Automatic segmentation of hemorrhagic transformation on follow-up non-contrast CT after acute ischemic stroke

BackgroundHemorrhagic transformation (HT) following reperfusion therapies is a serious complication for patients with acute ischemic stroke. Segmentation and quantification of hemorrhage provides critical insights into patients’ condition and aids in prognosis. This study aims to automatically segment hemorrhagic regions on follow-up non-contrast head CT (NCCT) for stroke patients treated with endovascular thrombectomy (EVT).MethodsPatient data were collected from 10 stroke centers across two countries. We propose a semi-automated approach with adaptive thresholding methods, eliminating the need for extensive training data and reducing computational demands. We used Dice Similarity Coefficient (DSC) and Lin’s Concordance Correlation Coefficient (Lin’s CCC) to evaluate the performance of the algorithm.ResultsA total of 51 patients were included, with 28 Type 2 hemorrhagic infarction (HI2) cases and 23 parenchymal hematoma (PH) cases. The algorithm achieved a mean DSC of 0.66 ± 0.17. Notably, performance was superior for PH cases (mean DSC of 0.73 ± 0.14) compared to HI2 cases (mean DSC of 0.61 ± 0.18). Lin’s CCC was 0.88 (95% CI 0.79–0.93), indicating a strong agreement between the algorithm’s results and the ground truth. In addition, the algorithm demonstrated excellent processing time, with an average of 2.7 s for each patient case.ConclusionTo our knowledge, this is the first study to perform automated segmentation of post-treatment hemorrhage for acute stroke patients and evaluate the performance based on the radiological severity of HT. This rapid and effective tool has the potential to assist with predicting prognosis in stroke patients with HT after EVT

Neural Mechanisms Underlying Hierarchical Speech-in-Noise Processing

One of the most commonly reported complaints related to hearing is difficulty understanding speech-in-noise (SIN). Numerous individuals struggle to effectively communicate in adverse listening conditions, even those with normal hearing. These difficulties are exacerbated due to age and hearing-related deficits such as hearing loss and auditory processing disorders. Despite the high prevalence of SIN deficits in individuals across the lifespan, the neural mechanisms underlying successful speech comprehension in noise are not well understood. Communication in noise is an incredibly complex process that requires efficient processing throughout the entire auditory pathway as well as contributions from higher-order cognitive processes including working memory, inhibition, and attention. In a series of studies using electrophysiologic (EEG) and behavioral measures, this dissertation evaluated the neural correlates of SIN perception across subcortical and cortical levels of the auditory system to identify how top-down and bottom-up influences aid SIN understanding. The first study examined the effects of hearing loss on SIN processing in older adults at the cortical level using frequency-specific neural oscillations (i.e., brain rhythms) and functional connectivity (i.e., directed neural transmission). We found that low-frequency alpha and beta oscillations within and between prefrontal and auditory cortices reflect the ability to flexibly allocate neural resources and recruit top-down predictions to compensate for hearing-related declines and facilitate efficient SIN perception. The second study, in younger adults, investigated the role of attention in SIN processing and how it interacts with early sensory encoding. Hierarchical processing in brainstem and cortex was assessed by simultaneously recording frequency-following responses (FFRs) and event-related potentials (ERPs) at the source level. We found that attention modulates SIN processing at both subcortical and cortical levels and strengthens bidirectional neural signaling within the central auditory pathway. A relative disengagement of corticofugal transmission was observed in noise but only for passive listening suggesting attention aids SIN perception by maintaining top-down reinforcement of acoustic feature encoding within the primary auditory pathways. Taken together, these results indicate that the neural networks engaged during SIN perception depend on a complex interplay between bottom-up and top-down factors including signal clarity, listeners hearing status, and attentional deployment

A robust framework for medical image segmentation through adaptable class-specific representation

Medical image segmentation is an increasingly important component in virtual pathology, diagnostic imaging and computer-assisted surgery. Better hardware for image acquisition and a variety of advanced visualisation methods have paved the way for the development of computer based tools for medical image analysis and interpretation. The routine use of medical imaging scans of multiple modalities has been growing over the last decades and data sets such as the Visible Human Project have introduced a new modality in the form of colour cryo section data. These developments have given rise to an increasing need for better automatic and semiautomatic segmentation methods. The work presented in this thesis concerns the development of a new framework for robust semi-automatic segmentation of medical imaging data of multiple modalities. Following the specification of a set of conceptual and technical requirements, the framework known as ACSR (Adaptable Class-Specific Representation) is developed in the first case for 2D colour cryo section segmentation. This is achieved through the development of a novel algorithm for adaptable class-specific sampling of point neighbourhoods, known as the PGA (Path Growing Algorithm), combined with Learning Vector Quantization. The framework is extended to accommodate 3D volume segmentation of cryo section data and subsequently segmentation of single and multi-channel greyscale MRl data. For the latter the issues of inhomogeneity and noise are specifically addressed. Evaluation is based on comparison with previously published results on standard simulated and real data sets, using visual presentation, ground truth comparison and human observer experiments. ACSR provides the user with a simple and intuitive visual initialisation process followed by a fully automatic segmentation. Results on both cryo section and MRI data compare favourably to existing methods, demonstrating robustness both to common artefacts and multiple user initialisations. Further developments into specific clinical applications are discussed in the future work section

Combining global and local information for the segmentation of MR images of the brain

Magnetic resonance imaging can provide high resolution volumetric images of the brain with exceptional soft tissue contrast. These factors allow the complex structure of the brain to be clearly visualised. This has lead to the development of quantitative methods to analyse neuroanatomical structures. In turn, this has promoted the use of computational methods to automate and improve these techniques. This thesis investigates methods to accurately segment MRI images of the brain. The use of global and local image information is considered, where global information includes image intensity distributions, means and variances and local information is based on the relationship between spatially neighbouring voxels. Methods are explored that aim to improve the classification and segmentation of MR images of the brain by combining these elements. Some common artefacts exist in MR brain images that can be seriously detrimental to image analysis methods. Methods to correct for these artifacts are assessed by exploring their effect, first with some well established classification methods and then with methods that combine global information with local information in the form of a Markov random field model. Another characteristic of MR images is the partial volume effect that occurs where signals from different tissues become mixed over the finite volume of a voxel. This effect is demonstrated and quantified using a simulation. Analysis methods that address these issues are tested on simulated and real MR images. They are also applied to study the structure of the temporal lobes in a group of patients with temporal lobe epilepsy. The results emphasise the benefits and limitations of applying these methods to a problem of this nature. The work in this thesis demonstrates the advantages of using global and local information together in the segmentation of MR brain images and proposes a generalised framework that allows this information to be combined in a flexible way