Scaling up fine-grained intracranial vessel annotations in computed tomography angiography
This dataset addresses the lack of fine-grained intracranial vessel annotations for CT angiography, but the contribution is incremental as it extends existing segmentation approaches with a new dataset.
The authors introduce SemanticVessel, a dataset for fine-grained brain vessel segmentation in CTA scans, using dynamic 4D-CTA to generate artery/vein traces and intensity-guided region growing for vascular territory segmentation. They show that including a generic artery class improves fine-grained segmentation across all classes.
In this work, we present SemanticVessel, a dataset for fine-grained brain vessel segmentation in computed tomography angiography scans. Based on the detailed contrast provided by dynamic 4D-CTA scans, we generate segmentation traces for arteries and veins. We then use intensity-guided region growing to obtain segmentations of the majority of vascular territories in the human brain, which are refined and annotated with 20 unique arterial classes by an expert radiologist. Unlike existing datasets, where minor arteries are discarded as background content, we merge these minor arteries into a generic arterial class. Due to the multiple-phase acquisition of dynamic 4D-CTA, labels for a single phase can be re-used for other phases in the same series, greatly increasing the size of our dataset with no additional annotation cost. The results show that models trained with the additional generic artery class produce better fine-grained segmentations across the board. We will make our code, annotation GUI, and model weights available to the scientific community. Code, weights, and data will be made available on https://github.com/alceballosa/robust-vessel-segmentation