Vision-based estimation of volume status in ultrasound

This thesis provides a proof-of-concept approach to the analysis of ultrasound imagery using machine learning and computer vision for the purposes of tracking relative changes in apparent circulating blood volume. Data for the models was collected from a simulation which involved having healthy subjects recline at angles between 0 and 90 degrees to induce changes in the size of the internal jugular vein (IJV) resulting from gravity. Ultrasound video clips were then captured of the IJV. The clips were segmented, followed by feature generation, feature selection and training of predictive models to determine the angle of inclination. This research provides insight into the feasibility of using automated analysis techniques to enhance portable ultrasound as a monitoring tool. In a dataset of 34 subjects the angle was predicted within 11 degrees. An accuracy of 89% was achieved for high/low classification

Computer-based estimation of circulating blood volume from ultrasound imagery

Detection of relative changes in circulating blood volume is important to guide resuscitation and manage a variety of medical conditions including sepsis, trauma, dialysis and congestive heart failure. In recent years, ultrasound images of inferior vena cava (IVC) and internal jugular vein (IJV) have been used to assess volume status and guide fluid administration. This approach has limitations in that a skilled operator must perform repeated measurements over time. In this dissertation, we develop semi-automatic image processing algorithms for estimation and tracking of the IVC anterior-posterior (AP)-diameter and IJV crosssectional area in ultrasound videos. The proposed algorithms are based on active contours (ACs), where either the IVC AP-diameter or IJV CSA is estimated by minimization of an energy functional. More specifically, in chapter 2, we propose a novel energy functional based on the third centralized moment and show that it outperforms the functionals that are traditionally used with active contours (ACs). We combine the proposed functional with the polar contour representation and use it for segmentation of the IVC. In chapters 3 and 4, we propose active shape models based on ellipse; circle; and rectangles fitted inside the IVC as efficient, consistent and novel approaches to tracking and approximating the anterior-posterior (AP)-diameter even in the context of poor quality images. The proposed algorithms are based on a novel heuristic evolution functional that works very well with ultrasound images. In chapter 3, we show that the proposed active circle algorithm accurately, estimates the IVC AP-diameter. Although the estimated AP-diameter is very close to its actual value, the clinicians define the IVC AP-diameter as the largest vertical diameter of the IVC contour which deviates from its actual definition. To solve this problem and estimate the AP-diameter in the same way as its clinical definition, in chapter 4, we propose the active rectangle algorithm, where clinically measured AP-diameter is modeled as the height of a vertical thin rectangle. The results show that the AP-diameter estimated by the active rectangle algorithm is closer to its clinically measurement than the active circle and active ellipse algorithms. In chapter 5, we propose a novel adaptive polar active contour (Ad-PAC) algorithm for the segmentation and tracking of the IJV in ultrasound videos. In the proposed algorithm, the parameters of the Ad-PAC algorithm are adapted based on the results of segmentation in previous frames. The Ad-PAC algorithm has been applied to 65 ultrasound videos and shown to be a significant improvement over existing segmentation algorithms. So far, all proposed algorithms are semi-automatic as they need an operator to either locate the vessel in the first frame, or manually segment the first first and work automatically for the next frames. In chapter 6, we proposed a novel algorithm to automatically locate the vessel in ultrasound videos. The proposed algorithm is based on convolutional neural networks (CNNs) and is trained and applied for IJV videos. In this chapter we show that although the proposed algorithm is trained for data acquired from healthy subjects, it works efficiently for the data collected from coronary heart failure (CHF) patients without additional training. Finally, conclusions are drawn and possible extensions are discussed in chapter 7

Computational haemodynamics in stenotic internal jugular veins

Stenosis in internal jugular veins (IJVs) are frequently associated to pathological venous circulation and insufficient cerebral blood drainage. In this work, we set up a computational framework to assess the relevance of IJV stenoses through numerical simulation, combining medical imaging, patient-specific data and a mathematical model for venous occlusions. Coupling a three-dimensional (3D) description of blood flow in IJVs with a reduced one-dimesional model (1D) for major intracranial veins, we are able to model different anatomical configurations, an aspect of importance to understand the impact of IJV stenosis in intracranial venous haemodynamics. We investigate several stenotic configurations in a physiologic patient-specific regime, quantifying the effect of the stenosis in terms of venous pressure increase and wall shear stress patterns. Simulation results are in qualitative agreement with reported pressure anomalies in pathological cases. Moreover, they demonstrate the potential of the proposed multiscale framework for individual-based studies and computer-aided diagnosis

Imaging of adult ocular and orbital pathology - a pictorial review

Orbital pathology often presents a diagnostic challenge to the reporting radiologist. The aetiology is protean, and clinical input is therefore often necessary to narrow the differential diagnosis. With this manuscript, we provide a pictorial review of adult ocular and orbital pathology.peer-reviewe

The structure of the holocephalan head and the relationships of the Chondrichthyes

The interrelationship of the chondrichthyan subclasses is evaluated based on divergence in the nature of the suspensorium, the preorbital cranial anatomy, the distribution of major venous sinuses and localization of hematopoietic tissue. The anatomy of representative extant taxa was examined by radiography and/or dissection. Fossil selachians, paraselachians, and holocephalans of the Bear Gulch of Montana, U.S.A. (Mississippian, Namurian E2B) were studied for evidence of vascular pigmentation, suspensorium, and cranial, branchial, and pectoral anatomy. These studies validate the suspensorial condition of autodiastyly and suggest autodiastyly is a fundamental condition involved in the basic radiation of Chondrichthyes. The plesiomorphous condition of all gnathostomes is proposed to be sutodiastylic, with the hyoid arch modified for the support of an opercular covering. A precerebral fontanelle is primary within Chondrichthyes, being lost in Holocephali as cranial remodeling induces ethmoid canal formation. The holocephalan pattern of cranial vascularization is based on the more general selachian plan. Thus, given the formulation of a morphocline based on selachian, paraselachian, and holocephalan data, seemingly distinct selachian and holocephalan vascular elements are shown to be analogous. Similarly, the unique patterns of lymphomyeloid tissue distribution identified for each subclass may also be explained on the basis of general plan which has been subject to relocalization stresses. Finally, both the morphocline and a cladistical analysis of the data support a cochliodont ancestry for extant holocephalans

Fully automated segmentation and tracking of the intima media thickness in ultrasound video sequences of the common carotid artery

Abstract—The robust identification and measurement of the intima media thickness (IMT) has a high clinical relevance because it represents one of the most precise predictors used in the assessment of potential future cardiovascular events. To facilitate the analysis of arterial wall thickening in serial clinical investigations, in this paper we have developed a novel fully automatic algorithm for the segmentation, measurement, and tracking of the intima media complex (IMC) in B-mode ultrasound video sequences. The proposed algorithm entails a two-stage image analysis process that initially addresses the segmentation of the IMC in the first frame of the ultrasound video sequence using a model-based approach; in the second step, a novel customized tracking procedure is applied to robustly detect the IMC in the subsequent frames. For the video tracking procedure, we introduce a spatially coherent algorithm called adaptive normalized correlation that prevents the tracking process from converging to wrong arterial interfaces. This represents the main contribution of this paper and was developed to deal with inconsistencies in the appearance of the IMC over the cardiac cycle. The quantitative evaluation has been carried out on 40 ultrasound video sequences of the common carotid artery (CCA) by comparing the results returned by the developed algorithm with respect to ground truth data that has been manually annotated by clinical experts. The measured IMTmean ± standard deviation recorded by the proposed algorithm is 0.60 mm ± 0.10, with a mean coefficient of variation (CV) of 2.05%, whereas the corresponding result obtained for the manually annotated ground truth data is 0.60 mm ± 0.11 with a mean CV equal to 5.60%. The numerical results reported in this paper indicate that the proposed algorithm is able to correctly segment and track the IMC in ultrasound CCA video sequences, and we were encouraged by the stability of our technique when applied to data captured under different imaging conditions. Future clinical studies will focus on the evaluation of patients that are affected by advanced cardiovascular conditions such as focal thickening and arterial plaques

Liver cirrhosis: New concepts

The term `cirrhosis` is used for two centuries to define the end-stage of chronic liver diseases with different etiologies. Clinical manifestations of cirrhosis are related to portal hypertension, hepatic dysfunction progressing to liver failure and development of hepatocellular carcinoma, conditions with unfavorable prognosis. However, recent advances in the diagnosis and treatment of chronic liver diseases have changed the natural history of cirrhosis significantly. According to current concept, liver cirrhosis is heterogeneous, multi-stage condition with variable prognosis. Cirrhosis is considered as a dynamic, biphasic process, based on numerous clinical reports indicating the reversal of advanced fibrosis and cirrhosis after cessation of perpetual injury. This review was focused on current pathology and clinical staging of cirrhosis. The potential mechanism and proves of concept for reversibility of cirrhosis were also discussed

The relative contributions of muscle deformation and ischaemia to pressure ulcer development

Pressure ulcers are localised areas of soft tissue breakdown that develop over bony prominences as a result of sustained mechanical loading. They are particularly common in bedridden and wheelchair-bound individuals, and represent one of the most common secondary complications in spinal cord injured subjects. A specific form of pressure ulcers is termed deep tissue injury (DTI), which is defined as pressure-related injury to subcutaneous tissues such as skeletal muscle, initially under intact skin. DTI represents a severe problem, because tissue damage at the skin surface only becomes apparent at an advanced stage, and is associated with a variable prognosis. Therefore, early identification and subsequent treatment of DTI are critical to reduce comorbidities and the financial and manpower burdens associated with treatment. This requires a better understanding of its underlying aetiology, in order to develop appropriate risk assessment tools and early detection methods. Therefore, the main goal of the present thesis was to study the aetiology of DTI. In addition, some explorative studies were performed to examine potential methods for the early detection of DTI. The aetiological factors were investigated using a combination of experiments and numerical models. This involved an established rat model for DTI that has previously been used to study the effects of deformation due to 2 h continuous loading. In the present thesis, different loading regimens were applied to further investigate the role of deformation. In addition, a previously developed finite element model to estimate muscle deformations during loading, was substantially improved to enable a local comparison of deformation with damage. Furthermore, the duration of the experiments was extended to 6 h to investigate the effects of ischaemia and reperfusion. It was found that deformation is the primary trigger for muscle damage for loading periods up to 2 h when a specific deformation threshold is exceeded. Ischaemia started to cause changes in muscle tissue between 2-4 h loading. Therefore, the damage development in skeletal muscle during prolonged loading is determined by deformation, ischaemia, and reperfusion, each mechanism exhibiting a unique time profile. The developed methods were also applied to a porcine model for DTI to investigate the deformations of the different soft tissues of the buttocks during loading. In this study, it was shown that the relative mechanical properties of the different tissue layers have a large influence on the distribution of the internal deformations. The release of biochemical damage markers from injured muscle tissue into the circulation was studied to investigate the possibility of using these proteins for the early detection of DTI. Baseline variations of creatine kinase, myoglobin, heart-type fatty acid binding protein, and C-reactive protein were assessed in able-bodied and spinal cord injured human volunteers. These variations were small compared to the predicted increase in biomarker concentrations during DTI development, indicating that this combination of markers may prove appropriate for the early detection of DTI. Moreover, a considerable increase in myoglobin concentrations in blood and urine was observed in a rat model for DTI after 6 h mechanical loading. The present findings have implications for clinical practice. In particular, it is important to minimise the internal tissue deformations in subjects at risk of DTI, such as present in subjects with spinal cord injury and those positioned on hard surfaces, such as stretchers or operating tables, for prolonged periods. Furthermore, the period of loading should be limited to prevent the accumulation of ischaemic damage. The observation of increased myoglobin levels in blood and urine after mechanical loading demonstrates the potential of using biochemical markers of muscle damage for the early detection of DTI. Moreover, the increase of myoglobin levels in urine suggests that a noninvasive approach for this screening method may be satisfactory

Personalized computational models of deep brain stimulation

University of Minnesota Ph.D. dissertation. December 2016. Major: Biomedical Engineering. Advisor: Matthew Johnson. 1 computer file (PDF); xii, 138 pages.Deep brain stimulation (DBS) therapy is used for managing symptoms associated with a growing number of neurological disorders. One of the primary challenges with delivering this therapy, however, continues to be accurate neurosurgical targeting of the DBS lead electrodes and post-operative programming of the stimulation settings. Two approaches for addressing targeting have been advanced in recent years. These include novel DBS lead designs with more electrodes and computational models that can predict cellular modulation during DBS. Here, we developed a personalized computational modeling framework to (1) thoroughly investigate the electrode design parameter space for current and future DBS array designs, (2) generate and evaluate machine learning feature sets for semi-automated programming of DBS arrays, (3) study the influence of model parameters in predicting behavioral and electrophysiological outcomes of DBS in a preclinical animal model of Parkinson’s disease, and (4) evaluate feasibility of a novel endovascular targeting approach to delivering DBS therapy in humans. These studies show how independent current controlled stimulation with advanced machine learning algorithms can negate the need for highly dense electrode arrays to shift, steer, and sculpt regions of modulation within the brain. Additionally, these studies show that while advanced and personalized computational models of DBS can predict many of the behavioral and electrophysiological outcomes of DBS, there are remaining inconsistencies that suggest there are additional physiological mechanisms of DBS that are not yet well understood. Finally, the results show how computational models can be beneficial for prospective development of novel approaches to neuromodulation prior to large-scale preclinical and clinical studies