An Automatic Segmentation Method of Cerebral Arterial Tree in TOF-MRA Based on DBCNet

Abstract

Arterial tree region segmentation from medical images of the brain is an early step in the diagnosis and evaluation of many cerebrovascular diseases. Most of the existing region segmentation methods rely on manual assistance. In this paper, we propose an automatic brain arterial tree partitioning method based on a dual branch connected network (DBCNet), which can partition the arterial tree in time of flight-magnetic resonance angiography (TOF-MRA) into six main regions. The branch feature decoupling module and the global and local feature fusion module based on Swin Transformer mechanism were used for DBCNet. The two-step training strategy of localization followed by segmentation was used for training. In this study, 111 cases of TOF-MRA data were used, of which 81 cases as the training set, 20 cases as the validation set, and 10 cases as the test set. The average Dice coefficient of the model on the test set was 74.72% and 95% Haus dorff distance (HD95) was 3.89 mm. Compared with other advanced segmentation networks, the network reported in this paper can segment each major region more accurately with robustness.

Keywords

• cerebral arterial tree
• time of flight-magnetic resonance angiography (tof-mra)
• deep learning
• dual branch connected network
• automatic segmentation