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

Automatic Reporting of TBI Lesion Location in CT based on Deep Learning and Atlas Mapping

Tese de mestrado integrado, Engenharia Biomédica e Biofísica (Biofísica Médica e Fisiologia de Sistemas), 2021, Universidade de Lisboa, Faculdade de CiênciasThe assessment of Computed Tomography (CT) scans for Traumatic Brain Injury (TBI) management remains a time consuming and challenging task for physicians. Computational methods for quantitative lesion segmentation and localisation may increase consistency in diagnosis and prognosis criteria. Our goal was to develop a registration-based tool to accurately localise several lesion classes (i.e., calculate the volume of lesion per brain region), as an extension of the Brain Lesion Analysis and Segmentation Tool for CT (BLAST-CT). Lesions were located by projecting a Magnetic Resonance Imaging (MRI) labelled atlas from the Montreal Neurological Institute (MNI MRI atlas) to a lesion map in native space. We created a CT template to work as an intermediate step between the two imaging spaces, using 182 non-lesioned CT scans and an unbiased iterative registration approach. We then non-linearly registered the parcellated atlas to the CT template, subsequently registering (affine) the result to native space. From the final atlas alignment, it was possible to calculate the volume of each lesion class per brain region. This pipeline was validated on a multi-centre dataset (n=839 scans), and defined three methods to flag any scans that presented sub-optimal results. The first one was based on the similarity metric of the registration of every scan to the study-specific CT template, the second aimed to identify any scans with regions that were completely collapsed post registration, and the final one identified scans with a significant volume of intra-ventricular haemorrhage outside of the ventricles. Additionally, an assessment of lesion prevalence and of the false negative and false positive rates of the algorithm, per anatomical region, was conducted, along with a bias assessment of the BLAST-CT tool. Our results show that the constructed pipeline is able to successfully localise TBI lesions across the whole brain, although without voxel-wise accuracy. We found the error rates calculated for each brain region to be inversely correlated with the lesion volume within that region. No considerable bias was identified on BLAST-CT, as all the significant correlation coefficients calculated between the Dice scores and clinical variables (i.e., age, Glasgow Coma Scale, Extended Glasgow Outcome Scale and Injury Severity Score) were below 0.2. Our results also suggest that the variation in DSC between male and female patients within a specific age range was caused by the discrepancy in lesion volume presented by the scans included in each sample

Need for speed:Achieving fast image processing in acute stroke care

This thesis aims to investigate the use of high-performance computing (HPC) techniques in developing imaging biomarkers to support the clinical workflow of acute stroke patients. In the first part of this thesis, we evaluate different HPC technologies and how such technologies can be leveraged by different image analysis applications used in the context of acute stroke care. More specifically, we evaluated how computers with multiple computing devices can be used to accelerate medical imaging applications in Chapter 2. Chapter 3 proposes a novel data compression technique that efficiently processes CT perfusion (CTP) images in GPUs. Unfortunately, the size of CTP datasets makes data transfers to computing devices time-consuming and, therefore, unsuitable in acute situations. Chapter 4 further evaluates the algorithm's usefulness proposed in Chapter 3 with two different applications: a double threshold segmentation and a time-intensity profile similarity (TIPS) bilateral filter to reduce noise in CTP scans. Finally, Chapter 5 presents a cloud platform for deploying high-performance medical applications for acute stroke patients. In Part 2 of this thesis, Chapter 6 presents a convolutional neural network (CNN) for detecting and volumetric segmentation of subarachnoid hemorrhages (SAH) in non-contrast CT scans. Chapter 7 proposed another method based on CNNs to quantify the final infarct volumes in follow-up non-contrast CT scans from ischemic stroke patients

Automatic quantification of brain midline shift in CT images

Ph.DDOCTOR OF PHILOSOPH

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

Computer aided diagnosis of cerebrovascular disease based on DSA image

In recent years, the incidence of cerebrovascular diseases in China has shown a significant upward trend, and it has become a common disease threatening people's lives. Digital Subtraction Angiography (DSA) is the gold standard for the diagnosis of clinical cerebrovascular disease, and it is the most direct method to check the brain lesion. At present, there are the following two problems in the clinical research of DSA images: DSA is a real-time image with numerous frames, containing much useless information in frames; thus, human interpretation and annotation are time-consuming and labor-intensive. The blood vessel structure in DSA images is so complicated that high practical skills are required for clinicians. In the computer-aided diagnosis of DSA sequence images, there is currently a lack of automatic and effective computer-aided diagnosis algorithms for cerebrovascular diseases. Based on the above issues, the main work of this paper is as follows: 1.A multi-target detection algorithm based on Faster-RCNN is designed and applied to the analysis of brain DSA images. The algorithm divides DSA images into arterial phase, capillary phase, pre-venous phase and sinus phase by identifying the main blood vessel structure in each frame. And on this basis, we analyze the time relationship between the time phases. 2.On the basis of DSA phase detection, a key frame location algorithm based on single blood vessel structure detection is designed for moyamoya disease. First, the target detection model is applied to locate the internal carotid artery and the Willis circle. Then, five frames of images are extracted from the arterial period as keyframes. Finally, the nidus' ROI is determined according to the position of the internal carotid artery. 3.A diagnostic method for cerebral arteriovenous malformation (AVM) is designed, which combines temporal features and radiomics features. First, on the basis of DSA time phase detection, we propose a deep learning network to extract vascular time features from the DSA video; then, the time feature is combined with the radiomics features of the static keyframe to establish an AVM diagnosis model. While assisting diagnosis, this method does not require any human intervention, and reduces the workload of clinicians. The diagnostic model that combines time features and radiomics features is applied to the study of AVM staging. The experimental results prove that the classification model trained by fusion features has better diagnostic performance than the model trained by either time features or radiomics features. Based on the above three parts, this paper establishes a cerebrovascular disease analysis framework based on radiomics method and deep learning. We introduce corresponding solutions for DSA automatic image reading, rapid diagnosis of moyamoya disease, and precise diagnosis of AVM. The method proposed in this paper has practical significance for assisting the diagnosis of cerebrovascular disease and reducing the burden of medical staff.Digital Subtraction Angiography(DSA), Radiomics analysis, Arteriovenous malformations, Moyamoya, Faster-RCNN, Temporal features, Fusion feature

Advancements and Breakthroughs in Ultrasound Imaging

Ultrasonic imaging is a powerful diagnostic tool available to medical practitioners, engineers and researchers today. Due to the relative safety, and the non-invasive nature, ultrasonic imaging has become one of the most rapidly advancing technologies. These rapid advances are directly related to the parallel advancements in electronics, computing, and transducer technology together with sophisticated signal processing techniques. This book focuses on state of the art developments in ultrasonic imaging applications and underlying technologies presented by leading practitioners and researchers from many parts of the world