Region-based U-net for accelerated training and enhanced precision in
  deep brain segmentation
Mengyu Li Magnus Magnusson Thilo van Eimeren Lotta M. Ellingsen
Segmentation of brain structures on MRI is the primary step for further
quantitative analysis of brain diseases. Manual segmentation is still
considered the gold standard in terms of accuracy; however, such data is
extremely time-consuming to generate. This paper presents a deep learning-based
segmentation approach for 12 deep-brain structures, utilizing multiple
region-based U-Nets. The brain is divided into three focal regions of interest
that encompass the brainstem, the ventricular system, and the striatum. Next,
three region-based U-nets are run in parallel to parcellate these larger
structures into their respective four substructures. This approach not only
greatly reduces the training and processing times but also significantly
enhances the segmentation accuracy, compared to segmenting the entire MRI image
at once. Our approach achieves remarkable accuracy with an average Dice
Similarity Coefficient (DSC) of 0.901 and 95% Hausdorff Distance (HD95) of
1.155 mm. The method was compared with state-of-the-art segmentation
approaches, demonstrating a high level of accuracy and robustness of the
proposed method.