Vascular Bifurcation Detection in Cerebral CT Angiography Using Convolutional Neural Networks and Frangi Filters
Typ
Examensarbete för masterexamen
Program
Complex adaptive systems (MPCAS), MSc
Publicerad
2021
Författare
Jacobson, Nils
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
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.
Beskrivning
Ämne/nyckelord
CNN , CTA , DenseNet , Vessel Segmentation , Vessel Branch , Vascular Bifurcation Detection