IMAGE UNDERSTANDING OF MOLAR PREGNANCY BASED ON ANOMALIES DETECTION

Cancer occurs when normal cells grow and multiply without normal control. As the cells multiply, they form an area of abnormal cells, known as a tumour. Many tumours exhibit abnormal chromosomal segregation at cell division. These anomalies play an important role in detecting molar pregnancy cancer. Molar pregnancy, also known as hydatidiform mole, can be categorised into partial (PHM) and complete (CHM) mole, persistent gestational trophoblastic and choriocarcinoma. Hydatidiform moles are most commonly found in women under the age of 17 or over the age of 35. Hydatidiform moles can be detected by morphological and histopathological examination. Even experienced pathologists cannot easily classify between complete and partial hydatidiform moles. However, the distinction between complete and partial hydatidiform moles is important in order to recommend the appropriate treatment method. Therefore, research into molar pregnancy image analysis and understanding is critical. The hypothesis of this research project is that an anomaly detection approach to analyse molar pregnancy images can improve image analysis and classification of normal PHM and CHM villi. The primary aim of this research project is to develop a novel method, based on anomaly detection, to identify and classify anomalous villi in molar pregnancy stained images. The novel method is developed to simulate expert pathologists’ approach in diagnosis of anomalous villi. The knowledge and heuristics elicited from two expert pathologists are combined with the morphological domain knowledge of molar pregnancy, to develop a heuristic multi-neural network architecture designed to classify the villi into their appropriated anomalous types. This study confirmed that a single feature cannot give enough discriminative power for villi classification. Whereas expert pathologists consider the size and shape before textural features, this thesis demonstrated that the textural feature has a higher discriminative power than size and shape. The first heuristic-based multi-neural network, which was based on 15 elicited features, achieved an improved average accuracy of 81.2%, compared to the traditional multi-layer perceptron (80.5%); however, the recall of CHM villi class was still low (64.3%). Two further textural features, which were elicited and added to the second heuristic-based multi-neural network, have improved the average accuracy from 81.2% to 86.1% and the recall of CHM villi class from 64.3% to 73.5%. The precision of the multi-neural network II has also increased from 82.7% to 89.5% for normal villi class, from 81.3% to 84.7% for PHM villi class and from 80.8% to 86% for CHM villi class. To support pathologists to visualise the results of the segmentation, a software tool, Hydatidiform Mole Analysis Tool (HYMAT), was developed compiling the morphological and pathological data for each villus analysis

Proceedings of the International Workshop on Medical Ultrasound Tomography: 1.- 3. Nov. 2017, Speyer, Germany

Ultrasound Tomography is an emerging technology for medical imaging that is quickly approaching its clinical utility. Research groups around the globe are engaged in research spanning from theory to practical applications. The International Workshop on Medical Ultrasound Tomography (1.-3. November 2017, Speyer, Germany) brought together scientists to exchange their knowledge and discuss new ideas and results in order to boost the research in Ultrasound Tomography

Modern Approaches To Quality Control

Rapid advance have been made in the last decade in the quality control procedures and techniques, most of the existing books try to cover specific techniques with all of their details. The aim of this book is to demonstrate quality control processes in a variety of areas, ranging from pharmaceutical and medical fields to construction engineering and data quality. A wide range of techniques and procedures have been covered

Segmentation of neuroanatomy in magnetic resonance images

Segmentation in neurological Magnetic Resonance Imaging (MRI) is necessary for volume measurement, feature extraction and for the three-dimensional display of neuroanatomy. This thesis proposes several automated and semi-automated methods which offer considerable advantages over manual methods because of their lack of subjectivity, their data reduction capabilities, and the time savings they give. Work has concentrated on the use of dual echo multi-slice spin-echo data sets in order to take advantage of the intrinsically multi-parametric nature of MRI. Such data is widely acquired clinically and segmentation therefore does not require additional scans. The literature has been reviewed. Factors affecting image non-uniformity for a modem 1.5 Tesla imager have been investigated. These investigations demonstrate that a robust, fast, automatic three-dimensional non-uniformity correction may be applied to data as a pre-processing step. The merit of using an anisotropic smoothing method for noisy data has been demonstrated. Several approaches to neurological MRI segmentation have been developed. Edge-based processing is used to identify the skin (the major outer contour) and the eyes. Edge-focusing, two threshold based techniques and a fast radial CSF identification approach are proposed to identify the intracranial region contour in each slice of the data set. Once isolated, the intracranial region is further processed to identify CSF, and, depending upon the MRI pulse sequence used, the brain itself may be sub-divided into grey matter and white matter using semiautomatic contrast enhancement and clustering methods. The segmentation of Multiple Sclerosis (MS) plaques has also been considered. The utility of the stack, a data driven multi-resolution approach to segmentation, has been investigated, and several improvements to the method suggested. The factors affecting the intrinsic accuracy of neurological volume measurement in MRI have been studied and their magnitudes determined for spin-echo imaging. Geometric distortion - both object dependent and object independent - has been considered, as well as slice warp, slice profile, slice position and the partial volume effect. Finally, the accuracy of the approaches to segmentation developed in this thesis have been evaluated. Intracranial volume measurements are within 5% of expert observers' measurements, white matter volumes within 10%, and CSF volumes consistently lower than the expert observers' measurements due to the observers' inability to take the partial volume effect into account

Pattern Recognition

A wealth of advanced pattern recognition algorithms are emerging from the interdiscipline between technologies of effective visual features and the human-brain cognition process. Effective visual features are made possible through the rapid developments in appropriate sensor equipments, novel filter designs, and viable information processing architectures. While the understanding of human-brain cognition process broadens the way in which the computer can perform pattern recognition tasks. The present book is intended to collect representative researches around the globe focusing on low-level vision, filter design, features and image descriptors, data mining and analysis, and biologically inspired algorithms. The 27 chapters coved in this book disclose recent advances and new ideas in promoting the techniques, technology and applications of pattern recognition

Detection of somatic variants from genomic data and their role in neurodegenerative diseases

[eng] Somatic mutations are those that arise after the zygote is formed and are therefore inherited by a fraction of the cells of an individual. Their relevance in certain skin diseases has been known for almost half a decade and cancer, the most common disease caused by somatic mutations, has been extensively studied. Yet, their prevalence in healthy individuals as well as their putative role in other human disorders such as neurodegenerative diseases are still unanswered questions. Furthermore, accurate detection of somatic variants from bulk sequencing data still poses a technical challenge. This work focuses on detecting and circumventing the biases that hinder their identification. Using this knowledge, we identified somatic point mutations in the exomes of five different tissues from sporadic Parkinson disease patients. We also assessed the detection of somatic copy number variants from array CGH data using two tissues from Alzheimer disease patients. Finally, we participated in the identification of somatic variants in an extensive genomic dataset from a neurotypical individual.[spa] Las mutaciones somáticas son aquellas que surgen tras la formación del cigoto y son, por tanto, heredadas por una fracción de las células de un individuo. Su importancia en algunas enfermedades cutáneas se conoce desde hace casi medio siglo. El cáncer, la enfermedad más común causada por mutaciones somáticas, se ha estudiado extensamente. Sin embargo, su prevalencia en individuos sanos, así como su potencial relevancia en otras afecciones humanas, como las enfermedades neurodegenerativas, son cuestiones todavía por resolver. Asimismo, detectar variantes somáticas con precisión en datos de secuenciación de muestras homogeneizadas sigue siendo complejo técnicamente. Este trabajo se centra en la detección y resolución de los sesgos que dificultan su identificación. Aplicando este conocimiento, identificamos mutaciones somáticas de una sola base en datos de secuenciación del exoma de cinco tejidos diferentes de pacientes de la enfermedad de Parkinson. También evaluamos la detección de variantes de número de copia somáticas en datos de array CGH de dos tejidos de pacientes de Alzheimer. Finalmente, participamos en la identificación de variantes somáticas en un amplio conjunto de datos genómicos de un individuo neurotípico

Technology 2002: the Third National Technology Transfer Conference and Exposition, Volume 1

The proceedings from the conference are presented. The topics covered include the following: computer technology, advanced manufacturing, materials science, biotechnology, and electronics

Proceedings of the Scientific-Practical Conference "Research and Development - 2016"

talent management; sensor arrays; automatic speech recognition; dry separation technology; oil production; oil waste; laser technolog

Use of Software Tools to Implement Quality Control of Ultrasound Images in a Large Clinical Trial

Research Question This thesis aims to answer the question as to whether software tools might be developed for automating the analysis of images used to measure ovaries in transvaginal sonography (TVS) exams. Such tools would allow the routine collection of independent and objective metrics at low cost and might be used to drive a programme of continuous Quality Improvement (QI) in TVS scanning. The tools will be assessed by processing images from thousands of TVS exams performed by the United Kingdom Collaborative Trial of Ovarian Cancer Screening (UKCTOCS). Background This research is important because TVS is core to any ovarian cancer (OC) screening strategy yet independent and objective quality control (QC) metrics for this procedure are not routinely obtained due to the high cost of manual image inspection. Improving the quality of TVS in the National Health Service (NHS) would assist in the early diagnosis of the disease and result in improved outcome for some women. Therefore, the research has clear translational potential for the >1.2 million scans performed annually by the NHS. Research Findings A study performed to process images from 1,000 TVS exams has shown the tool produces accurate and reliable QC metrics. A further study revealed that over half of these exams should have been classified as unsatisfactory as an expert review of the images showed that that the sonographer had mistakenly measured a structure that was not an ovary. It also reported a correlation between such ovary visualisation and a novel metric (DCR) measured by the tools from the examination images. Conclusion The research results suggest both a need to improve the quality of TVS scanning and the viability of achieving this objective by introducing a QI programme driven by metrics gathered by software tools able to analyze the images used to measure ovaries

Proceedings of the Scientific-Practical Conference "Research and Development - 2016"

talent management; sensor arrays; automatic speech recognition; dry separation technology; oil production; oil waste; laser technolog