Machine Learning-based Segmentation of WMH in Brain MRI

In this project, we explored various machine learning algorithms to perform white matter hyperintensities (WMH) segmentation in brain MRI. From this project, we could observe how well deep neural networks for WMH segmentation compared to more conventional machine learning algorithms, such as support vector machine (SVM) and random forest (RF).

Published publications:

• Rachmadi, M. F., Valdés-Hernández, M. D. C., Agan, M. L. F., Di Perri, C., Komura, T., & Alzheimer’s Disease Neuroimaging Initiative. (2018). Segmentation of white matter hyperintensities using convolutional neural networks with global spatial information in routine clinical brain MRI with none or mild vascular pathology. Computerized Medical Imaging and Graphics, 66, 28-43. doi: 10.1016/j.compmedimag.2018.02.002
• Rachmadi, M., Valdés-Hernández, M., Agan, M., & Komura, T. (2017). Deep learning vs. conventional machine learning: pilot study of WMH segmentation in brain MRI with absence or mild vascular pathology. Journal of Imaging, 3(4), 66. doi: 10.3390/jimaging3040066
• Rachmadi, M. F., Valdés-Hernández, M. D. C., Agan, M. L. F., Komura, T., & Alzheimer’s Disease Neuroimaging Initiative. (2017, July). Evaluation of Four Supervised Learning Schemes in White Matter Hyperintensities Segmentation in Absence or Mild Presence of Vascular Pathology. In Annual Conference on Medical Image Understanding and Analysis (pp. 482-493). Springer, Cham.