Emerging Technologies, Signal Processing and Statistical Methods for Screening of Cervical Cancer In Vivo: Are They Good Candidates for Cervical Screening?

The current cervical cancer screening test (the Pap smear) is a manual cytological procedure. This cytology test has various limitations and many errors. Excellent candidates for improving the performance of the cervical cancer screening procedure are electro-optical systems (EOSs), used for assessment of the cervical cancer precursors in vivo, such as digital spectroscopy, digital colposcopy and bioelectrical phenomena-based systems. These EOSs use the advantages of signal processing methods and can replace the qualitative assessments, with objective metrics. The EOSs can be used as an adjunct to the current screener or as a primary screener. We analyse and discuss the effectiveness of the signal processing and statistical methods for diagnosis of cervical cancer in vivo. This analysis is reinforced by the presentation of the scientific and clinical contributions of these methods in clinical practice. As a result of this analysis, we outline and discuss the well-established estimates of the signal processing features and the ambiguous features, that are used for classification of the cervical pre-cancer in vivo

Cervical weakness and preterm birth: The structure and function of the internal cervical os

The cervix is integral to the maintenance of pregnancy and timely delivery of the baby. Mechanical failure of the cervix resulting in spontaneous preterm birth presents with collapse of the internal os, yet little is known about why the cervix behaves in this way. This may in part be due to research being technically limited and/or limited to punch biopsies of the distal cervix that did not include tissue from the internal os. The aim of this thesis was to re-evaluate cervical anatomy using novel laboratory and imaging methods to gain further insight into the structure of the cervix and how this may influence function during pregnancy. To achieve this, whole cervical samples were obtained from women undergoing hysterectomy for benign pathology. Uterine tissue was subsequently fixed and analysed using 2D and 3D histological methods. Cervical anatomy was characterised using markers for smooth muscle and collagen and analysed using computer-assisted quantification methods. Sequential tissue slices were then reconstructed to produce 3D models of the proximal, middle and distal cervix. High-resolution diffusion-tensor imaging was used to determine whether complex cervical anatomy could be visualised using radiological methods. Tissue was assessed using quantitative and qualitative diffusion methods, and directly compared to immunohistochemically stained tissue. The results obtained demonstrated that diffusion-tensor imaging accurately assessed cervical anatomy and provided further detail in terms of fibre volume, density and organisation. Ex vivo endoscopic ultrasound was used to assess whether current, established medical imaging technology could discern cervical smooth muscle and collagen fibres. Although this method could be used to identify gross anatomical structures, it was not an appropriate method to identify cervical microanatomy. The results described in this thesis provide further insight into how the cervix resists intrauterine forces throughout pregnancy, and then dilates and effaces to allow for delivery of a fetus. Diffusion-tensor imaging accurately assessed cervical anatomy, which may have implications for in vivo characterisation of cervical remodelling during pregnancy and identifying those at risk of delivering early. Finally, observations in this thesis encourage continued re-examination of the cervix using high-resolution imaging to provide insight into function and to develop strategies to discern cervical insufficiency from other known causes of preterm birth

Digital capture of the histological microarchitecture in the myometrium and its implications for the propagation of electrophysiological excitation.

Coordination of uterine contractions during labour is critical for successful delivery. The mechanisms underlying this coordination are not fully understood. Propagation of contraction signals has previously been observed to occur through transmission of electrical excitation waves. This thesis aims to examine the histological microarchitecture of the muscular layer of the uterus (myometrium) and determine how this structure affects the propagation of excitation by means of in silico three-dimensional reconstruction of the myometrium and numerical simulations of a spatially structured excitation-relaxation model. A key aim of the in silico reconstruction of the smooth muscle architecture of the myometrium is to identify structural features that correspond to the control of excitation behaviour in the myometrium. This examination is aided by analysis of excitation patterns observed in multi-electrode array recordings. The reconstruction is subsequently used as a basis for simulating electrical activity in the myometrium. Novel structural features are identified here that are located at the initiation points of electrical activity and are proposed to be the pacemaker sites in rat myometrium. Furthermore, boundary of low connectivity across the mesometrial border was observed in the rat, which corresponds to the termination of excitation waves observed in multielectrode array recordings. In addition, bridges of smooth muscle cells connecting the inner and outer layers of the myometrium were observed in both rat and human myometrium. Taken together these three features suggest a novel mechanism for control of contraction in the rat myometrium; an analogous mechanism is proposed for the human myometrium. The results presented in this thesis could provide an explanation for the patterns of excitation propagation observed in human and rat uteri. Further refinements of the methods used here are outlined and expected to generate a more detailed visualisation of the structures underpinning these mechanisms

An integrated clinical-MR radiomics model to estimate survival time in patients with endometrial cancer

Background: Determination of survival time in women with endometrial cancer using clinical features remains imprecise. Features from MRI may improve the survival estimation allowing improved treatment planning. Purpose: To identify clinical features and imaging signatures on T2-weighted MRI that can be used in an integrated model to estimate survival time for endometrial cancer subjects. Study Type: Retrospective. Population: Four hundred thirteen patients with endometrial cancer as training (N = 330, 66.41 ± 11.42 years) and validation (N = 83, 67.60 ± 11.89 years) data and an independent set of 82 subjects as testing data (63.26 ± 12.38 years). Field Strength/Sequence: 1.5-T and 3-T scanners with sagittal T2-weighted spin echo sequence. Assessment: Tumor regions were manually segmented on T2-weighted images. Features were extracted from segmented masks, and clinical variables including age, cancer histologic grade and risk score were included in a Cox proportional hazards (CPH) model. A group least absolute shrinkage and selection operator method was implemented to determine the model from the training and validation datasets. Statistical Tests: A likelihood-ratio test and decision curve analysis were applied to compare the models. Concordance index (CI) and area under the receiver operating characteristic curves (AUCs) were calculated to assess the model. Results: Three radiomic features (two image intensity and volume features) and two clinical variables (age and cancer grade) were selected as predictors in the integrated model. The CI was 0.797 for the clinical model (includes clinical variables only) and 0.818 for the integrated model using training and validation datasets, the associated mean AUC value was 0.805 and 0.853. Using the testing dataset, the CI was 0.792 and 0.882, significantly different and the mean AUC was 0.624 and 0.727 for the clinical model and integrated model, respectively. Data Conclusion: The proposed CPH model with radiomic signatures may serve as a tool to improve estimated survival time in women with endometrial cancer

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