Automated Detection and Screening of Traumatic Brain Injury (TBI) Using Computed Tomography Images: A Comprehensive Review and Future Perspectives.
Vidhya VAnjan GudigarU RaghavendraAjay HegdeGirish R MenonFilippo MolinariEdward J CiaccioUdyavara Rajendra AcharyaPublished in: International journal of environmental research and public health (2021)
Traumatic brain injury (TBI) occurs due to the disruption in the normal functioning of the brain by sudden external forces. The primary and secondary injuries due to TBI include intracranial hematoma (ICH), raised intracranial pressure (ICP), and midline shift (MLS), which can result in significant lifetime disabilities and death. Hence, early diagnosis of TBI is crucial to improve patient outcome. Computed tomography (CT) is the preferred modality of choice to assess the severity of TBI. However, manual visualization and inspection of hematoma and its complications from CT scans is a highly operator-dependent and time-consuming task, which can lead to an inappropriate or delayed prognosis. The development of computer aided diagnosis (CAD) systems could be helpful for accurate, early management of TBI. In this paper, a systematic review of prevailing CAD systems for the detection of hematoma, raised ICP, and MLS in non-contrast axial CT brain images is presented. We also suggest future research to enhance the performance of CAD for early and accurate TBI diagnosis.
Keyphrases
- traumatic brain injury
- computed tomography
- dual energy
- contrast enhanced
- positron emission tomography
- image quality
- severe traumatic brain injury
- coronary artery disease
- magnetic resonance imaging
- deep learning
- white matter
- magnetic resonance
- high resolution
- case report
- convolutional neural network
- mild traumatic brain injury
- resting state
- mass spectrometry
- label free
- machine learning
- high throughput
- functional connectivity
- multiple sclerosis
- pet ct