置学习相对位置嵌入，以提升查询-键矩阵的可靠性。
• 在解码器中使用3D转置卷积层进行上采样，优于三线性插值与块扩展方法。
• 采用小步长卷积层进行下采样，而非3D块合并，以更好地保留空间层次结构并提升性能。

实验结果

研究问题

• RQ1 与标准的基于CNN的模型相比，采用体素级嵌入与归纳偏置的3D Swin Transformer是否能提升肝脏血管分割的准确性？
• RQ2 在混合架构中，多尺度卷积与自注意力的融合如何影响3D CT体数据中的特征表示？
• RQ3 在低对比度、复杂血管环境下，3D Transformer-based分割网络的最优下采样与上采样策略是什么？
• RQ4 与标准的绝对位置编码或相对位置编码相比，所提出的归纳偏置位置嵌入在多大程度上增强了注意力学习？
• RQ5 该模型在肝脏CT扫描中面对不同结构复杂度与病理状态时，其泛化能力如何？

主要发现

• IBIMHAV-Net在3DIRCADb数据集上取得了74.8%的平均Dice系数，优于现有深度学习方法与图割方法。
• 模型表现出77.5%的敏感度，表明在检测小血管与弯曲血管方面表现优异。
• 采用3D转置卷积进行上采样时Dice得分最高（74.83%），优于三线性插值（72.21%）与块扩展（67.11%）。
• 使用小步长卷积层进行下采样优于3D块合并，血管Dice达74.83%，背景Dice达96.92%。
• 移除局部特征提取模块后，Dice系数下降7.5%，准确率下降12%，证实其在保留细节方面起着关键作用。
• 输入分辨率为128×128×96时，在性能与计算成本之间实现了良好平衡，更大输入仅带来±0.3%的Dice提升，但计算成本显著增加。