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J. Imaging 2017, 3(4), 66; doi:10.3390/jimaging3040066

Deep Learning vs. Conventional Machine Learning: Pilot Study of WMH Segmentation in Brain MRI with Absence or Mild Vascular Pathology

1
School of Informatics, University of Edinburgh, Edinburgh EH8 9AB, UK
2
Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh EH16 4SB, UK
This paper is an extended version of a conference paper: Rachmadi, M.; Komura, T.; Valdes Hernandez, M.; Agan, M. Evaluation of Four Supervised Learning Schemes in White Matter Hyperintensities Segmentation in Absence or Mild Presence of Vascular Pathology. In Communications in Computer and Information Science, Proceedings of the Medical Image Understanding and Analysis. (MIUA), Edinburgh, UK, 11–13 July 2017; Valdés Hernández, M., González-Castro, V., Eds.; Springer: Cham, Switzerland, 2017; Volume 723, pp. 482–493
*
Authors to whom correspondence should be addressed.
Received: 7 November 2017 / Revised: 7 December 2017 / Accepted: 12 December 2017 / Published: 14 December 2017
(This article belongs to the Special Issue Selected Papers from “MIUA 2017”)
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Abstract

In the wake of the use of deep learning algorithms in medical image analysis, we compared performance of deep learning algorithms, namely the deep Boltzmann machine (DBM), convolutional encoder network (CEN) and patch-wise convolutional neural network (patch-CNN), with two conventional machine learning schemes: Support vector machine (SVM) and random forest (RF), for white matter hyperintensities (WMH) segmentation on brain MRI with mild or no vascular pathology. We also compared all these approaches with a method in the Lesion Segmentation Tool public toolbox named lesion growth algorithm (LGA). We used a dataset comprised of 60 MRI data from 20 subjects in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, each scanned once every year during three consecutive years. Spatial agreement score, receiver operating characteristic and precision-recall performance curves, volume disagreement score, agreement with intra-/inter-observer reliability measurements and visual evaluation were used to find the best configuration of each learning algorithm for WMH segmentation. By using optimum threshold values for the probabilistic output from each algorithm to produce binary masks of WMH, we found that SVM and RF produced good results for medium to very large WMH burden but deep learning algorithms performed generally better than conventional ones in most evaluations. View Full-Text
Keywords: Alzheimer’s Disease; brain MRI; conventional machine learning; deep learning; dementia; white matter hyperintensities; segmentation; machine learning; medical image analysis Alzheimer’s Disease; brain MRI; conventional machine learning; deep learning; dementia; white matter hyperintensities; segmentation; machine learning; medical image analysis
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Rachmadi, M.F.; Valdés-Hernández, M.C.; Agan, M.L.F.; Komura, T. Deep Learning vs. Conventional Machine Learning: Pilot Study of WMH Segmentation in Brain MRI with Absence or Mild Vascular Pathology. J. Imaging 2017, 3, 66.

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