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

Journal of Imaging

Maria Leonora Fatimah Agan¹  Maria del C. Valdés-Hernández¹  Taku Komura²  Muhammad Febrian Rachmadi² 

[1] Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh EH16 4SB, UK;School of Informatics, University of Edinburgh, Edinburgh EH8 9AB, UK;

关键词: Alzheimer’s Disease;    brain MRI;    conventional machine learning;    deep learning;    dementia;    white matter hyperintensities;    segmentation;    machine learning;    medical image analysis;   

DOI  :  10.3390/jimaging3040066

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown