Benefit from public unlabeled data: A Frangi filtering-based pretraining
  network for 3D cerebrovascular segmentation
Gen Shi Hao Lu Hui Hui Jie Tian
The precise cerebrovascular segmentation in time-of-flight magnetic resonance
angiography (TOF-MRA) data is crucial for clinically computer-aided diagnosis.
However, the sparse distribution of cerebrovascular structures in TOF-MRA
results in an exceedingly high cost for manual data labeling. The use of
unlabeled TOF-MRA data holds the potential to enhance model performance
significantly. In this study, we construct the largest preprocessed unlabeled
TOF-MRA datasets (1510 subjects) to date. We also provide three additional
labeled datasets totaling 113 subjects. Furthermore, we propose a simple yet
effective pertraining strategy based on Frangi filtering, known for enhancing
vessel-like structures, to fully leverage the unlabeled data for 3D
cerebrovascular segmentation. Specifically, we develop a Frangi filtering-based
preprocessing workflow to handle the large-scale unlabeled dataset, and a
multi-task pretraining strategy is proposed to effectively utilize the
preprocessed data. By employing this approach, we maximize the knowledge gained
from the unlabeled data. The pretrained model is evaluated on four
cerebrovascular segmentation datasets. The results have demonstrated the
superior performance of our model, with an improvement of approximately 3\%
compared to state-of-the-art semi- and self-supervised methods. Furthermore,
the ablation studies also demonstrate the generalizability and effectiveness of
the pretraining method regarding the backbone structures. The code and data
have been open source at: \url{https://github.com/shigen-StoneRoot/FFPN}.