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

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

Biomedical Photoacoustic Imaging and Sensing Using Affordable Resources

The overarching goal of this book is to provide a current picture of the latest developments in the capabilities of biomedical photoacoustic imaging and sensing in an affordable setting, such as advances in the technology involving light sources, and delivery, acoustic detection, and image reconstruction and processing algorithms. This book includes 14 chapters from globally prominent researchers , covering a comprehensive spectrum of photoacoustic imaging topics from technology developments and novel imaging methods to preclinical and clinical studies, predominantly in a cost-effective setting. Affordability is undoubtedly an important factor to be considered in the following years to help translate photoacoustic imaging to clinics around the globe. This first-ever book focused on biomedical photoacoustic imaging and sensing using affordable resources is thus timely, especially considering the fact that this technique is facing an exciting transition from benchtop to bedside. Given its scope, the book will appeal to scientists and engineers in academia and industry, as well as medical experts interested in the clinical applications of photoacoustic imaging

Ultrasound Imaging

This book provides an overview of ultrafast ultrasound imaging, 3D high-quality ultrasonic imaging, correction of phase aberrations in medical ultrasound images, etc. Several interesting medical and clinical applications areas are also discussed in the book, like the use of three dimensional ultrasound imaging in evaluation of Asherman's syndrome, the role of 3D ultrasound in assessment of endometrial receptivity and follicular vascularity to predict the quality oocyte, ultrasound imaging in vascular diseases and the fetal palate, clinical application of ultrasound molecular imaging, Doppler abdominal ultrasound in small animals and so on

Synchrotron imaging of bovine and human ovaries ex vivo

Background and Rationale: Reproductive dysfunction affects more than 15% of Canadian women; however, the underlying causes remain largely unknown. Ultrasonography is the most commonly used research and diagnostic tool for imaging the ovaries and uterus. However, current ultrasonographic techniques allow the detection of ovarian structures (eg. follicles, corpora lutea) at diameters of only ≥2 mm. The increased effectiveness of synchrotron technology for imaging ovaries in comparison to conventional imaging methods is currently unknown. Overall Objective: The overall objective of this research was to determine the effectiveness of synchrotron techniques for imaging ovaries. We hypothesized that synchrotron techniques would provide greater contrast for visualizing structural details of follicles, corpora lutea (CL), and cumulus oocyte complexes (COC), compared to conventional ultrasonography. Materials and Methods: Three studies were conducted to evaluate phase-contrast based synchrotron imaging methods. The first study involved Diffraction Enhanced Imaging (DEI) of bovine ovaries (n=6). The second study involved Propagation-Based Computed Tomography (PB-CT) imaging of bovine (n=4) and human ovaries (n=4). A third, preliminary study was conducted to explore the use of Talbot Grating Interferometry (TGI-CT) imaging of bovine (n=1) and human ovaries (n=1). Fresh and formalin-fixed bovine and human ovaries were imaged without or with contrast injection into the ovarian artery. Following synchrotron imaging, all ovarian samples were evaluated using diagnostic ultrasonography and histology. Images obtained using synchrotron techniques, ultrasonography and histology were qualitative and quantitatively compared. Results: DEI allowed the identification of 71% of follicles ≥2 mm and 67% of CL detected using ultrasonography. Mean follicle diameter was similar between DEI (9.6 ± 2.4 mm), ultrasonography (9.0 ± 2.6 mm), and histology (6.9 ± 1.9 mm) for fresh ovaries without contrast (P = 0.70). Likewise, no difference in CL diameter was detected between DEI (11.64 ± 1.67 mm), ultrasonography (9.34 ± 0.35 mm), and histology (9.6 ± 0.4 mm), (P = 0.34). Antral Follicle Count (AFC; ≥2mm) was similar between ultrasonography (6.5 ± 0.7 mm, fresh with no contrast; 6.5 ± 2.5 mm, preserved with no contrast) and DEI ( 4.5 ± 0.5 mm, fresh with no contrast; 6.5 ± 0.50 mm, preserved with no contrast) (P > 0.05). However, the contrast resolution for differentiating follicles and CL was inferior with DEI compared to ultrasonography. Small antral follicles <2mm, cell layers comprising the follicle wall and COC were not detected using either DEI or ultrasonography. PB-CT imaging enabled the visualization of 100% of follicles ≥2 mm and 100% of CL that were detected with ultrasonography. CL containing a central cystic cavity were identified using PB-CT; however, CL without a central cystic cavity were not well-visualized. Mean follicle and luteal diameters did not differ among PB-CT, ultrasonography and histology (P>0.05). PB-CT was superior to ultrasonography for detecting small antral follicles <2 mm in bovine ovaries (P = 0.04), and the granulosa and theca cell layers of the follicle wall in bovine and human ovaries (P < 0.0001). However, TGI-CT images exhibited greater contrast resolution for visualizing small and large antral follicles, CL, and the cell layers of the follicle wall compared to both PB-CT and ultrasonography. High contrast structures resembling COC were detected with both PB-CT and TGI-CT, but not with ultrasonography. Only TGI-CT permitted the visualization of the oocyte within the COC in fresh and preserved ovaries. Conclusions: DEI was inferior to ultrasonography for detecting ovarian follicles and CL. PB-CT was superior to ultrasonography for visualizing follicles <2 mm, COC, and the cell layers of the follicle wall. However, PB-CT was as effective as ultrasonography for detecting and measuring follicles ≥2 mm and cystic CL. Preliminary findings suggest that TGI-CT provides the greatest contrast for imaging both ovarian macro- and microanatomy compared to PB-CT, DEI, and ultrasonography

Transforming fertility services into remote health care

[EN] With the rising of technology, remote services providing Health Care at home is becoming a must for organizations, as it enables additional patient-centric initiatives. The transition to remote health care has attracted both new competitors and new partners to the health care industry. In the fertility care industry, the entry of new firms indicates there is an opportunity for remote processes also for incumbent firms. This TFM aims to analyze in depth the convenience of transforming on a remote service every medical interaction with patients that are required for fertility care. Previous analyses will include trends in the sector, competitors positioning and the remote services they offer, including technologies and systems required as support for these services. Then, a redefinition of the service design based on the patient journey will be carried out, evaluating how many of the compulsory visits to the clinics can be replaced by a patient-at-home service. This will contain a look at the current or upcoming state of art of technology. The TFM can be useful as a reference for firms in the Health Care market which are considering some transition to remote care services.Almudéver Galán, MÁ. (2022). Transforming fertility services into remote health care. Universitat Politècnica de València. http://hdl.handle.net/10251/187073TFG

Methodology for extensive evaluation of semiautomatic and interactive segmentation algorithms using simulated Interaction models

Performance of semiautomatic and interactive segmentation(SIS) algorithms are usually evaluated by employing a small number of human operators to segment the images. The human operators typically provide the approximate location of objects of interest and their boundaries in an interactive phase, which is followed by an automatic phase where the segmentation is performed under the constraints of the operator-provided guidance. The segmentation results produced from this small set of interactions do not represent the true capability and potential of the algorithm being evaluated. For example, due to inter-operator variability, human operators may make choices that may provide either overestimated or underestimated results. As well, their choices may not be realistic when compared to how the algorithm is used in the field, since interaction may be influenced by operator fatigue and lapses in judgement. Other drawbacks to using human operators to assess SIS algorithms, include: human error, the lack of available expert users, and the expense. A methodology for evaluating segmentation performance is proposed here which uses simulated Interaction models to programmatically generate large numbers of interactions to ensure the presence of interactions throughout the object region. These interactions are used to segment the objects of interest and the resulting segmentations are then analysed using statistical methods. The large number of interactions generated by simulated interaction models capture the variabilities existing in the set of user interactions by considering each and every pixel inside the entire region of the object as a potential location for an interaction to be placed with equal probability. Due to the practical limitation imposed by the enormous amount of computation for the enormous number of possible interactions, uniform sampling of interactions at regular intervals is used to generate the subset of all possible interactions which still can represent the diverse pattern of the entire set of interactions. Categorization of interactions into different groups, based on the position of the interaction inside the object region and texture properties of the image region where the interaction is located, provides the opportunity for fine-grained algorithm performance analysis based on these two criteria. Application of statistical hypothesis testing make the analysis more accurate, scientific and reliable in comparison to conventional evaluation of semiautomatic segmentation algorithms. The proposed methodology has been demonstrated by two case studies through implementation of seven different algorithms using three different types of interaction modes making a total of nine segmentation applications to assess the efficacy of the methodology. Application of this methodology has revealed in-depth, fine details about the performance of the segmentation algorithms which currently existing methods could not achieve due to the absence of a large, unbiased set of interactions. Practical application of the methodology for a number of algorithms and diverse interaction modes have shown its feasibility and generality for it to be established as an appropriate methodology. Development of this methodology to be used as a potential application for automatic evaluation of the performance of SIS algorithms looks very promising for users of image segmentation