  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.
