Evaluation of Four Supervised Learning Schemes in White Matter Hyperintensities Segmentation in Absence or Mild Presence of Vascular Pathology

Muhammad Rachmadi, Taku Komura, Maria Valdes Hernandez, Maria Agan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We investigated the performance of four popular supervised learning algorithms in medical image analysis for white matter hyperintensities segmentation in brain MRI with mild or no vascular pathology. The algorithms evaluated in this study are support vector machine (SVM), random forest (RF), deep Boltzmann machine (DBM) and convolution encoder network (CEN). We compared these algorithms with two methods in the Lesion Segmentation Tool (LST) public toolbox which are lesion growth algorithm (LGA) and lesion prediction algorithm
(LPA). We used a dataset comprised of 60 MRI data from 20 subjects from the ADNI database, each scanned once in three consecutive years. In this study, CEN produced the best Dice similarity coefficient (DSC): mean value 0.44. All algorithms struggled to produce good DSC due to the very small WMH burden (i.e., smaller than 1,500 mm3). LST-LGA, LST-LPA, SVM, RF and DBM produced mean DSC scores ranging from 0.17 to 0.34.
Original languageEnglish
Title of host publication Medical Image Understanding and Analysis (MIUA 2017)
PublisherSpringer, Cham
Pages482-493
Number of pages12
ISBN (Electronic)978-3-319-60964-5
ISBN (Print)978-3-319-60963-8
DOIs
Publication statusPublished - 22 Jun 2017
EventMedical Image Understanding and Analysis (MIUA 2017) - Edinburgh, United Kingdom
Duration: 11 Jul 201713 Jul 2017

Publication series

NameCommunications in Computer and Information Science
PublisherSpringer, Cham
Volume723
ISSN (Print)1865-0929

Conference

ConferenceMedical Image Understanding and Analysis (MIUA 2017)
CountryUnited Kingdom
CityEdinburgh
Period11/07/1713/07/17

Keywords

  • brain MRI
  • white matter hyperintensity
  • image segmentation
  • supervised learning
  • Deep neural network

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