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Automated segmentation of medical imaging is of broad interest to cliniciansand machine learning researchers alike. The goal of segmentation is to increaseefficiency and simplicity of visualization and quantification of regions ofinterest within a medical image. Image segmentation is a difficult task becauseof multiparametric heterogeneity within the images, an obstacle that has provenespecially challenging in efforts to automate the segmentation of brain lesionsfrom non-contrast head computed tomography (CT). In this research, we haveexperimented with multiple available deep learning architectures to segmentdifferent phenotypes of hemorrhagic lesions found after moderate to severetraumatic brain injury (TBI). These include: intraparenchymal hemorrhage (IPH),subdural hematoma (SDH), epidural hematoma (EDH), and traumatic contusions. Wewere able to achieve an optimal Dice Coefficient1 score of 0.94 using UNet++ 2DArchitecture with Focal Tversky Loss Function, an increase from 0.85 using UNet2D with Binary Cross-Entropy Loss Function in intraparenchymal hemorrhage (IPH)cases. Furthermore, using the same setting, we were able to achieve the DiceCoefficient score of 0.90 and 0.86 in cases of Extra-Axial bleeds and Traumaticcontusions, respectively.

A comparative study of 2D image segmentation algorithms for traumatic brain lesions using CT data from the ProTECTIII multicenter clinical trial