  Liver cancer is one of the most common malignant diseases in the world.
Segmentation and labeling of liver tumors and blood vessels in CT images can
provide convenience for doctors in liver tumor diagnosis and surgical
intervention. In the past decades, many state-of-the-art medical image
segmentation algorithms appeared during this period. With the development of
embedded devices, embedded deployment for medical segmentation and automatic
reconstruction brings prospects for future automated surgical tasks. Yet, most
of the existing segmentation methods mostly care about the spatial feature
context and have a perception defect in the semantic relevance of medical
images, which significantly affects the segmentation accuracy of liver tumors
and blood vessels. Deploying large and complex models into embedded devices
requires a reasonable trade-off between model accuracy, reasoning speed and
model capacity. Given these problems, we introduce a multi-scale feature fusion
network called TransFusionNet based on Transformer. This network achieved very
competitive performance for liver vessel and liver tumor segmentation tasks,
meanwhile it can improve the recognition of morphologic margins of liver tumors
by exploiting the global information of CT images. Experiments show that in
vessel segmentation task TransFusionNet achieved mean Dice coefficients of
0.899 and in liver tumor segmentation task TransFusionNet achieved mean Dice
coefficients of 0.961. Compared with the state-of-the-art framework, our model
achieves the best segmentation result. In addition, we deployed the model into
an embedded micro-structure and constructed an integrated model for liver tumor
vascular segmentation and reconstruction. This proprietary structure will be
the exclusive component of the future medical field.
