  Automated segmentation of medical imaging is of broad interest to clinicians
and machine learning researchers alike. The goal of segmentation is to increase
efficiency and simplicity of visualization and quantification of regions of
interest within a medical image. Image segmentation is a difficult task because
of multiparametric heterogeneity within the images, an obstacle that has proven
especially challenging in efforts to automate the segmentation of brain lesions
from non-contrast head computed tomography (CT). In this research, we have
experimented with multiple available deep learning architectures to segment
different phenotypes of hemorrhagic lesions found after moderate to severe
traumatic brain injury (TBI). These include: intraparenchymal hemorrhage (IPH),
subdural hematoma (SDH), epidural hematoma (EDH), and traumatic contusions. We
were able to achieve an optimal Dice Coefficient1 score of 0.94 using UNet++ 2D
Architecture with Focal Tversky Loss Function, an increase from 0.85 using UNet
2D with Binary Cross-Entropy Loss Function in intraparenchymal hemorrhage (IPH)
cases. Furthermore, using the same setting, we were able to achieve the Dice
Coefficient score of 0.90 and 0.86 in cases of Extra-Axial bleeds and Traumatic
contusions, respectively.
