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

Abstract

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.

9 pages, 3 figures, 3 tables

References in corpus (10)

• An overview of gradient descent optimization algorithms
• Efficient Multi-Scale 3D CNN with Fully Connected CRF for Accurate Brain Lesion Segmentation
• Brain Tumor Segmentation with Deep Neural Networks
• Generalised Dice overlap as a deep learning loss function for highly unbalanced segmentations
• Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels
• Focal Loss for Dense Object Detection
• MatConvNet - Convolutional Neural Networks for MATLAB
• Machine Learning for Neuroimaging with Scikit-Learn
• TBI Contusion Segmentation from MRI using Convolutional Neural Networks
• Improving Siamese Networks for One Shot Learning using Kernel Based Activation functions

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