  In modern medical diagnostics, magnetic resonance imaging (MRI) is an
important technique that provides detailed insights into anatomical structures.
In this paper, we present a comprehensive methodology focusing on streamlining
the segmentation, reconstruction, and visualization process of 3D MRI data.
Segmentation involves the extraction of anatomical regions with the help of
state-of-the-art deep learning algorithms. Then, 3D reconstruction converts
segmented data from the previous step into multiple 3D representations.
Finally, the visualization stage provides efficient and interactive
presentations of both 2D and 3D MRI data. Integrating these three steps, the
proposed system is able to augment the interpretability of the anatomical
information from MRI scans according to our interviews with doctors. Even
though this system was originally designed and implemented as part of human
brain haptic feedback simulation for surgeon training, it can also provide
experienced medical practitioners with an effective tool for clinical data
analysis, surgical planning and other purposes
