Recent Advances in Machine Learning Applied to Ultrasound Imaging

Machine learning (ML) methods are pervading an increasing number of fields of application because of their capacity to effectively solve a wide variety of challenging problems. The employment of ML techniques in ultrasound imaging applications started several years ago but the scientific interest in this issue has increased exponentially in the last few years. The present work reviews the most recent (2019 onwards) implementations of machine learning techniques for two of the most popular ultrasound imaging fields, medical diagnostics and non-destructive evaluation. The former, which covers the major part of the review, was analyzed by classifying studies according to the human organ investigated and the methodology (e.g., detection, segmentation, and/or classification) adopted, while for the latter, some solutions to the detection/classification of material defects or particular patterns are reported. Finally, the main merits of machine learning that emerged from the study analysis are summarized and discussed. © 2022 by the authors. Licensee MDPI, Basel, Switzerland

Computer-Assisted Algorithms for Ultrasound Imaging Systems

Ultrasound imaging works on the principle of transmitting ultrasound waves into the body and reconstructs the images of internal organs based on the strength of the echoes. Ultrasound imaging is considered to be safer, economical and can image the organs in real-time, which makes it widely used diagnostic imaging modality in health-care. Ultrasound imaging covers the broad spectrum of medical diagnostics; these include diagnosis of kidney, liver, pancreas, fetal monitoring, etc. Currently, the diagnosis through ultrasound scanning is clinic-centered, and the patients who are in need of ultrasound scanning has to visit the hospitals for getting the diagnosis. The services of an ultrasound system are constrained to hospitals and did not translate to its potential in remote health-care and point-of-care diagnostics due to its high form factor, shortage of sonographers, low signal to noise ratio, high diagnostic subjectivity, etc. In this thesis, we address these issues with an objective of making ultrasound imaging more reliable to use in point-of-care and remote health-care applications. To achieve the goal, we propose (i) computer-assisted algorithms to improve diagnostic accuracy and assist semi-skilled persons in scanning, (ii) speckle suppression algorithms to improve the diagnostic quality of ultrasound image, (iii) a reliable telesonography framework to address the shortage of sonographers, and (iv) a programmable portable ultrasound scanner to operate in point-of-care and remote health-care applications

Quantification of tumour heterogenity in MRI

Cancer is the leading cause of death that touches us all, either directly or indirectly. It is estimated that the number of newly diagnosed cases in the Netherlands will increase to 123,000 by the year 2020. General Dutch statistics are similar to those in the UK, i.e. over the last ten years, the age-standardised incidence rate1 has stabilised at around 355 females and 415 males per 100,000. Figure 1 shows the cancer incidence per gender. In the UK, the rise in lifetime risk of cancer is more than one in three and depends on many factors, including age, lifestyle and genetic makeup

Immune Cell Membrane-Coated Biomimetic Nanoparticles for Targeted Cancer Therapy

Nanotechnology has provided great opportunities for managing neoplastic conditions at various levels, from preventive and diagnostic to therapeutic fields. However, when it comes to clinical application, nanoparticles (NPs) have some limitations in terms of biological stability, poor targeting, and rapid clearance from the body. Therefore, biomimetic approaches, utilizing immune cell membranes, are proposed to solve these issues. For example, macrophage or neutrophil cell membrane coated NPs are developed with the ability to interact with tumor tissue to suppress cancer progression and metastasis. The functionality of these particles largely depends on the surface proteins of the immune cells and their preserved function during membrane extraction and coating process on the NPs. Proteins on the outer surface of immune cells can render a wide range of activities to the NPs, including prolonged blood circulation, remarkable competency in recognizing antigens for enhanced targeting, better cellular interactions, gradual drug release, and reduced toxicity in vivo. In this review, nano-based systems coated with immune cells-derived membranous layers, their detailed production process, and the applicability of these biomimetic systems in cancer treatment are discussed. In addition, future perspectives and challenges for their clinical translation are also presented.Peer reviewe

Automated sample preparation for streamlined proteomic profiling of clinical specimens

The genetic information of all life is encoded within DNA molecules that are translated into functional entities, so-called proteins. They are responsible for operating and controlling a vast array of molecular mechanisms in any biological system and ubiquitous in (patho)physiology as a result. Besides, proteins are the primary target of drugs and can have a central role as biomarkers for diagnostic, prognostic, or predictive purposes. Here, many regulatory mechanisms and spatiotemporal influences prevent an accurate prediction of a proteins’ abundance and its associated functionality based on the genome information alone. Nowadays, it has become possible to measure and quantify thousands of proteins simultaneously, however, involving comprehensive sample preparation procedures. Currently, no universally standardized method enables a routine application of proteome profiling in a clinical environment. In this thesis, an automated workflow for the efficient processing of the most common and quantity-limited specimens is described. In order to demonstrate the usefulness of the end-to- end pipeline, which was termed autoSP3, it was applied to the proteome profiling of histologically defined and WHO recognized growth patterns of pulmonary adenocarcinoma (ADC) that currently have a limited clinical implication. Secondly, we investigated the proteome composition of a molecularly well-defined cohort of Ependymoma (EPN) pediatric brain tumors. Despite the availability of substantial NGS data and their ability to differentiate nine distinct subgroups, the majority of tumors remained without a functional insight. Here, the proteome profiling could provide a missing link and emphasize several subgroup-specific protein targets. In summary, this thesis describes the optimization of SP3 and its automation into a robust and cost-efficient pipeline for quantity-limited sample preparation and biological insight into the proteome composition of ADC growth patterns and EPN tumor subgroups

Aerospace Medicine and Biology: Cumulative index, 1979

This publication is a cumulative index to the abstracts contained in the Supplements 190 through 201 of 'Aerospace Medicine and Biology: A Continuing Bibliography.' It includes three indexes-subject, personal author, and corporate source