Generalizable automated pixel-level structural segmentation of medical and biological data

Over the years, the rapid expansion in imaging techniques and equipments has driven the demand for more automation in handling large medical and biological data sets. A wealth of approaches have been suggested as optimal solutions for their respective imaging types. These solutions span various image resolutions, modalities and contrast (staining) mechanisms. Few approaches generalise well across multiple image types, contrasts or resolution. This thesis proposes an automated pixel-level framework that addresses 2D, 2D+t and 3D structural segmentation in a more generalizable manner, yet has enough adaptability to address a number of specific image modalities, spanning retinal funduscopy, sequential fluorescein angiography and two-photon microscopy. The pixel-level segmentation scheme involves: i ) constructing a phase-invariant orientation field of the local spatial neighbourhood; ii ) combining local feature maps with intensity-based measures in a structural patch context; iii ) using a complex supervised learning process to interpret the combination of all the elements in the patch in order to reach a classification decision. This has the advantage of transferability from retinal blood vessels in 2D to neural structures in 3D. To process the temporal components in non-standard 2D+t retinal angiography sequences, we first introduce a co-registration procedure: at the pairwise level, we combine projective RANSAC with a quadratic homography transformation to map the coordinate systems between any two frames. At the joint level, we construct a hierarchical approach in order for each individual frame to be registered to the global reference intra- and inter- sequence(s). We then take a non-training approach that searches in both the spatial neighbourhood of each pixel and the filter output across varying scales to locate and link microvascular centrelines to (sub-) pixel accuracy. In essence, this \link while extract" piece-wise segmentation approach combines the local phase-invariant orientation field information with additional local phase estimates to obtain a soft classification of the centreline (sub-) pixel locations. Unlike retinal segmentation problems where vasculature is the main focus, 3D neural segmentation requires additional exibility, allowing a variety of structures of anatomical importance yet with different geometric properties to be differentiated both from the background and against other structures. Notably, cellular structures, such as Purkinje cells, neural dendrites and interneurons, all display certain elongation along their medial axes, yet each class has a characteristic shape captured by an orientation field that distinguishes it from other structures. To take this into consideration, we introduce a 5D orientation mapping to capture these orientation properties. This mapping is incorporated into the local feature map description prior to a learning machine. Extensive performance evaluations and validation of each of the techniques presented in this thesis is carried out. For retinal fundus images, we compute Receiver Operating Characteristic (ROC) curves on existing public databases (DRIVE & STARE) to assess and compare our algorithms with other benchmark methods. For 2D+t retinal angiography sequences, we compute the error metrics ("Centreline Error") of our scheme with other benchmark methods. For microscopic cortical data stacks, we present segmentation results on both surrogate data with known ground-truth and experimental rat cerebellar cortex two-photon microscopic tissue stacks.Open Acces

In-silico clinical trials for assessment of intracranial flow diverters

In-silico trials refer to pre-clinical trials performed, entirely or in part, using individualised computer models that simulate some aspect of drug effect, medical device, or clinical intervention. Such virtual trials reduce and optimise animal and clinical trials, and enable exploring a wider range of anatomies and physiologies. In the context of endovascular treatment of intracranial aneurysms, in-silico trials can be used to evaluate the effectiveness of endovascular devices over virtual populations of patients with different aneurysm morphologies and physiologies. However, this requires (i) a virtual endovascular treatment model to evaluate device performance based on a reliable performance indicator, (ii) models that represent intra- and inter-subject variations of a virtual population, and (iii) creation of cost-effective and fully-automatic workflows to enable a large number of simulations at a reasonable computational cost and time. Flow-diverting stents have been proven safe and effective in the treatment of large wide-necked intracranial aneurysms. The presented thesis aims to provide the ingredient models of a workflow for in-silico trials of flow-diverting stents and to enhance the general knowledge of how the ingredient models can be streamlined and accelerated to allow large-scale trials. This work contributed to the following aspects: 1) To understand the key ingredient models of a virtual treatment workflow for evaluation of the flow-diverter performance. 2) To understand the effect of input uncertainty and variability on the workflow outputs, 3) To develop generative statistical models that describe variability in internal carotid artery flow waveforms, and investigate the effect of uncertainties on quantification of aneurysmal wall shear stress, 4) As part of a metric to evaluate success of flow diversion, to develop and validate a thrombosis model to assess FD-induced clot stability, and 5) To understand how a fully-automatic aneurysm flow modelling workflow can be built and how computationally inexpensive models can reduce the computational costs

Assessment of coronary artery stenosis using myocardial contrast echocardiography.

The theoretical advantage of perfusion data over wall motion data for diagnosing coronary artery stenosis relates to the temporal sequence of these phenomena in the ischaemic cascade. Myocardial perfusion evaluation could thus provide earlier information than wall motion assessment, with important clinical consequences. This thesis examines myocardial perfusion assessment using ultrasound and micro-bubble contrast in stable coronary artery stenosis. The first set of experiments were undertaken to establish both a means of infusing Optison (GE Healthcare, UK), and of displaying static frame contrast signal using Power Contrast Imaging (Acuson Sequoia, Siemens Medical Solutions, Mountain View, CA, USA.). Three Optison concentrations, five infusion rates, and five trigger intervals were evaluated. This revealed an appropriate concentration and infusion rate for Optison and identified an ideal trigger interval of one in four cardiac cycles. The second part of this study evaluated Power Contrast Imaging with Optison infusion in stable single or double vessel coronary artery stenosis. Perfusion assessment during Adenosine vasodilator stress was compared with standard wall motion assessment during Dobutamine stress, coronary angiography being the diagnostic standard. Among twenty-eight subjects and eighty-four coronary territories, Power Contrast Imaging had low sensitivity but equivalent specificity compared to wall motion assessment. The third component of this research evaluated micro-bubble preserving real time Coherent Contrast Imaging (Acuson Sequoia , Siemens Medical Solutions) alongside Optison infusion in stable single or double vessel coronary stenosis. Thirty-eight subjects and one hundred and fourteen coronary arteries were evaluated. Each subject underwent Dobutamine stress, during which standard wall motion, contrast wall motion, and contrast perfusion imaging were all assessed, the diagnostic standard being coronary angiography. This demonstrated that contrast wall motion evaluation is accurate and that combined contrast wall motion and perfusion imaging is at least equivalent to standard wall motion imaging alone for detecting underlying coronary stenosis

Proceedings Virtual Imaging Trials in Medicine 2024

This submission comprises the proceedings of the 1st Virtual Imaging Trials in Medicine conference, organized by Duke University on April 22-24, 2024. The listed authors serve as the program directors for this conference. The VITM conference is a pioneering summit uniting experts from academia, industry and government in the fields of medical imaging and therapy to explore the transformative potential of in silico virtual trials and digital twins in revolutionizing healthcare. The proceedings are categorized by the respective days of the conference: Monday presentations, Tuesday presentations, Wednesday presentations, followed by the abstracts for the posters presented on Monday and Tuesday

Image Registration Workshop Proceedings

Automatic image registration has often been considered as a preliminary step for higher-level processing, such as object recognition or data fusion. But with the unprecedented amounts of data which are being and will continue to be generated by newly developed sensors, the very topic of automatic image registration has become and important research topic. This workshop presents a collection of very high quality work which has been grouped in four main areas: (1) theoretical aspects of image registration; (2) applications to satellite imagery; (3) applications to medical imagery; and (4) image registration for computer vision research

Artificial Intelligence with Light Supervision: Application to Neuroimaging

Recent developments in artificial intelligence research have resulted in tremendous success in computer vision, natural language processing and medical imaging tasks, often reaching human or superhuman performance. In this thesis, I further developed artificial intelligence methods based on convolutional neural networks with a special focus on the automated analysis of brain magnetic resonance imaging scans (MRI). I showed that efficient artificial intelligence systems can be created using only minimal supervision, by reducing the quantity and quality of annotations used for training. I applied those methods to the automated assessment of the burden of enlarged perivascular spaces, brain structural changes that may be related to dementia, stroke, mult

Use of advanced echocardiography imaging techniques in the critically ill

Background: Critical care echocardiography has become standard of care in the ICU. New technologies have been developed and have shown potential clinical utility to elucidate myocardial dysfunction not seen with conventional imaging. We sought to determine the feasibility and potential clinical benefit of these techniques in common situations seen in the ICU. Hypothesis: Advanced echo techniques would be feasible in the majority of critically ill patients and have prognostic significance, clinical utility and diagnose cardiac abnormalities, potentially in a more sensitive manner than conventional techniques. Results: (a) Speckle tracking echocardiography (STE) Left ventricle and RV analysis with STE was feasibly in ~80% of patients. More dysfunction was found using STE vs conventional analysis. RV dysfunction assessed by STE held significant prognostic relevance in those with septic shock and highlighted subtle dysfunction induced by mechanical ventilation, both in animal and human studies. (b) 3D transthoracic echocardiography (3D TTE) Despite finding 3D TTE feasible in mechanically ventilated ICU patients (LV 72% and RV 55%), it lacked necessary low variability and high precision vs standard measures. (c) Myocardial contrast perfusion echocardiography (MCPE) Assessing acute coronary artery occlusion in the ICU patient is challenging. Troponin elevation, acute ECG changes, regional wall motion analysis on echo and overall clinical acumen often lack diagnostic capabilities. MCPE was found to be feasible in the critically ill and had better association predicting acute coronary artery occlusion vs clinical acumen alone. Conclusions: STE, 3D TTE and MCPE are feasible in the majority of ICU patients. STE may show dysfunction not recognised by conventional imaging. 3D TTE for volumetric analysis is likely not suitable for clinical use at this stage. MCPE may help guide interventions in acute coronary artery occlusion