Thrombolytic removal of intraventricular haemorrhage in treatment of severe stroke: results of the randomised, multicentre, multiregion, placebo-controlled CLEAR III trial

Background: Intraventricular haemorrhage is a subtype of intracerebral haemorrhage, with 50% mortality and serious disability for survivors. We aimed to test whether attempting to remove intraventricular haemorrhage with alteplase versus saline irrigation improved functional outcome. Methods: In this randomised, double-blinded, placebo-controlled, multiregional trial (CLEAR III), participants with a routinely placed extraventricular drain, in the intensive care unit with stable, non-traumatic intracerebral haemorrhage volume less than 30 mL, intraventricular haemorrhage obstructing the 3rd or 4th ventricles, and no underlying pathology were adaptively randomly assigned (1:1), via a web-based system to receive up to 12 doses, 8 h apart of 1 mg of alteplase or 0·9% saline via the extraventricular drain. The treating physician, clinical research staff, and participants were masked to treatment assignment. CT scans were obtained every 24 h throughout dosing. The primary efficacy outcome was good functional outcome, defined as a modified Rankin Scale score (mRS) of 3 or less at 180 days per central adjudication by blinded evaluators. This study is registered with ClinicalTrials.gov, NCT00784134. Findings: Between Sept 18, 2009, and Jan 13, 2015, 500 patients were randomised: 249 to the alteplase group and 251 to the saline group. 180-day follow-up data were available for analysis from 246 of 249 participants in the alteplase group and 245 of 251 participants in the placebo group. The primary efficacy outcome was similar in each group (good outcome in alteplase group 48% vs saline 45%; risk ratio [RR] 1·06 [95% CI 0·88–1·28; p=0·554]). A difference of 3·5% (RR 1·08 [95% CI 0·90–1·29], p=0·420) was found after adjustment for intraventricular haemorrhage size and thalamic intracerebral haemorrhage. At 180 days, the treatment group had lower case fatality (46 [18%] vs saline 73 [29%], hazard ratio 0·60 [95% CI 0·41–0·86], p=0·006), but a greater proportion with mRS 5 (42 [17%] vs 21 [9%]; RR 1·99 [95% CI 1·22–3·26], p=0·007). Ventriculitis (17 [7%] alteplase vs 31 [12%] saline; RR 0·55 [95% CI 0·31–0·97], p=0·048) and serious adverse events (114 [46%] alteplase vs 151 [60%] saline; RR 0·76 [95% CI 0·64–0·90], p=0·002) were less frequent with alteplase treatment. Symptomatic bleeding (six [2%] in the alteplase group vs five [2%] in the saline group; RR 1·21 [95% CI 0·37–3·91], p=0·771) was similar. Interpretation: In patients with intraventricular haemorrhage and a routine extraventricular drain, irrigation with alteplase did not substantially improve functional outcomes at the mRS 3 cutoff compared with irrigation with saline. Protocol-based use of alteplase with extraventricular drain seems safe. Future investigation is needed to determine whether a greater frequency of complete intraventricular haemorrhage removal via alteplase produces gains in functional status

A New Image Quantitative Method for Diagnosis and Therapeutic Response

abstract: Accurate quantitative information of tumor/lesion volume plays a critical role in diagnosis and treatment assessment. The current clinical practice emphasizes on efficiency, but sacrifices accuracy (bias and precision). In the other hand, many computational algorithms focus on improving the accuracy, but are often time consuming and cumbersome to use. Not to mention that most of them lack validation studies on real clinical data. All of these hinder the translation of these advanced methods from benchside to bedside. In this dissertation, I present a user interactive image application to rapidly extract accurate quantitative information of abnormalities (tumor/lesion) from multi-spectral medical images, such as measuring brain tumor volume from MRI. This is enabled by a GPU level set method, an intelligent algorithm to learn image features from user inputs, and a simple and intuitive graphical user interface with 2D/3D visualization. In addition, a comprehensive workflow is presented to validate image quantitative methods for clinical studies. This application has been evaluated and validated in multiple cases, including quantifying healthy brain white matter volume from MRI and brain lesion volume from CT or MRI. The evaluation studies show that this application has been able to achieve comparable results to the state-of-the-art computer algorithms. More importantly, the retrospective validation study on measuring intracerebral hemorrhage volume from CT scans demonstrates that not only the measurement attributes are superior to the current practice method in terms of bias and precision but also it is achieved without a significant delay in acquisition time. In other words, it could be useful to the clinical trials and clinical practice, especially when intervention and prognostication rely upon accurate baseline lesion volume or upon detecting change in serial lesion volumetric measurements. Obviously, this application is useful to biomedical research areas which desire an accurate quantitative information of anatomies from medical images. In addition, the morphological information is retained also. This is useful to researches which require an accurate delineation of anatomic structures, such as surgery simulation and planning.Dissertation/ThesisDoctoral Dissertation Biomedical Informatics 201

Characterizing and revealing biomarkers on patients with Cerebral Amyloid Angiopathy using artificial intelligence

Dissertação de mestrado em BioinformáticaCerebral Amyloid Angiopathy is a cerebrovascular disorder resulting from the deposition of an amyloidogenic protein in small and medium sized cortical and leptomeningeal vessels. A primary cause of spontaneous intracerebral haemorrhages, it manifests predominantly in the elder population. Although CAA is a common neuropathological finding on itself, it is also known to frequently occur in conjunction with Alzheimer’s disease, being sometimes misdiagnosed. Currently, CAA diagnosis is generally conducted by post-mortem examination or, in live patients by the examination of an evacuated hematoma or brain biopsy samples, which are typically unavailable. Therefore, a reliable and non-invasive method for diagnosing CAA would facilitate the clinical decision making and accelerate the clinical intervention. The main goal of this dissertation is to study the application of Machine Learning (ML) to reveal possible biomarkers to aid the diagnosis and early medical intervention, and better understand the disease. Therefore, three scenarios were tested: Classification of four neurodegenerative diseases with annotation data obtained from visual rating scores, age and gender; Classification of the diseases with radiomic data derived from the patient’s MRI; and a combination of the previous experiments. The results show that the application of Artificial intelligence in the medical field brings advantages to support the physicians in the decision making process and, at some point, make a correct prediction of the disease label. Although the results are satisfactory, there are still improvements to be done. For instance, image segmentation of cerebral lesions or brain regions and additional clinical information of the patients would be of value.Angiopatia Amiloide Cerebral (AAC) é uma doença vascular cerebral resultante da deposição de matéria amiloide. Principal causa de hemorragias cerebral espontâneas, a AAC manifesta se predominantemente na população idosa. Embora a AAC seja uma doença que por si só tem um grande impacto no grupo etário referido, ocorre em simultâneo com inúmeras outras doenças neurodegenerativas, como a doença de Alzheimer. Atualmente, o diagnóstico de AAC realiza-se quer em post-mortem, quer em pacientes vivos. No entanto, o diagnóstico em vida é conseguido por meio de biópsias de tecidos cerebrais, sendo um método invasivo, o que dificulta a intervenção clínica. Deste modo, torna-se imperativa a procura de alternativas fiáveis e não invasivas em vida para auxiliar o diagnóstico da doença e permitir a melhoria da qualidade de vida do paciente. Perante os progressos na área da tecnologia e medicina, esta dissertação propõe o estudo da aplicação de algoritmos de Machine Learning (ML) para revelar possíveis biomarcadores para auxiliar o diagnóstico e permitir uma intervenção precoce. Deste modo, foram testados três cenários distintos: a classificação de quatro doenças neurodegenerativas com dados anotados obtidos a partir de métricas visuais de avaliação da atrofia, idade e sexo; a classificação das doenças com dados gerados a partir de métodos radiómicos; e uma combinação das duas abordagens anteriores. Neste documento apresenta-se e discute-se os resultados obtidos com a aplicação de quatro diferentes algoritmos de ML que visam a deteção automática da doença associada à imagem testada. Adicionalmente, é feita uma análise crítica de quais as características mais relevantes que levaram à tomada de decisão por parte do algoritmo. Os resultados demonstram que através de aplicação de metodologias automáticas é possível o auxílio ao diagnostico médico por especialistas e, no limite, a realização de diagnostico automático com elevada precisão. Finalmente, são apresentadas possíveis alternativas de trabalho futuro para que os resultados possam ser aperfeiçoados, como por exemplo, a segmentação das regiões de interesse, i.e., identificação das lesões, aquando da anotação por especialistas. Mediante a inclusão dessa segmentação, uma vez que será mais especifica, os resultados serão, por sua vez, aprimorados

Automated segmentation of haematoma and perihaematomal oedema in MRI of acute spontaneous intracerebral haemorrhage

BackgroundSpontaneous intracerebral haemorrhage (SICH) is a common condition with high morbidity and mortality. Segmentation of haematoma and perihaematoma oedema on medical images provides quantitative outcome measures for clinical trials and may provide important markers of prognosis in people with SICH.MethodsWe take advantage of improved contrast seen on magnetic resonance (MR) images of patients with acute and early subacute SICH and introduce an automated algorithm for haematoma and oedema segmentation from these images. To our knowledge, there is no previously proposed segmentation technique for SICH that utilises MR images directly. The method is based on shape and intensity analysis for haematoma segmentation and voxel-wise dynamic thresholding of hyper-intensities for oedema segmentation.ResultsUsing Dice scores to measure segmentation overlaps between labellings yielded by the proposed algorithm and five different expert raters on 18 patients, we observe that our technique achieves overlap scores that are very similar to those obtained by pairwise expert rater comparison. A further comparison between the proposed method and a state-of-the-art Deep Learning segmentation on a separate set of 32 manually annotated subjects confirms the proposed method can achieve comparable results with very mild computational burden and in a completely training-free and unsupervised way.ConclusionOur technique can be a computationally light and effective way to automatically delineate haematoma and oedema extent directly from MR images. Thus, with increasing use of MR images clinically after intracerebral haemorrhage this technique has the potential to inform clinical practice in the future

Multi-Method Diagnosis of CT Images for Rapid Detection of Intracranial Hemorrhages Based on Deep and Hybrid Learning

Intracranial hemorrhaging is considered a type of disease that affects the brain and is very dangerous, with high-mortality cases if there is no rapid diagnosis and prompt treatment. CT images are one of the most important methods of diagnosing intracranial hemorrhages. CT images contain huge amounts of information, requiring a lot of experience and taking a long time for proper analysis and diagnosis. Thus, artificial intelligence techniques provide an automatic mechanism for evaluating CT images to make a diagnosis with high accuracy and help radiologists make their diagnostic decisions. In this study, CT images for rapid detection of intracranial hemorrhages are diagnosed by three proposed systems with various methodologies and materials, where each system contains more than one network. The first system is proposed by three pretrained deep learning models, which are GoogLeNet, ResNet-50 and AlexNet. The second proposed system using a hybrid technology consists of two parts: the first part is the GoogLeNet, ResNet-50 and AlexNet models for extracting feature maps, while the second part is the SVM algorithm for classifying feature maps. The third proposed system uses artificial neural networks (ANNs) based on the features of the GoogLeNet, ResNet-50 and AlexNet models, whose dimensions are reduced by a principal component analysis (PCA) algorithm, and then the low-dimensional features are combined with the features of the GLCM and LBP algorithms. All the proposed systems achieved promising results in the diagnosis of CT images for the rapid detection of intracranial hemorrhages. The ANN network based on fusion of the deep feature of AlexNet with the features of GLCM and LBP reached an accuracy of 99.3%, precision of 99.36%, sensitivity of 99.5%, specificity of 99.57% and AUC of 99.84

Automated detection and analysis of fluorescence changes evoked by molecular signalling

Fluorescent dyes and genetically encoded fluorescence indicators (GEFI) are common tools for visualizing concentration changes of specific ions and messenger molecules during intra- as well as intercellular communication. While fluorescent dyes have to be directly loaded into target cells and function only transiently, the expression of GEFIs can be controlled in a cell and time-specific fashion, even allowing long-term analysis in living organisms. Dye and GEFI based fluorescence fluctuations, recorded using advanced imaging technologies, are the foundation for the analysis of physiological molecular signaling. Analyzing the plethora of complex fluorescence signals is a laborious and time-consuming task. An automated analysis of fluorescent signals circumvents user bias and time constraints. However, it requires to overcome several challenges, including correct estimation of fluorescence fluctuations at basal concentrations of messenger molecules, detection and extraction of events themselves, proper segmentation of neighboring events as well as tracking of propagating events. Moreover, event detection algorithms need to be sensitive enough to accurately capture localized and low amplitude events exhibiting a limited spatial extent. This thesis presents three novel algorithms, PBasE, CoRoDe and KalEve, for the automated analysis of fluorescence events, developed to overcome the aforementioned challenges. The algorithms are integrated into a graphical application called MSparkles, specifically designed for the analysis of fluorescence signals, developed in MATLAB. The capabilities of the algorithms are demonstrated by analyzing astroglial Ca2+ events, recorded in anesthetized and awake mice, visualized using genetically encoded Ca2+ indicators (GECIs) GCaMP3 as well as GCaMP5. The results were compared to those obtained by other software packages. In addition, the analysis of neuronal Na+ events recorded in acute brain slices using SBFI-AM serve to indicate the putatively broad application range of the presented algorithms. Finally, due to increasing evidence of the pivotal role of astrocytes in neurodegenerative diseases such as epilepsy, a metric to assess the synchronous occurrence of fluorescence events is introduced. In a proof-of-principle analysis, this metric is used to correlate astroglial Ca2+ events with EEG measurementsFluoreszenzfarbstoffe und genetisch kodierte Fluoreszenzindikatoren (GEFI) sind gängige Werkzeuge zur Visualisierung von Konzentrationsänderungen bestimmter Ionen und Botenmoleküle der intra- sowie interzellulären Kommunikation. Während Fluoreszenzfarbstoffe direkt in die Zielzellen eingebracht werden müssen und nur über einen begrenzten Zeitraum funktionieren, kann die Expression von GEFIs zell- und zeitspezifisch gesteuert werden, was darüber hinaus Langzeitanalysen in lebenden Organismen ermöglicht. Farbstoff- und GEFI-basierte Fluoreszenzfluktuationen, die mit Hilfe moderner bildgebender Verfahren aufgezeichnet werden, bilden die Grundlage für die Analyse physiologischer molekularer Kommunikation. Die Analyse einer großen Zahl komplexer Fluoreszenzsignale ist jedoch eine schwierige und zeitaufwändige Aufgabe. Eine automatisierte Analyse ist dagegen weniger zeitaufwändig und unabhängig von der Voreingenommenheit des Anwenders. Allerdings müssen hierzu mehrere Herausforderungen bewältigt werden. Unter anderem die korrekte Schätzung von Fluoreszenzschwankungen bei Basalkonzentrationen von Botenmolekülen, die Detektion und Extraktion von Signalen selbst, die korrekte Segmentierung benachbarter Signale sowie die Verfolgung sich ausbreitender Signale. Darüber hinaus müssen die Algorithmen zur Signalerkennung empfindlich genug sein, um lokalisierte Signale mit geringer Amplitude sowie begrenzter räumlicher Ausdehnung genau zu erfassen. In dieser Arbeit werden drei neue Algorithmen, PBasE, CoRoDe und KalEve, für die automatische Extraktion und Analyse von Fluoreszenzsignalen vorgestellt, die entwickelt wurden, um die oben genannten Herausforderungen zu bewältigen. Die Algorithmen sind in eine grafische Anwendung namens MSparkles integriert, die speziell für die Analyse von Fluoreszenzsignalen entwickelt und in MATLAB implementiert wurde. Die Fähigkeiten der Algorithmen werden anhand der Analyse astroglialer Ca2+-Signale demonstriert, die in narkotisierten sowie wachen Mäusen aufgezeichnet und mit den genetisch kodierten Ca2+-Indikatoren (GECIs) GCaMP3 und GCaMP5 visualisiert wurden. Erlangte Ergebnisse werden anschließend mit denen anderer Softwarepakete verglichen. Darüber hinaus dient die Analyse neuronaler Na+-Signale, die in akuten Hirnschnitten mit SBFI-AM aufgezeichnet wurden, dazu, den breiten Anwendungsbereich der Algorithmen aufzuzeigen. Zu guter Letzt wird aufgrund der zunehmenden Indizien auf die zentrale Rolle von Astrozyten bei neurodegenerativen Erkrankungen wie Epilepsie eine Metrik zur Bewertung des synchronen Auftretens fluoreszenter Signale eingeführt. In einer Proof-of-Principle-Analyse wird diese Metrik verwendet, um astrogliale Ca2+-Signale mit EEG-Messungen zu korrelieren

2016 Annual Research Symposium Abstract Book

2016 annual volume of abstracts for science research projects conducted by students at Trinity Colleg