Evaluating classifiers for atherosclerotic plaque component segmentation in MRI

Arna van Engelen*, Marleen De Bruijne, Torben Schneider, Anouk C. van Dijk, M. Eline Kooi, Jeroen Hendrikse, Aart Nederveen, Wiro J. Niessen, Rene Botnar

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingOther chapter contributionpeer-review


Segmentation of tissue components of atherosclerotic plaques in MRI is promising for improving future treatment strategies of cardiovascular diseases. Several methods have been proposed before with varying results. This study aimed to perform a structured comparison of various classifiers, training set sizes, and MR image sequences to determine the most promising strategy for methodology development. Five different classifiers (linear discriminant classifier (LDC), quadratic discriminant classifier (QDC), random forest (RF), and support vector classifiers with both a linear (SVMlin) and radial basis function kernel (SVMrbf)) were evaluated. We used carotid MRI data from 124 symptomatic patients, scanned in 4 centres with 2 different MRI protocols (45 and 79 patients). Firstly, learning curves of accuracy as a function of increasing training data size showed stabilisation of performance after using ∼10–15 patients for training. Best results were found for LDC, QDC and RF. Intraplaque haemorrhage was most accurately classified in both protocols, and lowest accuracy was found for the lipid-rich necrotic core. Secondly, for LDC and RF it was shown that leaving out different MRI sequences usually negatively affects results for one or more classes. However, leaving out T2-weighted scans did not have a big impact. In conclusion, several classifiers obtain generally good results for classification of plaque components in MRI. Identification of intraplaque haemorrhage is the most promising, and lipid-rich necrotic core remains the most difficult.

Original languageEnglish
Title of host publicationMedical Image Understanding and Analysis - 21st Annual Conference, MIUA 2017, Proceedings
PublisherSpringer Verlag
Number of pages13
ISBN (Print)9783319609638
Publication statusPublished - 2017
Event21st Annual Conference on Medical Image Understanding and Analysis, MIUA 2017 - Edinburgh, United Kingdom
Duration: 11 Jul 201713 Jul 2017

Publication series

NameCommunications in Computer and Information Science
ISSN (Print)18650929


Conference21st Annual Conference on Medical Image Understanding and Analysis, MIUA 2017
Country/TerritoryUnited Kingdom


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