Fast detection of venous air embolism in Doppler heart sound using the wavelet transform

The introduction of air bubbles into the systemic circulation can result in significant morbidity. Real-time monitoring of continuous heart sound in patients detected by precordial Doppler ultrasound is, thus, vital for early detection of venous air embolism (VAE) during surgery. In this study, the multiscale feature of wavelet transforms (WT's) is exploited to examine the embolic Doppler heart sound (DHS) during intravenous air injections in dogs. As both humans and dogs share similar physiological conditions, the authors' methods and results for dogs are expected to be applicable to humans. The WT of DHS at scale 2 j(j=1,2) selectively magnified the power of embolic, but not the normal, heart sound. Statistically, the enhanced embolic power was found to be sensitive (P<0.01 at 0.01 ml of injected air) and correlated significantly (P<0.0005, τ=0.83) with the volume of injected air from 0.01 to 0.10 ml. A fast detection algorithm of O(N) complexity with unit complexity constant for VAE was developed (processing speed=8 ms per heartbeat), which confirmed the feasibility of real-time processing for both humans and dogs.published_or_final_versio

Automated Axial Right Ventricle to Left Ventricle Diameter Ratio Computation in Computed Tomography Pulmonary Angiography

Background and Purpose Right Ventricular to Left Ventricular (RV/LV) diameter ratio has been shown to be a prognostic biomarker for patients suffering from acute Pulmonary Embolism (PE). While Computed Tomography Pulmonary Angiography (CTPA) images used to confirm a clinical suspicion of PE do include information of the heart, a numerical RV/LV diameter ratio is not universally reported, likely because of lack in training, inter-reader variability in the measurements, and additional effort by the radiologist. This study designs and validates a completely automated Computer Aided Detection (CAD) system to compute the axial RV/LV diameter ratio from CTPA images so that the RV/LV diameter ratio can be a more objective metric that is consistently reported in patients for whom CTPA diagnoses PE. Materials and Methods The CAD system was designed specifically for RV/LV measurements. The system was tested in 198 consecutive CTPA patients with acute PE. Its accuracy was evaluated using reference standard RV/LV radiologist measurements and its prognostic value was established for 30-day PE-specific mortality and a composite outcome of 30-day PE-specific mortality or the need for intensive therapies. The study was Institutional Review Board (IRB) approved and HIPAA compliant. Results The CAD system analyzed correctly 92.4% (183/198) of CTPA studies. The mean difference between automated and manually computed axial RV/LV ratios was 0.03±0.22. The correlation between the RV/LV diameter ratio obtained by the CAD system and that obtained by the radiologist was high (r=0.81). Compared to the radiologist, the CAD system equally achieved high accuracy for the composite outcome, with areas under the receiver operating characteristic curves of 0.75 vs. 0.78. Similar results were found for 30-days PE-specific mortality, with areas under the curve of 0.72 vs. 0.75. Conclusions An automated CAD system for determining the CT derived RV/LV diameter ratio in patients with acute PE has high accuracy when compared to manual measurements and similar prognostic significance for two clinical outcomes.Madrid-MIT M+Vision Consortiu

Automated iterative neutrosophic lung segmentation for image analysis in thoracic computed tomography

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134994/1/mp2679.pd

Automatic segmentation of the left ventricle cavity and myocardium in MRI data

A novel approach for the automatic segmentation has been developed to extract the epi-cardium and endo-cardium boundaries of the left ventricle (lv) of the heart. The developed segmentation scheme takes multi-slice and multi-phase magnetic resonance (MR) images of the heart, transversing the short-axis length from the base to the apex. Each image is taken at one instance in the heart's phase. The images are segmented using a diffusion-based filter followed by an unsupervised clustering technique and the resulting labels are checked to locate the (lv) cavity. From cardiac anatomy, the closest pool of blood to the lv cavity is the right ventricle cavity. The wall between these two blood-pools (interventricular septum) is measured to give an approximate thickness for the myocardium. This value is used when a radial search is performed on a gradient image to find appropriate robust segments of the epi-cardium boundary. The robust edge segments are then joined using a normal spline curve. Experimental results are presented with very encouraging qualitative and quantitative results and a comparison is made against the state-of-the art level-sets method

A two step deep learning workflow for pumonary embolism segmentation and classification

Orientador: Lucas Ferrari de OliveiraDissertação (mestrado) - Universidade Federal do Paraná, Setor de Ciências Exatas, Programa de Pós-Graduação em Informática. Defesa : Curitiba, 14/12/2021Inclui referências: p. 57-60Área de concentração: Ciência da ComputaçãoResumo: Embolia pulmonar está entre as principais causas de morte no mundo todo, de acordo com o Datasus 22% dos pacientes hospitalizados por embolia pulmonar acabam falecendo, trata-se de um trombo alojado em alguma região da vascularização arterial pulmonar. Para estabelecer o tratamento adequado e reduzir a mortalidade associada é necessário um diagnóstico rápido por parte da equipe médica, atualmente a forma de diagnóstico mais utilizado é a analise de imagens de tomografia computadorizada, devido a vários fatores como a sua velocidade de aquisição, alta disponibilidade dos equipamentos que fazem a sua captura e uma alta acurácia no diagnóstico. Um exame de tomografia computadorizada é composto de centenas de imagens que requerem a atenção do radiologista, pelo número alto de dados produzidos a análise de tais exames pode ser cansativa e levar a erros de diagnóstico devido a fadiga, ainda hoje a embolia pulmonar está entre as doenças onde se há mais erros diagnósticos. Nos últimos anos alguns sistemas computacionais de auxílio ao diagnóstico foram desenvolvidos para auxiliar radiologistas na detecção de trombos, tais sistemas têm se tornado de grande ajuda para um diagnóstico mais eficaz. Deep learning tem sido um dos tópicos mais comentados na área da visão computacional ultimamente, especialmente na área do processamento de imagens médicas, mais especificamente em aplicações de detecção e reconhecimento de imagens. Isso também se aplica no auxílio ao diagnóstico de embolia pulmonar, alguns trabalhos têm atingido resultados do estado da arte utilizando modelos complexos de redes neurais, que são capazes de identificar trombos dentro das imagens geradas pela tomografia, removendo outros ruídos que podem ser vistos como um falso positivo. O objetivo deste trabalho é desenvolver uma aplicação de deep learning capaz de encontrar tromboembolismos pulmonares em imagens de tomografias computadorizadas, a robustez do modelo permitirá que detecte trombos em exames de diferente origens. Se bem sucedido, o algoritmo produzido neste trabalho será capaz de auxiliar radiologistas em um diagnóstico rápido com uma alta probabilidade de acerto. Alguns testes preliminares já mostram que modelos de deep learning são capazes de discriminar embolias pulmonares, em uma base de dados pública contendo imagens de tomografia computadorizada de pulmão a rede foi capaz de encontrar vários trombos. Com um total de 35 exames, 28 foram usados para treinar o modelo e validar seus resultados, ajustando seus hiperparâmetros de acordo com os resultados, as outras 7 imagens foram utilizadas como teste, avaliado como o sistema se comporta quando recebe dados reais, atingindo um Dice score de 0.81 e uma acurácia de 84%, apesar de já apresentar bons resultados a modelo ainda possui espaço para melhores, pois ainda há diversos métodos de otimização que costumam melhorar os resultados das arquiteturas.Abstract: Pulmonary embolism is one of the leading causes of death all over the world, according to Datasus the mortality rate of patients hospitalized due to pulmonary embolism is 22%. To break the clot and save the patient a fast diagnosis is required, that is the reason why computer tomography is used as a means to detect embolisms. A computed tomography exam is composed of hundreds of images that require an analysis from a radiologist, due to the high number of images this process can be tiresome and can lead to errors due to fatigue, pulmonary embolism remains one of the frequent misdiagnosis due to this fact. Over the years some computed aided systems had been developed aiming to help radiologists to see some missed clot, those systems had proven to be of great aid to an even faster diagnosis. Deep learning models have been increased significantly in many computer vision problems, especially in medical imaging, in image detection and recognition. This is also true in the classification of pulmonary embolisms, some works achieve a state of the art results by applying complex neural network models that can identify a clot from a whole tomography exam and remove any potential false positive found. The purpose of this work is to develop a deep learning application that is capable of discriminate pulmonary embolisms from a whole computed tomography volume, due to the use of deep learning a robust model can be developed that can generalize the process of embolism detection in different sources of data. If successful, this work will be able to aid radiologists in a fast diagnosis of pulmonary embolisms with a high discrimination probability. Some preliminary tests show that a deep learning architecture can discriminate pulmonary embolisms, a public dataset was used for validation of this architecture and can find several clots. With a total of 35 exams, 28 were used for training the model and validating its results, tweaking the models' hyperparameters with the results, the last 7 exams were used for testing the model, simulating how it should behave in a receiving unknown data, it achieves a Dice score of 0.81 and an accuracy of 84%, even if it got a relatively good result, it got plenty of room for improvement still, since many known improvement methods can still be applied in the architecture

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

Wavelet Estimators in Nonparametric Regression: A Comparative Simulation Study

Wavelet analysis has been found to be a powerful tool for the nonparametric estimation of spatially-variable objects. We discuss in detail wavelet methods in nonparametric regression, where the data are modelled as observations of a signal contaminated with additive Gaussian noise, and provide an extensive review of the vast literature of wavelet shrinkage and wavelet thresholding estimators developed to denoise such data. These estimators arise from a wide range of classical and empirical Bayes methods treating either individual or blocks of wavelet coefficients. We compare various estimators in an extensive simulation study on a variety of sample sizes, test functions, signal-to-noise ratios and wavelet filters. Because there is no single criterion that can adequately summarise the behaviour of an estimator, we use various criteria to measure performance in finite sample situations. Insight into the performance of these estimators is obtained from graphical outputs and numerical tables. In order to provide some hints of how these estimators should be used to analyse real data sets, a detailed practical step-by-step illustration of a wavelet denoising analysis on electrical consumption is provided. Matlab codes are provided so that all figures and tables in this paper can be reproduced