Deep Learning for Medical Image Segmentation using Prior Knowledge and Topology

Image segmentation refers to the division of a digital image into distinct segments or groups of pixels/voxels. However, most of the existing deep learning approaches lack the utilization of prior knowledge, such as shape information, which could improve segmentation accuracy. In addition, conventional image segmentation frequently falls short in preserving intricate spatial details, motivating the innovation of strategies for multi-scaled feature integration. Furthermore, traditional image segmentation methods primarily concentrate on pixel-level or region-level analysis. However, given the inherent morphological similarities among various image objects, the significance of topology information surpasses that of pixel-level data in the realm of medical image semantic segmentation, and the incorporation of topology information for image segmentation is important. The first aim of this dissertation is to incorporate shape priors into medical image segmentation. A shape-prior-V-Net (SP-V-Net) is proposed, which contains a shape transformation module to refine the segmentation results according to the shape prior. SP-V-Net has been applied to lung segmentation and proximal femur segmentation. The second aim aims to improve image segmentation by leveraging hierarchical features. Two approaches are proposed: the feature pyramid U-Net++ (FP-U-Net++), which dynamically aggregates the feature pyramid in the decoder of U-Net ++, and the multi-input multi-scale U-Net (MIMS U-Net), which integrates the features in the encoder of the U-Net. The third aim explores topology-based image semantic segmentation using graph neural networks. Three graph-matching networks have been developed, including association graph-based, edge attention graph matching, and hyper-association graph matching networks. The proposed graph-matching networks convert the graph-matching problems into a vertex classification problem using an association graph, where the positive vertex indicates the nodes from two individual graphs are matched. These models were applied to coronary artery semantic labeling on invasive coronary angiograms. Moreover, this study presents a pioneering approach for topology-based image semantic labeling using graph matching. The successful completion of these aims contributes technically accurate and clinically applicable algorithms and techniques for medical image segmentation. The outcomes of this dissertation provide valuable tools for the medical imaging and computer vision communities, advancing the field and improving patient care through accurate and efficient medical image segmentation

Applications of Artificial Intelligence in Healthcare

Now in these days, artificial intelligence (AI) is playing a major role in healthcare. It has many applications in diagnosis, robotic surgeries, and research, powered by the growing availability of healthcare facts and brisk improvement of analytical techniques. AI is launched in such a way that it has similar knowledge as a human but is more efficient. A robot has the same expertise as a surgeon; even if it takes a longer time for surgery, its sutures, precision, and uniformity are far better than the surgeon, leading to fewer chances of failure. To make all these things possible, AI needs some sets of algorithms. In Artificial Intelligence, there are two key categories: machine learning (ML) and natural language processing (NPL), both of which are necessary to achieve practically any aim in healthcare. The goal of this study is to keep track of current advancements in science, understand technological availability, recognize the enormous power of AI in healthcare, and encourage scientists to use AI in their related fields of research. Discoveries and advancements will continue to push the AI frontier and expand the scope of its applications, with rapid developments expected in the future

Aerospace Medicine and Biology: A continuing bibliography with indexes (supplement 153)

This bibliography lists 175 reports, articles, and other documents introduced into the NASA scientific and technical information system in March 1976

Imaging of vascular abnormalities in ocular surface disease

The vascular system of the ocular surface plays a central role in infectious, autoimmune, inflammatory, traumatic and neoplastic diseases. The development, application, and mon-itoring of treatments for vascular abnormalities depends on the in vivo analysis of the oc-ular surface vasculature. Until recently, ocular surface vascular imaging was confined to biomicroscopic and color photographic assessment, both limited by poor reproducibility and the inability to image lymphatic vasculature in vivo . The evolvement and clinical im-plementation of innovative imaging modalities including confocal microscopy, intravenous, and optical coherence tomography-based angiography now allows standardized quantita-tive and functional vascular assessment with potential applicability to automated analysis algorithms and diagnostics. (c) 2021 Elsevier Inc. All rights reserved.

Malarial retinopathy and neurovascular injury in paediatric cerebral malaria

Background Diseases of the brain are difficult to study because this organ is relatively inaccessible. Only one part of the central nervous system is available to direct, non-invasive observation – the retina. The concept of the retina as a window to the brain has created much interest in the retina as a source of potential markers of brain disease. Paediatric cerebral malaria is a severe neurological complication of infection with the parasite Plasmodium falciparum, which is responsible for death and disability in a significant number of children in sub-Saharan Africa. As with many neurological diseases, the precise mechanisms by which this infection causes damage to the brain remain unclear, and this hampers efforts to develop effective treatments. It may be that studying the retina in paediatric cerebral malaria could both illuminate pathogenesis specific to this disease, and also provide an illustration of how to approach retinal biomarkers in a new, and potentially more effective way. Methods I approached the aim of developing retinal features as markers of brain disease in paediatric cerebral malaria via several objectives. I made use of an existing clinical study to collect new retinal data from ophthalmoscopic examinations and fundus fluorescein angiograms from patients over three successive malaria seasons in Malawi, and added these to historical data obtained previously at the same site. I devised a new method for grading retinal images. I reviewed the biological plausibility of associations between retina and brain in cerebral malaria, and then considered analytical methods to interpret my retinal data effectively. Finally I estimated associations between retinal features, outcomes, and a radiological measure of brain swelling using combinations of regression models. Results My review of retinal and cerebral histopathology, vascular anatomy and physiology indicated that certain retinal and brain regions may be similarly prone to damage from sequestration as a result of interactions between aberrant rheology and microvascular geometry, such as branching patterns and arteriole to venule ratios. My review of evaluations of analogy and surrogacy suggested that biological similarities between retina and brain could be used to justify statistical evaluation of the amount of information the subject and object of the inference share about a common outcome, as used to assess surrogate end points for clinical trials. This kind of approach is able to address questions about whether a particular retinal feature is effectively equivalent to an analogous disease manifestation in the brain. I report analyses on three overlapping groups of subjects, all of whom had retinopathy positive cerebral malaria: children with admission ophthalmoscopy (n=817), children with admission fluorescein angiography (n=260), and children with admission angiography and MRI of the brain (n=134). Several retinal features are associated with death and longer time to recover consciousness in paediatric cerebral malaria. Broadly speaking, these features appear to reflect two processes: neurovascular sequestration (e.g. orange vessel discolouration and death), and neurovascular leakage (e.g. >5 sites of punctate leak and death). Respective adjusted odds ratios and 95% confidence intervals for these particular associations are: 2.88 (1.64-5.05); and 6.90 (1.52-31.3). Other related processes may also be important, such as ischaemia, which can be extensive. Associations between retina and brain are less clear, in part because of selection bias in the samples. Conclusions Neurovascular leak is important in fatal paediatric cerebral malaria, suggesting that fatal brain swelling may occur primarily as a result of vasogenic oedema. Other processes are also likely to be involved, particularly neurovascular sequestration, which is visible on retinal imaging as orange vessels or intravascular filling defects. Sequestration may plausibly cause leak through direct damage to tight junctions and by increasing transmural pressure secondary to venous congestion. Several types of retinal leakage are seen and some of these may represent re-perfusion rather than acute injury. Future work to investigate temporal changes in retinal signs may find clearer associations with radiological and clinical outcomes. The steps taken to evaluate retinal markers in cerebral malaria illustrate a more rigorous approach to retinal biomarkers in general, which can be applied to other neurological disease

Machine learned boundary definitions for an expert's tracing assistant in image processing

Department Head: Anton Willem Bohm.Includes bibliographical references (pages 178-184).Most image processing work addressing boundary definition tasks embeds the assumption that an edge in an image corresponds to the boundary of interest in the world. In straightforward imagery this is true, however it is not always the case. There are images in which edges are indistinct or obscure, and these images can only be segmented by a human expert. The work in this dissertation addresses the range of imagery between the two extremes of those straightforward images and those requiring human guidance to appropriately segment. By freeing systems of a priori edge definitions and building in a mechanism to learn the boundary definitions needed, systems can do better and be more broadly applicable. This dissertation presents the construction of such a boundary-learning system and demonstrates the validity of this premise on real data. A framework was created for the task in which expert-provided boundary exemplars are used to create training data, which in turn are used by a neural network to learn the task and replicate the expert's boundary tracing behavior. This is the framework for the Expert's Tracing Assistant (ETA) system. For a representative set of nine structures in the Visible Human imagery, ETA was compared and contrasted to two state-of-the-art, user guided methods--Intelligent Scissors (IS) and Active Contour Models (ACM). Each method was used to define a boundary, and the distances between these boundaries and an expert's ground truth were compared. Across independent trials, there will be a natural variation in an expert's boundary tracing, and this degree of variation served as a benchmark against which these three methods were compared. For simple structural boundaries, all the methods were equivalent. However, in more difficult cases, ETA was shown to significantly better replicate the expert's boundary than either IS or ACM. In these cases, where the expert's judgement was most called into play to bound the structure, ACM and IS could not adapt to the boundary character used by the expert while ETA could

Dynamic And Quantitative Radiomics Analysis In Interventional Radiology

Interventional Radiology (IR) is a subspecialty of radiology that performs invasive procedures driven by diagnostic imaging for predictive and therapeutic purpose. The development of artificial intelligence (AI) has revolutionized the industry of IR. Researchers have created sophisticated models backed by machine learning algorithms and optimization methodologies for image registration, cellular structure detection and computer-aided disease diagnosis and prognosis predictions. However, due to the incapacity of the human eye to detect tiny structural characteristics and inter-radiologist heterogeneity, conventional experience-based IR visual evaluations may have drawbacks. Radiomics, a technique that utilizes machine learning, offers a practical and quantifiable solution to this issue. This technology has been used to evaluate the heterogeneity of malignancies that are difficult to detect by the human eye by creating an automated pipeline for the extraction and analysis of high throughput computational imaging characteristics from radiological medical pictures. However, it is a demanding task to directly put radiomics into applications in IR because of the heterogeneity and complexity of medical imaging data. Furthermore, recent radiomics studies are based on static images, while many clinical applications (such as detecting the occurrence and development of tumors and assessing patient response to chemotherapy and immunotherapy) is a dynamic process. Merely incorporating static features cannot comprehensively reflect the metabolic characteristics and dynamic processes of tumors or soft tissues. To address these issues, we proposed a robust feature selection framework to manage the high-dimensional small-size data. Apart from that, we explore and propose a descriptor in the view of computer vision and physiology by integrating static radiomics features with time-varying information in tumor dynamics. The major contributions to this study include: Firstly, we construct a result-driven feature selection framework, which could efficiently reduce the dimension of the original feature set. The framework integrates different feature selection techniques to ensure the distinctiveness, uniqueness, and generalization ability of the output feature set. In the task of classification hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC) in primary liver cancer, only three radiomics features (chosen from more than 1, 800 features of the proposed framework) can obtain an AUC of 0.83 in the independent dataset. Besides, we also analyze features’ pattern and contributions to the results, enhancing clinical interpretability of radiomics biomarkers. Secondly, we explore and build a pulmonary perfusion descriptor based on 18F-FDG whole-body dynamic PET images. Our major novelties include: 1) propose a physiology-and-computer-vision-interpretable descriptor construction framework by the decomposition of spatiotemporal information into three dimensions: shades of grey levels, textures, and dynamics. 2) The spatio-temporal comparison of pulmonary descriptor intra and inter patients is feasible, making it possible to be an auxiliary diagnostic tool in pulmonary function assessment. 3) Compared with traditional PET metabolic biomarker analysis, the proposed descriptor incorporates image’s temporal information, which enables a better understanding of the time-various mechanisms and detection of visual perfusion abnormalities among different patients. 4) The proposed descriptor eliminates the impact of vascular branching structure and gravity effect by utilizing time warping algorithms. Our experimental results showed that our proposed framework and descriptor are promising tools to medical imaging analysis