Semi-Automatic Segmentation of Normal Female Pelvic Floor Structures from Magnetic Resonance Images

Stress urinary incontinence (SUI) and pelvic organ prolapse (POP) are important health issues affecting millions of American women. Investigation of the cause of SUI and POP requires a better understand of the anatomy of female pelvic floor. In addition, pre-surgical planning and individualized treatment plans require development of patient-specific three-dimensional or virtual reality models. The biggest challenge in building those models is to segment pelvic floor structures from magnetic resonance images because of their complex shapes, which make manual segmentation labor-intensive and inaccurate. In this dissertation, a quick and reliable semi-automatic segmentation method based on a shape model is proposed. The model is built on statistical analysis of the shapes of structures in a training set. A local feature map of the target image is obtained by applying a filtering pipeline, including contrast enhancement, noise reduction, smoothing, and edge extraction. With the shape model and feature map, automatic segmentation is performed by matching the model to the border of the structure using an optimization technique called evolution strategy. Segmentation performance is evaluated by calculating a similarity coefficient between semi-automatic and manual segmentation results. Taguchi analysis is performed to investigate the significance of segmentation parameters and provide tuning trends for better performance. The proposed method was successfully tested on both two-dimensional and three-dimensional image segmentation using the levator ani and obturator muscles as examples. Although the method is designed for segmentation of female pelvic floor structures, it can also be applied to other structures or organs without large shape variatio

Semi-Automatic Segmentation of Normal Female Pelvic Floor Structures from Magnetic Resonance Images

Stress urinary incontinence (SUI) and pelvic organ prolapse (POP) are important health issues affecting millions of American women. Investigation of the cause of SUI and POP requires a better understand of the anatomy of female pelvic floor. In addition, pre-surgical planning and individualized treatment plans require development of patient-specific three-dimensional or virtual reality models. The biggest challenge in building those models is to segment pelvic floor structures from magnetic resonance images because of their complex shapes, which make manual segmentation labor-intensive and inaccurate. In this dissertation, a quick and reliable semi-automatic segmentation method based on a shape model is proposed. The model is built on statistical analysis of the shapes of structures in a training set. A local feature map of the target image is obtained by applying a filtering pipeline, including contrast enhancement, noise reduction, smoothing, and edge extraction. With the shape model and feature map, automatic segmentation is performed by matching the model to the border of the structure using an optimization technique called evolution strategy. Segmentation performance is evaluated by calculating a similarity coefficient between semi-automatic and manual segmentation results. Taguchi analysis is performed to investigate the significance of segmentation parameters and provide tuning trends for better performance. The proposed method was successfully tested on both two-dimensional and three-dimensional image segmentation using the levator ani and obturator muscles as examples. Although the method is designed for segmentation of female pelvic floor structures, it can also be applied to other structures or organs without large shape variatio

Semi-Automatic Segmentation of Normal Female Pelvic Floor Structures from Magnetic Resonance Images

Stress urinary incontinence (SUI) and pelvic organ prolapse (POP) are important health issues affecting millions of American women. Investigation of the cause of SUI and POP requires a better understand of the anatomy of female pelvic floor. In addition, pre-surgical planning and individualized treatment plans require development of patient-specific three-dimensional or virtual reality models. The biggest challenge in building those models is to segment pelvic floor structures from magnetic resonance images because of their complex shapes, which make manual segmentation labor-intensive and inaccurate. In this dissertation, a quick and reliable semi-automatic segmentation method based on a shape model is proposed. The model is built on statistical analysis of the shapes of structures in a training set. A local feature map of the target image is obtained by applying a filtering pipeline, including contrast enhancement, noise reduction, smoothing, and edge extraction. With the shape model and feature map, automatic segmentation is performed by matching the model to the border of the structure using an optimization technique called evolution strategy. Segmentation performance is evaluated by calculating a similarity coefficient between semi-automatic and manual segmentation results. Taguchi analysis is performed to investigate the significance of segmentation parameters and provide tuning trends for better performance. The proposed method was successfully tested on both two-dimensional and three-dimensional image segmentation using the levator ani and obturator muscles as examples. Although the method is designed for segmentation of female pelvic floor structures, it can also be applied to other structures or organs without large shape variatio

Computational Intelligence in Electromyography Analysis

Electromyography (EMG) is a technique for evaluating and recording the electrical activity produced by skeletal muscles. EMG may be used clinically for the diagnosis of neuromuscular problems and for assessing biomechanical and motor control deficits and other functional disorders. Furthermore, it can be used as a control signal for interfacing with orthotic and/or prosthetic devices or other rehabilitation assists. This book presents an updated overview of signal processing applications and recent developments in EMG from a number of diverse aspects and various applications in clinical and experimental research. It will provide readers with a detailed introduction to EMG signal processing techniques and applications, while presenting several new results and explanation of existing algorithms. This book is organized into 18 chapters, covering the current theoretical and practical approaches of EMG research

Modelling of expert nurses' pressure sore risk assessment skills as an expert system for in-service training

In the nursing literature to date there have been no reported applications of `cognitive simulation' nor of intelligent Computer Assisted Learning. In Chapter 1 of this thesis a critical review of existing nurse education by computer is used to establish a framework within which to explore the possibility of simulation of thinking processes of nurses on computer. One conclusion from this review which is offered concerns the importance of firstly undertaking reliable study of nursing cognition. The crucial issue is that an understanding must be gained of how expert nurses mentally represent their patients in order that a valid model might be constructed on computer. The construction of a valid computer based cognitive model proves to be an undertaking which occupies the remainder of this thesis. The approach has been to gradually raise the specificity of analysis of the knowledge base of expert and proficient nurses while seeking concurrently to evaluate validity of the findings. Reported in Chapter 2, therefore, are the several experimental stages of a knowledge acquisition project which begins the process of constructing this knowledge base. Discussed firstly is the choice of the skill domain to be studied - pressure sore risk assessment. Subsequently, the method of eliciting from nurses top-level and micro-level descriptors of patients is set out. This account of knowledge acquisition ends with scrutiny of the performance of nurse subjects who performed a comprehensive simulated patient assessment task in order that two groups might be established - one Expert and one Proficient with respect to the nursing task. In Chapter 3, an extensive analysis of the data provided by the simulated assessment experiment is undertaken. This analysis, as the most central phase of the project, proceeds by degrees. Hence, the aim is to `explain' progressively more of the measured cognitive behaviour of the Expert nurses while incorporating the most powerful explanations into a developing cognitive model. More specifically, explanations are sought of the role of `higher' cognition, of whether attribute importance is a feature of cognition, of the point at which a decision can be made, and of the process of deciding between competing patient judgements. Interesting findings included several reliable differences which were found to exist between the cognition of subjects deemed to be proficient and those taken as expert. In the final part of this thesis, Chapter 4, a more formal evaluation of the computer based cognitive model which was constructed and predictions made by it was undertaken. The first phase involved analysis in terms of process and product of decision making of the cognitive model in comparison to two alternative models; one derived from Discriminant Function Analysis and the other from Automated Rule Induction. The cognitive model was found to most closely approximate to the process of decision making of the human subjects and also to perform most accurately with a test set of unseen patients. The second phase reports some experimental support for the prediction made by the model that nurses represent their patients around action-related `care concepts' rather than in terms of diagnostic categories based on superficial features. The thesis concludes by offering some general conclusions and recommendations for further research

Effects of hormonal contraceptives on the female genital tract microbiota in South African adolescents

Background Young women in sub-Saharan Africa are disproportionally affected by HIV and often rely on injectable hormonal contraception (HC) to prevent unintended pregnancies. However, HC might affect HIV-1 risk through changes in the female genital tract (FGT) microbiota. We examined the impact of three different HC methods on the adolescent female genital tract microbiota and related cytokine and HIV target cell levels at the cervical mucosa in a randomized, crossover trial. Study design and methods 131 adolescent females aged 15 to 19 from Cape Town were enrolled into a randomized, crossover study. The participants were randomized into three study arms: 1. progestin-only injectable norethisterone enanthate (Net-En), 2. combined oral contraceptive pills (COCPs) or 3. combined contraceptive vaginal ring (CCVR) for 16 weeks. Participants then switched to one of the other HC options for a final four months. Vaginal samples were collected at baseline, crossover and exit. STI testing and Nugent scoring were performed at all study visits. Vaginal microbiota was characterized by 16S rRNA gene amplicon sequencing, cytokine concentrations were measured by Luminex and CD4+ T cells analysed by flow cytometry. Results Using fuzzy clustering, three major female genital tract bacterial community types were identified. Two of these were dominated by Lactobacillus species (L. crispatus and L. iners, respectively) and the third was comprised of a diverse group of anaerobic bacteria associated with bacterial vaginosis (BV). In an intention-to-treat analysis at crossover, participants randomized to COCP had a significantly less diverse vaginal microbiota compared to participants randomized to either Net-En or CCVR. The same was observed in an according to protocol analysis at crossover. Using differential abundance testing and random forest analyses, we found that species associated with BV and risk of HIV were significantly more abundant in, and predictive of, participants on Net-En (e.g. Prevotella, Sneathia and Dialister) or CCVR (e.g. Prevotella, Mycoplasma and Parvimonas) compared to COCP while L. iners was more common in the COCP group. Cytokine concentrations were positively associated with a diverse vaginal community and with specific bacterial taxa associated with BV and increased risk of HIV including species enriched in participants on Net-En and NuvaRing. In contrast, there were no association of the frequencies of CD4+ T cells expressing CCR5+ with the vaginal community or BV status. There was likewise no significant association with BV or diversity with Th17 cell frequency, yet BVassociated bacteria were more abundant in participants with higher frequencies of Th17 cells. Conclusions Our data generated from a randomized study suggests that COCPs use may exert a positive influence on genital health through an increase in lactobacilli and a decrease in BV-associated bacterial taxa with an accompanying decrease in overall bacterial diversity, vaginal pH and cytokine levels. In contrast, the vaginal microbiota of participants on Net-En and NuvaRing have increased levels of bacteria associated with BV and HIV risk and increased cytokine levels. We did not observe any association of the frequencies of CD4+ T cells expressing CCR5 or Th17-like cells with the vaginal community, BV status or HC use

Low-dimensional representations of neural time-series data with applications to peripheral nerve decoding

Bioelectronic medicines, implanted devices that influence physiological states by peripheral neuromodulation, have promise as a new way of treating diverse conditions from rheumatism to diabetes. We here explore ways of creating nerve-based feedback for the implanted systems to act in a dynamically adapting closed loop. In a first empirical component, we carried out decoding studies on in vivo recordings of cat and rat bladder afferents. In a low-resolution data-set, we selected informative frequency bands of the neural activity using information theory to then relate to bladder pressure. In a second high-resolution dataset, we analysed the population code for bladder pressure, again using information theory, and proposed an informed decoding approach that promises enhanced robustness and automatic re-calibration by creating a low-dimensional population vector. Coming from a different direction of more general time-series analysis, we embedded a set of peripheral nerve recordings in a space of main firing characteristics by dimensionality reduction in a high-dimensional feature-space and automatically proposed single efficiently implementable estimators for each identified characteristic. For bioelectronic medicines, this feature-based pre-processing method enables an online signal characterisation of low-resolution data where spike sorting is impossible but simple power-measures discard informative structure. Analyses were based on surrogate data from a self-developed and flexibly adaptable computer model that we made publicly available. The wider utility of two feature-based analysis methods developed in this work was demonstrated on a variety of datasets from across science and industry. (1) Our feature-based generation of interpretable low-dimensional embeddings for unknown time-series datasets answers a need for simplifying and harvesting the growing body of sequential data that characterises modern science. (2) We propose an additional, supervised pipeline to tailor feature subsets to collections of classification problems. On a literature standard library of time-series classification tasks, we distilled 22 generically useful estimators and made them easily accessible.Open Acces