3D follicle segmentation in ultrasound image volumes of ex-situ bovine ovaries

Conventional ultrasonographic examination of the bovine ovary is based on a sequence of two-dimensional (2D) cross-section images. Day-to-day estimation of the number, size, shape and position of the ovarian follicles is one of the most important aspects of ovarian research. Computer-assisted follicle segmentation of ovarian volume can relieve physicians from the tedious manual detection of follicles, provide objective assessment of spatial relationships between the ovarian structures and therefore has the potential to improve accuracy. Modern segmentation procedures are performed on 2D images and the three-dimensional (3D) visualization of follicles is obtained from the reconstruction of a sequence of 2D segmented follicles. The objective of this study was to develop a semi-automatic 3D follicle segmentation method based on seeded region growing. The 3D datasets were acquired from a sequence of 2D ultrasound images and the ovarian structures were segmented from the reconstructed ovarian volume in a single step. A “seed” is placed manually in each follicle and the growth of the seed is controlled by the algorithm using a combination of average grey-level, standard deviation of the intensity, newly-developed volumetric comparison test and a termination criterion. One important contribution of this algorithm is that it overcomes the boundary leakage problem of follicles of conventional 2D segmentation procedures. The results were validated against the aspiration volume of follicles, the manually detected follicles by an expert and an existing algorithm.We anticipate that this algorithm will enhance follicular assessment based on current ultrasound techniques in cases when large numbers of follicles (e.g. ovarian superstimulation) obviate accurate counting and size measurement

Advancements and Breakthroughs in Ultrasound Imaging

Ultrasonic imaging is a powerful diagnostic tool available to medical practitioners, engineers and researchers today. Due to the relative safety, and the non-invasive nature, ultrasonic imaging has become one of the most rapidly advancing technologies. These rapid advances are directly related to the parallel advancements in electronics, computing, and transducer technology together with sophisticated signal processing techniques. This book focuses on state of the art developments in ultrasonic imaging applications and underlying technologies presented by leading practitioners and researchers from many parts of the world

Evaluation of texture features for analysis of ovarian follicular development

Ovarian follicles in women are fluid-filled structures in the ovary that contain oocytes (eggs). A dominant follicle is physiologically selected and ovulates during the menstrual cycle. We examined the echotexture in ultrasonographic images of the follicle wall of dominant ovulatory follicles in women during natural menstrual cycles and dominant anovulatory follicles which developed in women using oral contraceptives (OC). Texture features of follicle wall regions of both ovulatory and anovulatory dominant follicles were evaluated over a period of seven days before ovulation (natural cycles) or peak estradiol concentrations (OC cycles). Differences in echotexture between the two classes of follicles were found for two co-occurrence matrix derived texture features and two edge-frequency based texture features. Co-occurrence energy and homogeneity were significantly lower for ovulatory follicles while edge density and edge contrast were higher for ovulatory follicles. In the each feature space, the two classes of follicle were adequately separable.This thesis employed several statistical approaches to analyses of texture features, such as plotting method and the Mann-Kendall method. A distinct change of feature trend was detected 3 or 4 days before the day of ovulation for ovulatory follicles in the two co-occurrence matrix derived texture features and two edge-frequency-based texture features. Anovulatory follicles, exhibited the biggest variation of the feature value 3 or 4 days before the day on which dominant follicles developed to maximum size. This discovery is believed to correspond to the ovarian follicles responding to system hormonal changes leading to presumptive ovulation

Segmentation of human ovarian follicles from ultrasound images acquired in vivo using geometric active contour models and a naïve Bayes classifier

Ovarian follicles are spherical structures inside the ovaries which contain developing eggs. Monitoring the development of follicles is necessary for both gynecological medicine (ovarian diseases diagnosis and infertility treatment), and veterinary medicine (determining when to introduce superstimulation in cattle, or dividing herds into different stages in the estrous cycle).Ultrasound imaging provides a non-invasive method for monitoring follicles. However, manually detecting follicles from ovarian ultrasound images is time consuming and sensitive to the observer's experience. Existing (semi-) automatic follicle segmentation techniques show the power of automation, but are not widely used due to their limited success.A new automated follicle segmentation method is introduced in this thesis. Human ovarian images acquired in vivo were smoothed using an adaptive neighbourhood median filter. Dark regions were initially segmented using geometric active contour models. Only part of these segmented dark regions were true follicles. A naïve Bayes classifier was applied to determine whether each segmented dark region was a true follicle or not. The Hausdorff distance between contours of the automatically segmented regions and the gold standard was 2.43 ± 1.46 mm per follicle, and the average root mean square distance per follicle was 0.86 ± 0.49 mm. Both the average Hausdorff distance and the root mean square distance were larger than those reported in other follicle segmentation algorithms. The mean absolute distance between contours of the automatically segmented regions and the gold standard was 0.75 ± 0.32 mm, which was below that reported in other follicle segmentation algorithms.The overall follicle recognition rate was 33% to 35%; and the overall image misidentification rate was 23% to 33%. If only follicles with diameter greater than or equal to 3 mm were considered, the follicle recognition rate increased to 60% to 63%, and the follicle misidentification rate increased slightly to 24% to 34%. The proposed follicle segmentation method is proved to be accurate in detecting a large number of follicles with diameter greater than or equal to 3 mm

Ultrasonic Evaluation of Superovulation in Cattle

Embryo transfer is now a standard management technique. Effective embryo transfer is dependent upon the success of superovulation used to increase the yield of viable, transferable embryos. Numerous studies have identified problems with repeatability and predictability of response to superovulation. Given these problems, an effective method of monitoring follicular growth, ovulation and formation of corpora lutea would be a useful adjunct to the embryo transfer procedure. This study evaluated ultrasound for monitoring follicular growth, ovulation and formation of the corpora lutea in superovulated cows and correlated findings with embryo recovery and post mortem examination of the ovaries. Eleven cows were studied through a control and superovulated cycle; superovulation was achieved using pregnant mare serum gonadotrophin (PMSG, 3000 or 1500 i.u.). Ovarian response was monitored using a real time B-mode scanner equipped with a linear 7.5 Mhz rectal transducer. In the control cycle, which was similar in all cows, luteolysis was followed by growth of a single follicle to 1.4+/-0.2 cm. Ovulation was identified by collapse of the follicle and appearance of a corpus haemorrhagicum and was confirmed by measuring plasma LH and progesterone concentration. By day 5 the mature corpus luteum was visible. At the time of PMSG injection the majority of follicles were 6 mm. Two days later, 6-10 follicles > 8 mm were identified on most ovaries. Ovulation, in the superovulated cycle was identified as either disappearance of large follicles or obvious reduction in the size of the ovary. It was possible to identify but not to quantify corpora haemorrhagica. It was considered possible to quantitate corpora lutea on day 6 after oestrus. However, this estimate of corpora lutea number correlated poorly with the number counted at post mortem. Plasma progesterone concentration was monitored throughout the cycles, until embryo flushing. Very high progesterone concentrations were measured at embryo flushing; these levels correlated poorly with either the number of corpora lutea on the ovaries post mortem, or the number of embryos recovered. Histological study of the superovulated ovaries revealed many follicles showing differing degrees of lutcinisation. It was suspected that these luteinised follicles contributed to the high progesterone levels. It was impossible to categorise some fully luteinised structures as either corpora lutea or anovulatory follicles at post mortem. However, ovulation failure would have explained the poor embryo yield. In conclusion, ultrasound examination was useful for monitoring follicular growth in response to superovulation. It proved difficult to accurately quantify ovulation or numbers of corpora lutea. As an aid to predicting embryo recovery ultrasound monitoring was inadequate for the same reasons that rectal palpation, laparoscopy, laparotomy or progesterone concentrations have proven inaccurate, namely, abnormal luteinised structures on the ovary and aberrant progesterone production. This problem is inherant to the superovulatory treatment

New markers for the detection of polycystic ovary syndrome

Polycystic Ovary Syndrome (PCOS) is a highly prevalent, complex genetic disorder of the endocrine system in women. Alterations that occur in women with PCOS can be due to several predisposing factors; among these contributors are genetic and epigenetic variations. Environmental factors play a weaker role, mainly in worsening insulin resistance. Enzyme, protein and genetic markers can depend as a biochemical diagnosis of PCOs. The genetic markers have been identified to be related to PCOS wasn’t useful for early diagnosis, which can only be used to confirm PCOS in patients already exhibiting the definitive symptoms. Protein and enzyme markers are commonly used for prognosis and monitoring the patient to prevent the development of the complications of PCOS. Proteins of the adipose tissue have been found to be greatly related to insulin resistance and the development of PCOS. The nature of enzymes and proteins of instability and easily degradable have prevented sufficient research from being carried out on them. Therefore, the diagnosis of PCOS relies on the analysis of multiple factors

Oestrus and ovulation detection in pasture-based dairy herds: the role of new technologies

Automatic milking systems (AMS) are becoming increasingly popular due to the growing cost of labour and reduced labour availability. The voluntary cow traffic and resultant distribution of milkings throughout the day and night affects most aspects of herd and farm management in AMS. The literature review (Chapter 1) highlighted a need to evaluate the effects of milk yield and milking frequency during early lactation on reproductive performance. The analysis of a 5-year historic database from Australia’s first AMS research farm (Chapter 2) found no significant association of average milk yield and milking frequency during 100 days in milk with any of the reproductive measures. However, the interval from calving to first oestrus increased gradually within the study period and consequently influenced other reproductive outcomes. As a result, a series of studies were conducted with a multidisciplinary approach (both physiological and technological) to investigate the potential to improve oestrus detection on pasture-based AMS farms. A field study (Chapter 3) was conducted to allow for the development and application of an algorithm to assess the application accuracy of an infrared thermography (IRT) device when used to detect oestrus events or pending oestrus events by detecting the time of ovulation. Vulval and muzzle temperatures were measured by IRT in twenty synchronized cows (using a controlled internal drug release and prostaglandin F2α). Whilst the IRT showed some potential as an oestrus detection aid with higher sensitivity than visual observation (67%) and Estrotect activation (67%), the specificity and positive predictive value were lower with the IRT. The vulva and muzzle were the focus areas for the IRT application and some concern was generated with regard to the potential for the IRT data to impacted by faecal contamination, obscuring of the vulva by the tail and time since last drinking (affecting muzzle surface temperature). To address these concerns a further study (Chapter 5) was conducted to test the hypothesis that the specificity of IRT in detecting oestrus (or imminent oestrus) could be improved if other body parts were focused on. In that study (Chapter 5), an additional technology was incorporated to test the hypothesis that the combined activity and rumination data generated by an accelerometer (SCR heat and rumination long distance tags) would provide a more accurate indication of oestrus and/or ovulation than the activity and rumination data alone. Unfortunately the monitoring of eyes and/or ears did not provide the improvement in accuracy of IRT (as an oestrus detection aid) indicating that as an oestrus detection aid there was likely to be limited value in developing this as an automated stand-alone device. Alerts generated by accelerometer based on a lower activity threshold level had high sensitivity and may be able to detect a high proportion of cows in ovulatory periods in pasture-based system; however, the specificities and positive predictive value were lower than the visual assessment of mounting indicators and would still require the herd’s person to filter data to identify the false alerts to ensure that cows are not inseminated unnecessarily. Whilst the use of in-line milk monitoring has already been commercialized for the assessment of milk progesterone, there is potential for other biomarkers to provide further opportunities for the assessment of milk components. Biomarkers of oxidative stress were evaluated in plasma showing that plasma glutathione was lower in ovulated cows compared to those of an-ovulated cows (Chapter 4). Whilst baseline plasma data for oxidative stress biomarkers was a useful starting point, the real value of these biomarkers would be realised if their concentration in milk could be linked with oestrus (and or ovulation). Milk superoxide dismutase activity was shown to be higher in ovulated cows while lipoperoxides, glutathione peroxidase were lower in ovulated cows compared to those in an-ovulated cows (Chapter 6). Further work would be required to determine the accuracy with which these biomarkers could be used to identify oestrus cows but these results are promising and suggest that there may be some potential to develop in-line milk sampling technology to alert the herdsperson to cows that should be inseminated. In summary, this thesis provides very useful, scientifically based information on potential use of technologies for oestrus and ovulation detection in dairy cows, which should serve as a foundation to develop and upgrade automated on-farm technologies and biosensors for better reproductive management of cows in pasture-based AMS. However, it is noted that the most likely success with automated oestrus detection is to require a combination of different indicators that should be incorporated to truly increase the accuracy of detection beyond that which can be achieved by skilled and devoted herd’s people