Vascular Bifurcation Detection in Cerebral CT Angiography Using Convolutional Neural Networks and Frangi Filters
Examensarbete för masterexamen
Complex adaptive systems (MPCAS), MSc
Segmentation and feature extraction are important tools for analysing and visualizing information in medical image data, particularly in vascular image data which relates to widely spread vascular diseases. Vessel segmentation is extensively featured in research, recently adapting deep learning trends in image processing. This paper aims to develop a vessel bifurcation detection method to support a seed point based segmentation approach. The suggested approach is a combination of classification, with a convolutional neural network, local vessel segmentation, with Frangi filters, and 3D morphological skeletonization. A small data set is produced for network training and evaluation. Results indicate a high classification accuracy which filters problematic samples for the Frangi filter. Thus, the combination can suggest quality branch seed points under most circumstances. Next step would be to expand the data set to enable further optimization and more rigid evaluation. In any case a combination of a high-performance classifier followed by qualitative assessment of local samples show potential.
CNN , CTA , DenseNet , Vessel Segmentation , Vessel Branch , Vascular Bifurcation Detection