Novel dataset and evaluation of state-of-the-art vessel segmentation methods

Introduction: Vascular diseases, such as intracranial aneurysms, are one of the top causes of death in the world. Due to the constantly increasing number of angiographic imaging examinations and their use in population screening there is a need for accurate and robust methods for vessel segmentation. Methods & Materials: We used a publicly available dataset of 570 cerebral TOF-MRA angiograms (IXI dataset) and manually created reference segmentations using interactive thresholding of the raw and vesselness filter enhanced angiograms. The obtained segmentations were visually verified by a skilled radiologist and then used to objectively and comparatively evaluate six approaches based on recent convolutional neural network (CNN) segmentation models. Results: Model training on raw images (without preprocessing) resulted in Dice similarity coefficient (DSC) value of 0.91, while preprocessing with specialized filters produced inferior DSC values. Spatially affixed model training on the Circle of Willis (CoW) region yielded a significantly better result (DSC=0.95; p-value < 0.001) as compared to the training on whole images (DSC=0.91). Conclusion: On the MRA scans of IXI dataset we created reference vessel segmentations to serve as a new benchmark for vessel segmentation studies. The reference segmentations are publicly available**. Among six state-of-the-art approaches evaluated on this dataset, we found that raw input images with spatially affixed CNN model training with respect to CoW achieved the best vessel segmentation.