Automatic Irregular Texture Detection in Brain MRI Without Human Supervision
We propose a novel approach named one-time sampling irregularity age map (OTS-IAM) to detect any irregular texture in FLAIR brain MRI without any human supervision or interaction. In this study, we show that OTS-IAM is able to detect FLAIR’s brain tissue irregularities (i.e. hyperintensities) without any manual labelling. One-time sampling (OTS) scheme is proposed in this study to speed up the computation. The proposed OTS-IAM implementation on GPU successfully speeds up IAM’s computation by more than 17 times. We compared the performance of OTS-IAM with two unsupervised methods for hyperintensities’ detection; the original IAM and the Lesion Growth Algorithm from public toolbox Lesion Segmentation Toolbox (LST-LGA), and two conventional supervised machine learning algorithms; support vector machine (SVM) and random forest (RF). Furthermore, we also compared OTS-IAM’s performance with three supervised deep neural networks algorithms; Deep Boltzmann machine (DBM), convolutional encoder network (CEN) and 2D convolutional neural network (2D Patch-CNN). Based on our experiments, OTS-IAM outperformed LST-LGA, SVM, RF and DBM while it was on par with CEN.
KeywordsIrregular texture detection MRI Unsupervised detection Hyperintensities detection
Funds from Indonesia Endowment Fund for Education (LPDP) of Ministry of Finance, Republic of Indonesia and Row Fogo Charitable Trust (Grant No. BRO-D.FID3668413) (MCVH) are gratefully acknowledged. Data collection and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense W81XWH-12-2-0012).
- 7.Rachmadi, M.F., Valdés-Hernández, M.C., Komura, T.: Voxel-based irregularity age map (IAM) for brain’s white matter hyperintensities in MRI. In: 2017 International Conference on Advanced Computer Science and Information Systems (ICACSIS), pp. 321–326. IEEE (2017). https://doi.org/10.1109/ICACSIS.2017.8355053