Τίτλος:
Automatic Characterization of Plaques and Tissue in IVOCT Images Using a
Multi-step Convolutional Neural Network Framework
Γλώσσες Τεκμηρίου:
Αγγλικά
Περίληψη:
Intravascular optical coherence tomography (IVOCT) is a light-based
imaging modality of great interest because it can contribute in
diagnosing and preventing atherosclerosis due to its ability to provide
in vivo insight of coronary arteries’ morphology. The substantial number
of slices which are obtained per artery, makes it laborious for medical
experts to classify image regions of interest. We propose a framework
based on Convolutional Neural Networks (CNN) for classification of
regions of intravascular OCT images into 4 categories: fibrous tissue,
mixed plaque, lipid plaque and calcified plaque. The framework consists
of 2 main parts. In the first part, square patches (8 x 8 pixels) of OCT
images are classified as fibrous tissue or plaque using a CNN which was
designed for texture classification. In the second part, larger regions
consisting of adjacent patches which are classified as plaque in the
first part, are classified in 3 categories: lipid, calcium, mixed.
Region classification is implemented by an AlexNet version re-trained on
images artificially constructed to depict only the core of the plaque
region which is considered as its blueprint. Various simple steps like
thresholding and morphological operations are used through the
framework, mainly to exclude background from analysis and to merge
patches into regions. The first results are promising since the
classification accuracy of the two networks is high (95% and 89%
respectively).
Συγγραφείς:
Cheimariotis, G. A.
Riga, M.
Toutouzas, K.
Tousoulis, D. and
Katsaggelos, A.
Maglaveras, N.
Τίτλος συνεδρίου:
WORLD CONGRESS ON MEDICAL PHYSICS AND BIOMEDICAL ENGINEERING 2018, VOL 1
Λέξεις-κλειδιά:
Segmentation; Intravascular OCT; Convolutional neural networks; Deep
learning
DOI:
10.1007/978-981-10-9035-6_47
