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Left ventricular geometry during unloading and the end-systolic pressure volume relationship: Measurement with a modified real-time MRI-based method in normal sheep
Get PDFThe left ventricular (LV) end-systolic (ES) pressure volume relationship (ESPVR) is the cornerstone of systolic LV function analysis. We describe a 2D real-time (RT) MRI-based method (RTPVR) with separate software tools for 1) semi-automatic level set-based shape prior method (LSSPM) of the LV, 2) generation of synchronized pressure area loops and 3) calculation of the ESPVR. We used the RTPVR method to measure ventricular geometry, ES pressure area relationship (ESPAR) and ESPVR during vena cava occlusion (VCO) in normal sheep. 14 adult sheep were anesthetized and underwent measurement of LV systolic function. Ten of the 14 sheep underwent RTMRI and eight of the 14 underwent measurement with conductance catheter; 4 had both RTMRI and conductance measurements. 2D cross sectional RTMRI were performed at apex, mid-ventricle and base levels during separate VCOs. The Dice similarity coefficient was used to compare LSSPM and manual image segmentation and thus determine LSSPM accuracy. LV cross-sectional area, major and minor axis length, axis ratio, major axis orientation angle and ESPAR were measured at each LV level. ESPVR was calculated with a trapezoidal rule. The Dice similarity coefficient between LSSPM and manual segmentation by two readers was 87.31±2.51% and 88.13±3.43%. All cross sections became more elliptical during VCO. The major axis orientation shifted during VCO but remained in the septo-lateral direction. LV chamber obliteration at the apical level occurred during VCO in 7 of 10 sheep that underwent RTMRI. ESPAR was non-linear at all levels. Finally, ESPVR was non-linear because of apical collapse. ESPVR measured by conductance catheter (EES,Index = 2.23±0.66 mmHg/ml/m2) and RT (EES,Index = 2.31±0.31 mmHg/ml/m2) was not significantly different. LSSPM segmentation of 2D RT MRI images is accurate and allows calculation of LV geometry, ESPAR and ESPVR during VCO. In the future, RTPVR will facilitate determination of regional systolic material parameters underlying ESPVR
My Data Body / Your Data Body
No full textSSHRC IDG awarded 2020: Building on research on the use of the medically scanned body to create artistic prints, sculptures and installations, I will work with radiologists and computer scientists to create two art installations, My Data Body, which focuses on representing by own medical and personal data, and Your Data Body, which will focus on representing the data of others. My Data Body will provide viewers with the experience of exploring a full body 3D and 4D magnetic resonance scan dataset in virtual reality (VR).Your Data Body will be made with ‘donated’ datasets of body parts made visible and manipulable in augmented reality (AR), where the virtual overlays on reality. Embedded in the body parts will be a personal story (also 'donated'), that will be audible as long as the scanned body part is being interacted with. Both works will be part of installations that include prints and sculptures generated from the data. My Data Body/Your Data Body will raise many ethical and theoretical questions as well as significant technicaland aesthetic challenges such as; what are the ethics of working artistically my own data and the data of others? What new aesthetic considerations are raised when viewers are able to enter into, pick up and manipulate 3D and 4D scanned bodies? What do these new visions of the digitized body and identity reveal about the human in the digital age? How do they challenge or support existing theoretical frameworks surrounding posthumanism, poststructuralism and technofeminism
Quantum Annealing for Machine Learning: Exploring NISQ Optimization for Image Processing
No full textQuantum computing has a lot of potential for applications of artificial intelligence, even during the current Noisy Intermediate-Scale Quantum (NISQ) era. One important application in image processing is image denoising. Existing methods use either a simple quantum method on binary images or various forms of hybrid quantumclassical methods. In this work, we extend a fully quantum denoising method based on random Markov fields for non-binary images and demonstrate its effectiveness. Our results show that solving such problems with full-depth images using NISQ anneals remains computationally challenging, with limited accuracy due to hardware constraints and noise.
Quantum anneals are known for their ability to efficiently approximate solutions, leveraging unique quantum phenomena such as quantum tunneling to navigate complex optimization landscapes. While these devices have been employed in various NP-hard problems that are central to artificial intelligence, the extent to which quantum tunneling contributes to performance enhancements is still unclear. To address this, we perform an experimental analysis examining the relationship between the complexity of the optimization energy landscape and the performance of quantum annealing. Our findings provide insights into the capabilities and limitations of current quantum anneals for solving complex optimization problems
Optimized U-Net for Left Ventricle Segmentation
Get PDFThe left ventricle segmentation is an important medical imaging task necessary to measure a patient's heart pumping efficiency. Recently, convolutional neural networks (CNN) have shown great potential in achieving state-of-the-art segmentation for such applications. However, most of the research is focusing on building complicated variations of these networks with modest changes to its performance. There is little to no insights on how these CNNs work and most of them are unfortunately treated the neural network as a black box. In this thesis, the famous U-Net architecture is used to segment the left ventricle from cardiac magnetic resonance (MR) images because of its simplicity and ability to analyze images at multiple scales. Posterior analysis of the network functionality demonstrates that by replacing the first set of layers of the U-Net with fixed filters, there is little change in performance compared to its fully connected version. This optimization was achieved by performing a Fourier analysis and visualization of the convolution layers after the completion of the network training phase. This analysis allows us to discover that some early layers approximate uniform filters which can then be replaced by fixed uniform kernel weights. Furthermore, in a separate experiment by removing the middle layers of the U-Net one can reduce the number of U-Net parameters from 31 million to 0.5 million to achieve faster prediction time without compromising the performance. Experimental results and analysis are presented
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
AI Assisted Mole Detection for Online Dermatology Triage in Telemedicine Settings
Get PDFSkin moles are one of the most commonly occurring dermatological conditions prevalent nowadays. Early identification and diagnosis of moles are absolutely crucial since they often turn out to be precursors to serious conditions such as melanoma, a dangerous type of skin cancer. Therefore, to ensure an efficient treatment of cases based on their severity, they need to be assessed systematically. In this thesis, we present an artificial intelligence (AI )enabled triage tool to identify moles from images uploaded by patients to a teledermatology platform. The proposed approach employs NesT, one of the latest state-of-the-art transformer-based network for classification. Our system acts as a filter by sending a warning flag if a mole is detected. This can be used to help dermatologists set up consultation appointments in a physical setting by giving priority if the patient has a mole on their image.
A comparative study of the prediction performance of the different neural network models has been provided for different performance metrics of interest. The results presented in this thesis have been obtained from two sets of data, consisting of more than 26,000 clinical pictures with combined different dermatological conditions. Multiple experiments using different models yielded a macro-average recall value as high as 0.955, along with overall accuracy and macro-average precision values of 0.962 and 0.958, respectively
A Diffeomorphic 3D-to-3D Registration Algorithm for the Segmentation of the Left Ventricle in Ultrasound Sequences
Get PDFHeart disease is the second leading cause of death in Canada, where it affects the lives of over two million people. One modality used to detect and diagnose heart disease and other abnormalities is echocardiography or ultrasound imaging of the heart. Ultrasound imaging, compared to other modalities has several advantages; it is non-ionizing, portable, and cost-effective, and provides good spatial and temporal resolution. It is crucial that the left ventricle must be analyzed in the case of cardiac diseases. Metrics derived from analysis of the left ventricle provide an indication to the clinician about the performance of the heart. However, the current clinical software and methods available in the literature to analyze the left ventricle suffer from several potential drawbacks. Geometrical assumptions may be made about the chamber, or a large amount of manual interaction is required. In the case of supervised deep learning neural networks, a training dataset may be required, which may be difficult to obtain.
Therefore the goal of this thesis was to focus on the development of semi-automated methods to delineate the endocardium of the left ventricle based on registration. The methods developed do not require the use of training data, geometrical assumptions, or prior knowledge about the image characteristics. The thesis focuses mainly on the application to ultrasound sequences, with additional testing on MR sequences. In particular, a semi-automated method has been developed with the use of a diffeomorphic registration algorithm to delineate the endocardial borders at end-diastole and end-systole. This method was expanded to provide a segmentation over the full temporal sequence of ultrasound images. Lastly, a 3D-to-3D diffeomorphic registration method was developed for segmentation, where the algorithm was able to capture the full dynamics of the motion of the left ventricle over the cardiac cycle.
We have compared the proposed methods to other common registration packages in terms of standard distance and clinical metrics. The results demonstrate the benefit of using a diffeomorphic registration method for the segmentation of the left ventricle
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