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.
Traumatic brain injury (TBI) is a major cause of death and disability in the United States. Time to treatment is often related to patient outcome. Access to cerebral imaging data in a timely manner is a vital component of patient care. Current methods of detecting and quantifying intracranial pathology can be time-consuming and require careful review of 2D/3D patient images by a radiologist. Additional time is needed for image protocoling, acquisition, and processing. These steps often occur in series, adding more time to the process and potentially delaying time-dependent management decisions for patients with traumatic brain injury.
Our team adapted machine learning and computer vision methods to develop a technique that rapidly and automatically detects CT-identifiable lesions. Specifically, we use scale invariant feature transform (SIFT)1 and deep convolutional neural networks (CNN)2 to identify important image features that can distinguish TBI lesions from background data. Our learning algorithm is a linear support vector machine (SVM)3. Further, we also employ tools from topological data analysis (TDA) for gleaning insights into the correlation patterns between healthy and pathological data. The technique was validated using 409 CT scans of the brain, acquired via the Progesterone for the Treatment of Traumatic Brain Injury phase III clinical trial (ProTECT_III) which studied patients with moderate to severe TBI4. CT data were annotated by a central radiologist and included patients with positive and negative scans. Additionally, the largest lesion on each positive scan was manually segmented. We reserved 80% of the data for training the SVM and used the remaining 20% for testing. Preliminary results are promising with 92.55% prediction accuracy (sensitivity = 91.15%, specificity = 93.45%), indicating the potential usefulness of this technique in clinical scenarios.
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