Evaluation of segmentation algorithms for optical coherence tomography images of ovarian tissue

Ovarian cancer has the lowest survival rate among all gynecologic cancers predominantly due to late diagnosis. Early detection of ovarian cancer can increase 5-year survival rates from 40% up to 92%, yet no reliable early detection techniques exist. Optical coherence tomography (OCT) is an emerging technique that provides depth-resolved, high-resolution images of biological tissue in real-time and demonstrates great potential for imaging of ovarian tissue. Mouse models are crucial to quantitatively assess the diagnostic potential of OCT for ovarian cancer imaging; however, due to small organ size, the ovaries must first be separated from the image background using the process of segmentation. Manual segmentation is time-intensive, as OCT yields three-dimensional data. Furthermore, speckle noise complicates OCT images, frustrating many processing techniques. While much work has investigated noise-reduction and automated segmentation for retinal OCT imaging, little has considered the application to the ovaries, which exhibit higher variance and inhomogeneity than the retina. To address these challenges, we evaluate a set of algorithms to segment OCT images of mouse ovaries. We examine five preprocessing techniques and seven segmentation algorithms. While all preprocessing methods improve segmentation, Gaussian filtering is most effective, showing an improvement of 32 % ± 1.2 % . Of the segmentation algorithms, active contours performs best, segmenting with an accuracy of 94.8 % ± 1.2 % compared with manual segmentation. Even so, further optimization could lead to maximizing the performance for segmenting OCT images of the ovaries.National Science Foundation Graduate Research Fellowship Program [DGE-1143953]; National Institutes of Health/National Cancer Institute [1R01CA195723]; University of Arizona Cancer Center [3P30CA023074]This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Biocompatibility and Biodegradation Studies of Subconjunctival Implants in Rabbit Eyes

Sustained ocular drug delivery is difficult to achieve. Most drugs have poor penetration due to the multiple physiological barriers of the eye and are rapidly cleared if applied topically. Biodegradable subconjunctival implants with controlled drug release may circumvent these two problems. In our study, two microfilms (poly [d,l-lactide-co-glycolide] PLGA and poly[d,l-lactide-co-caprolactone] PLC were developed and evaluated for their degradation behavior in vitro and in vivo. We also evaluated the biocompatibility of both microfilms. Eighteen eyes (9 rabbits) were surgically implanted with one type of microfilm in each eye. Serial anterior-segment optical coherence tomography (AS-OCT) scans together with serial slit-lamp microscopy allowed us to measure thickness and cross-sectional area of the microfilms. In vitro studies revealed bulk degradation kinetics for both microfilms, while in vivo studies demonstrated surface erosion kinetics. Serial slit-lamp microscopy revealed no significant inflammation or vascularization in both types of implants (mean increase in vascularity grade PLGA50/50 12±0.5% vs. PLC70/30 15±0.6%; P = 0.91) over a period of 6 months. Histology, immunohistochemistry and immuno-fluorescence also revealed no significant inflammatory reaction from either of the microfilms, which confirmed that both microfilms are biocompatible. The duration of the drug delivery can be tailored by selecting the materials, which have different degradation kinetics, to suit the desired clinical therapeutic application

Measuring the burden of arboviral diseases: the spectrum of morbidity and mortality from four prevalent infections

<p>Abstract</p> <p>Background</p> <p>Globally, arthropod-borne virus infections are increasingly common causes of severe febrile disease that can progress to long-term physical or cognitive impairment or result in early death. Because of the large populations at risk, it has been suggested that these outcomes represent a substantial health deficit not captured by current global disease burden assessments.</p> <p>Methods</p> <p>We reviewed newly available data on disease incidence and outcomes to critically evaluate the disease burden (as measured by disability-adjusted life years, or DALYs) caused by yellow fever virus (YFV), Japanese encephalitis virus (JEV), chikungunya virus (CHIKV), and Rift Valley fever virus (RVFV). We searched available literature and official reports on these viruses combined with the terms "outbreak(s)," "complication(s)," "disability," "quality of life," "DALY," and "QALY," focusing on reports since 2000. We screened 210 published studies, with 38 selected for inclusion. Data on average incidence, duration, age at onset, mortality, and severity of acute and chronic outcomes were used to create DALY estimates for 2005, using the approach of the current Global Burden of Disease framework.</p> <p>Results</p> <p>Given the limitations of available data, nondiscounted, unweighted DALYs attributable to YFV, JEV, CHIKV, and RVFV were estimated to fall between 300,000 and 5,000,000 for 2005. YFV was the most prevalent infection of the four viruses evaluated, although a higher proportion of the world's population lives in countries at risk for CHIKV and JEV. Early mortality and long-term, related chronic conditions provided the largest DALY components for each disease. The better known, short-term viral febrile syndromes caused by these viruses contributed relatively lower proportions of the overall DALY scores.</p> <p>Conclusions</p> <p>Limitations in health systems in endemic areas undoubtedly lead to underestimation of arbovirus incidence and related complications. However, improving diagnostics and better understanding of the late secondary results of infection now give a first approximation of the current disease burden from these widespread serious infections. Arbovirus control and prevention remains a high priority, both because of the current disease burden and the significant threat of the re-emergence of these viruses among much larger groups of susceptible populations.</p

Pharmacokinetic aspects of retinal drug delivery

Drug delivery to the posterior eye segment is an important challenge in ophthalmology, because many diseases affect the retina and choroid leading to impaired vision or blindness. Currently, intravitreal injections are the method of choice to administer drugs to the retina, but this approach is applicable only in selected cases (e.g. anti-VEGF antibodies and soluble receptors). There are two basic approaches that can be adopted to improve retinal drug delivery: prolonged and/or retina targeted delivery of intravitreal drugs and use of other routes of drug administration, such as periocular, suprachoroidal, sub-retinal, systemic, or topical. Properties of the administration route, drug and delivery system determine the efficacy and safety of these approaches. Pharmacokinetic and pharmacodynamic factors determine the required dosing rates and doses that are needed for drug action. In addition, tolerability factors limit the use of many materials in ocular drug delivery. This review article provides a critical discussion of retinal drug delivery, particularly from the pharmacokinetic point of view. This article does not include an extensive review of drug delivery technologies, because they have already been reviewed several times recently. Instead, we aim to provide a systematic and quantitative view on the pharmacokinetic factors in drug delivery to the posterior eye segment. This review is based on the literature and unpublished data from the authors' laboratory.Peer reviewe

In vivo optical coherence tomography of a mouse model of spontaneous ovarian cancer

Ovarian cancer is the deadliest gynecologic cancer, but can be addressed with early detection. We investigate optical coherence tomography for imaging ovarian cancer, finding that tissue changes can be detected through quantitative analysis.This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

In vivo multiphoton imaging of an ovarian cancer mouse model

Ovarian cancer is the deadliest gynecologic cancer due to predominantly late diagnosis. Early detection of ovarian cancer can increase 5-year survival rates from 40% up to 92%, yet no reliable early detection techniques exist. Multiphoton microscopy (MPM) is a relatively new imaging technique with tremendous potential for clinical diagnosis. A sub-modality of MPM is second harmonic generation (SHG) imaging, which generates contrast from anisotropic structures like collagen molecules, enabling the acquisition of detailed molecular structure maps. As collagen is known to change throughout the progression of cancer, MPM is a promising candidate for ovarian cancer screening. While MPM has shown favorable results in a research environment, it has not yet found broad success in a clinical setting. One major obstacle is the quantitative analysis of the image content. Recently, the application of texture analysis to MPM images has shown success for characterizing the collagen content of the tissue, making it a prime candidate for disease screening. Unfortunately, existing work is limited in its application to ovarian tissue and few texture analysis approaches have been evaluated in this context. To address these challenges, we applied texture analysis to second harmonic generation (SHG) and two-photon excited fluorescence (TPEF) images of a mouse model (TgMISIIR-TAg) of ovarian cancer. Using features from the grey-level co-occurrence matrix, we find that texture analysis of TPEF images of the ovary can differentiate between genotype with high statistical significance (p<0.001), whereas TPEF and SHG images of the oviducts are most sensitive to age, and SHG images of the ovaries are most sensitive to reproductive status. While these results suggest that texture analysis is suitable for characterizing ovarian tissue health, further work is focused on developing a classification algorithm based on these features, and also to couple the results with a histopathological analysis.This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]