White matter hyperintensities (WMH) are commonly found as bright regions in brain MRI images in older individuals. They are associated with various neurological and vascular diseases, such as stroke, dementia, and cardiovascular disorders. WMH is also one of the seven radiological parameters included in the idiopathic normal pressure hydrocephalus (iNPH) Radscale, used to grade the radiological signs of normal pressure hydrocephalus. The radiological patterns are highly heterogenous, making quantification and classification of WMHs complex. In this project, an automated method for quantitative and qualitative assessment of white matter hyperintensities was developed based on the deep learning architecture nnU-Net.  Different configurations of the nnU-Net network were trained on a publicly available dataset from the 2017 Medical Image Computing and Computer Assisted Invention Society (MICCAI) WMH segmentation challenge constituting different grades of WMHs, and eventually ensembled to produce the final output of the segmentation model. Based on the segmentation result, a volumetric assessment was performed using Fazekas scale, which can then be used as one of the essential radiological parameters in the iNPH Radscale. Furthermore, the pipeline subdivides and classifies the hyperintense regions based on spatial information and T1-signal intensity, which is believed to have a significant impact on the pathology of the WMHs.  The final pipeline accurately segments WMHs from T1-weighted and FLAIR MRI images with a Dice’s similarity coefficient of 0.81, quantitatively classifies each case according to Fazekas scale and further subdivides each hyperintense voxel based on its location in the brain and intensity in the T1-weighted image. Hopefully, this can serve as a meaningful tool in the diagnosis of iNPH as well as future research aiming to fully understand the clinical implications of different types of WMHs.