AUTOMATED MIDLINE SHIFT DETECTION ON BRAIN CT IMAGES FOR COMPUTER-AIDED CLINICAL DECISION SUPPORT

Midline shift (MLS), the amount of displacement of the brain’s midline from its normal symmetric position due to illness or injury, is an important index for clinicians to assess the severity of traumatic brain injury (TBI). In this dissertation, an automated computer-aided midline shift estimation system is proposed. First, a CT slice selection algorithm (SSA) is designed to automatically select a subset of appropriate CT slices from a large number of raw images for MLS detection. Next, ideal midline detection is implemented based on skull bone anatomical features and global rotation assumptions. For the actual midline detection algorithm, a window selection algorithm (WSA) is applied first to confine the region of interest, then the variational level set method is used to segment the image and extract the ventricle contours. With a ventricle identification algorithm (VIA), the position of actual midline is detected based on the identified right and left lateral ventricle contours. Finally, the brain midline shift is calculated using the positions of detected ideal midline and actual midline. One of the important applications of midline shift in clinical medical decision making is to estimate the intracranial pressure (ICP). ICP monitoring is a standard procedure in the care of severe traumatic brain injury (TBI) patients. An automated ICP level prediction model based on machine learning method is proposed in this work. Multiple features, including midline shift, intracranial air cavities, ventricle size, texture patterns, and blood amount, are used in the ICP level prediction. Finally, the results are evaluated to assess the effectiveness of the proposed method in ICP level prediction

Computer aided assessment of CT scans of traumatic brain injury patients

A thesis submitted in partial fulfilment for the degree of Doctor of PhilosophyOne of the serious public health problems is the Traumatic Brain Injury, also known as silent epidemic, affecting millions every year. Management of these patients essentially involves neuroimaging and noncontrast CT scans are the first choice amongst doctors. Significant anatomical changes identified on the neuroimages and volumetric assessment of haemorrhages and haematomas are of critical importance for assessing the patients’ condition for targeted therapeutic and/or surgical interventions. Manual demarcation and annotation by experts is still considered gold standard, however, the interpretation of neuroimages is fraught with inter-observer variability and is considered ’Achilles heel’ amongst radiologists. Errors and variability can be attributed to factors such as poor perception, inaccurate deduction, incomplete knowledge or the quality of the image and only a third of doctors confidently report the findings. The applicability of computer aided dianosis in segmenting the apposite regions and giving ’second opinion’ has been positively appraised to assist the radiologists, however, results of the approaches vary due to parameters of algorithms and manual intervention required from doctors and this presents a gap for automated segmentation and estimation of measurements of noncontrast brain CT scans. The Pattern Driven, Content Aware Active Contours (PDCAAC) Framework developed in this thesis provides robust and efficient segmentation of significant anatomical landmarks, estimations of their sizes and correlation to CT rating to assist the radiologists in establishing the diagnosis and prognosis more confidently. The integration of clinical profile of the patient into image segmentation algorithms has significantly improved their performance by highlighting characteristics of the region of interest. The modified active contour method in the PDCAAC framework achieves Jaccard Similarity Index (JI) of 0.87, which is a significant improvement over the existing methods of active contours achieving JI of 0.807 with Simple Linear Iterative Clustering and Distance Regularized Level Set Evolution. The Intraclass Correlation Coefficient of intracranial measurements is >0.97 compared with radiologists. Automatic seeding of the initial seed curve within the region of interest is incorporated into the method which is a novel approach and alleviates limitation of existing methods. The proposed PDCAAC framework can be construed as a contribution towards research to formulate correlations between image features and clinical variables encompassing normal development, ageing, pathological and traumatic cases propitious to improve management of such patients. Establishing prognosis usually entails survival but the focus can also be extended to functional outcomes, residual disability and quality of life issues

CT Scanning

Since its introduction in 1972, X-ray computed tomography (CT) has evolved into an essential diagnostic imaging tool for a continually increasing variety of clinical applications. The goal of this book was not simply to summarize currently available CT imaging techniques but also to provide clinical perspectives, advances in hybrid technologies, new applications other than medicine and an outlook on future developments. Major experts in this growing field contributed to this book, which is geared to radiologists, orthopedic surgeons, engineers, and clinical and basic researchers. We believe that CT scanning is an effective and essential tools in treatment planning, basic understanding of physiology, and and tackling the ever-increasing challenge of diagnosis in our society

An efficient CNN-BiLSTM model for multi-class intracranial hemorrhage classification

Intracranial hemorrhage (ICH) refers to a type of bleeding that occurs within the skull. ICH may be caused by a wide range of pathology, including, trauma, hypertension, cerebral amyloid angiopa- thy, and cerebral aneurysms. Different subtypes of ICH exist based on their location in the brain, including epidural hemorrhage (EDH), subdural hemorrhage (SDH), subarachnoid hemorrhage (SAH), intraventricular hemorrhage (IVH), and intraparenchymal hemorrhage (IPH). Prompt de- tection and management of ICH are crucial as it is a life-threatening medical emergency with high morbidity and mortality rates. Despite accounting for only 10-15% of all strokes, ICH is respon- sible for over 50% of stroke-related deaths. Therefore, the presence, type, and location of an ICH must be immediately diagnosed so that the patients can receive medical intervention. However, accurately identifying ICH in CT slices can be challenging due to the brain’s complex anatomy and the variability in hemorrhage appearance. [...

Automated Decision Support System for Traumatic Injuries

With trauma being one of the leading causes of death in the U.S., automated decision support systems that can accurately detect traumatic injuries and predict their outcomes are crucial for preventing secondary injuries and guiding care management. My dissertation research incorporates machine learning and image processing techniques to extract knowledge from structured (e.g., electronic health records) and unstructured (e.g., computed tomography images) data to generate real-time, robust, quantitative trauma diagnosis and prognosis. This work addresses two challenges: 1) incorporating clinical domain knowledge into deep convolutional neural networks using classical image processing techniques and 2) using post-hoc explainers to align black box predictive machine learning models with clinical domain knowledge. Addressing these challenges is necessary for developing trustworthy clinical decision-support systems that can be generalized across the healthcare system. Motivated by this goal, we introduce an explainable and expert-guided machine learning framework to predict the outcome of traumatic brain injury. We also propose image processing approaches to automatically assess trauma from computed tomography scans. This research comprises four projects. In the first project, we propose an explainable hierarchical machine learning framework to predict the long-term functional outcome of traumatic brain injury using information available in electronic health records. This information includes demographic data, baseline features, radiology reports, laboratory values, injury severity scores, and medical history. To build such a framework, we peer inside the black-box machine learning models to explain their rationale for each predicted risk score. Accordingly, additional layers of statistical inference and human expert validation are added to the model, which ensures the predicted risk score’s trustworthiness. We demonstrate that imposing statistical and domain knowledge “checks and balances” not only does not adversely affect the performance of the machine learning classifier but also makes it more reliable. In the second project, we introduce a framework for detecting and assessing the severity of brain subdural hematomas. First, the hematoma is segmented using a combination of hand-crafted and deep learning features. Next, we calculate the volume of the injured region to quantitatively assess its severity. We show that the combination of classical image processing and deep learning can outperform deep-learning-only methods to achieve improved average performance and robustness. In the third project, we develop a framework to identify and assess liver trauma by calculating the percentage of the liver parenchyma disrupted by trauma. First, liver parenchyma and trauma masks are segmented by employing a deep learning backbone. Next, these segmented regions are refined with respect to the domain knowledge about the location and intensity distribution of liver trauma. This framework accurately estimated the severity of liver parenchyma trauma. In the final project, we propose a kidney segmentation method for patients with blunt abdominal trauma. This model incorporates machine learning and active contour modeling to generate kidney masks on abdominal CT images. The resultant of this component can provide a region of interest for screening kidney traumas in future studies. Together, the four projects discussed in this thesis contribute to diagnosis and prognosis of trauma across multiple body regions. They provide a quantitative assessment of traumas that is a more accurate measurement of the risk for adverse health outcomes as an alternative to current qualitative and sometimes subjective current clinical practice.PHDBioinformaticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/168065/1/negarf_1.pd

Bioengineering Analysis of Traumatic Brain Injury

Traumatic brain injury (TBI) is a serious health concern affecting over a million people in the UK. Brain shift and herniation, which are closely related to severe disability or death, are important signs of abnormally elevated intracranial pressure (ICP) or space-occupying intracranial mass after trauma. This research aims to use medical image computing and biomechanical modelling techniques to characterise the specific deformation field of brain tissues under various TBI scenarios and strengthen the biomechanical understanding across the full spectrum of TBI. Medical image computing provides the research with a solid clinical grounding. To better interpret the neuro-images, three computational tools have been developed, including a CT preprocessing pipeline, an automatic mid-sagittal plane detector and an automatic brain extractor. Using these tools, a novel concept of midplane shift (MPS) is developed to quantitatively evaluate the brain herniation condition across the mid-sagittal plane. In the meantime, a lesion heatmap is generated to quantify the asymmetric haematoma volumes across the mid-sagittal plane. The MPS heatmaps generated for 33 TBI patients with heterogeneous brain pathologies demonstrate highly similar shift patterns. Together with the lesion heatmap, a brain deformation mechanism has been presented: the brain will not deform randomly in response to trauma, instead, it will only deform in a regulated mechanism so that the deformation is directed and restricted to the soft ventricular region, thanks to the anatomic structures of the head such as the falx. The MPS heatmap, the lesion heatmap, together with the novel CT parameters derived from them, provide a rich abundance of information on intracranial brain herniation, for a more complete overview of TBI from medical images. Biomechanical modelling, being one of the most important tools in trauma biomechanics, has been used to quantitatively simulate the brain shift and herniation condition caused by various intracranial lesions and increasing ICP. Preliminary finite element models reconstructed from the Virtual Human Project have demonstrated some limitations. To resolve the observed deficiencies, an advanced high-fidelity patient-specific FE brain model is constructed and explicitly assessed to optimise its injury simulation performance with the help of the developed medical image computing tools. During simulation, the patient-specific traumatic injuries have been reconstructed by imposing both the primary lesion and the secondary injury. The primary lesion simulation is achieved mechanically by ``indenting" a rigid lesion surface simulating the shape of the haematoma to the brain model. While the secondary swelling is modelled with a thermal-expansion-based method to simulate the bulging brain. Using this approach, the observed brain herniation can be decomposed into a deformation due to pure mass effect of space-occupying primary lesion and a shift as a result of secondary swelling. The head injuries of six different TBI patients have been reconstructed and simulated using the prescribed method. The realistic case study suggested that the subdural haematoma patients, as compared to the epidural haematoma patients, were exposed to more significant secondary swelling, which agrees well with the historical clinical findings. In addition to the realistic TBI case studies, an idealised traumatic lesion simulation is performed to investigate the role of lesion morphology and the lesion locations of onsets, in brain herniations during TBI. It is suggested by the idealised TBI cases that the brain is more sensitive to lesion that is more concentrated spatially, if lesion volumes and lesion locations were exactly the same. Moreover, in terms of lesion locations, lesions that strikes on the temporal region and the anterior region are more likely to lead to greater brain deformation, if other lesion morphologies were equal and no secondary swelling considered. Ultimately, the developed tools are expected to help clinicians better understand and predict the brain behaviour after the onset of TBI and during subsequent injury evolution.WD Armstrong Trus