Gland context networks: A novel approach for improving prostate cancer identification.

Prostate cancer (PCa) is a pervasive condition that is manifested in a wide range of histologic patterns in biopsy samples. Given the importance of identifying abnormal prostate tissue to improve prognosis, many computerized methodologies aimed at assisting pathologists in diagnosis have been developed. It is often argued that improved diagnosis of a tissue region can be obtained by considering measurements that can take into account several properties of its surroundings, therefore providing a more robust context for the analysis. Here we propose a novel methodology that can be used for systematically defining contextual features regarding prostate glands. This is done by defining a Gland Context Network (GCN), a representation of the prostate sample containing information about the spatial relationship between glands as well as the similarity between their appearance. We show that such a network can be used for establishing contextual features at any spatial scale, therefore providing information that is not easily obtained from traditional shape and textural features. Furthermore, it is shown that even basic features derived from a GCN can lead to state-of-the-art classification performance regarding PCa. All in all, GCNs can assist in defining more effective approaches for PCa grading.

Quantification of retinal blood leakage in fundus fluorescein angiography in a retinal angiogenesis model.

Blood leakage from the vessels in the eye is the hallmark of many vascular eye diseases. One of the preclinical mouse models of retinal blood leakage, the very-low-density-lipoprotein receptor deficient mouse (Vldlr-/-), is used for drug screening and mechanistic studies. Vessel leakage is usually examined using Fundus fluorescein angiography (FFA). However, interpreting FFA images of the Vldlr-/- model is challenging as no automated and objective techniques exist for this model. A pipeline has been developed for quantifying leakage intensity and area including three tasks: (i) blood leakage identification, (ii) blood vessel segmentation, and (iii) image registration. Morphological operations followed by log-Gabor quadrature filters were used to identify leakage regions. In addition, a novel optic disk detection algorithm based on graph analysis was developed for registering the images at different timepoints. Blood leakage intensity and area measured by the methodology were compared to ground truth quantifications produced by two annotators. The relative difference between the quantifications from the method and those obtained from ground truth images was around 10% ± 6% for leakage intensity and 17% ± 8% for leakage region. The Pearson correlation coefficient between the method results and the ground truth was around 0.98 for leakage intensity and 0.94 for leakage region. Therefore, we presented a computational method for quantifying retinal vascular leakage and vessels using FFA in a preclinical angiogenesis model, the Vldlr-/- model.

Microbial ecology in selenate-reducing biofilm communities: Rare biosphere and their interactions with abundant phylotypes.

Selenate (SeO42- ) reduction in hydrogen (H2 )-fed membrane biofilm reactors (H2 -MBfRs) was studied in combinations with other common electron acceptors. We employed H2 -MBfRs with two distinctly different conditions: R1, with ample electron-donor availability and acceptors SeO42- and sulfate (SO42- ), and R2, with electron-donor limitation and the presence of electron acceptors SeO42- , nitrate (NO3- ), and SO42- . Even though H2 was available to reduce all input SeO42- and SO42- in R1, SeO42- reduction was preferred over SO42- reduction. In R2, co-reduction of NO3- and SeO42- occurred, and SO42- reduction was mostly suppressed. Biofilms in all MBfRs had high microbial diversity that was influenced by the "rare biosphere" (RB), phylotypes with relative abundance less than 1%. While all MBfR biofilms had abundant members, such as Dechloromonas and Methyloversatilis, the bacterial communities were significantly different between R1 and R2. For R1, abundant genera were Methyloversatilis, Melioribacter, and Propionivibrio; for R2, abundant genera were Dechloromonas, Hydrogenophaga, Cystobacter, Methyloversatilis, and Thauera. Although changes in electron-acceptor or -donor loading altered the phylogenetic structure of the microbial communities, the biofilm communities were resilient in terms of SeO42- and NO3- reductions, because interacting members of the RB had the capacity of respiring these electron acceptors.

Automated high-content morphological analysis of muscle fiber histology.

In the search for a cure for many muscular disorders it is often necessary to analyze muscle fibers under a microscope. For this morphological analysis, we developed an image processing approach to automatically analyze and quantify muscle fiber images so as to replace today's less accurate and time-consuming manual method. Muscular disorders, that include cardiomyopathy, muscular dystrophies, and diseases of nerves that affect muscles such as neuropathy and myasthenia gravis, affect a large percentage of the population and, therefore, are an area of active research for new treatments. In research, the morphological features of muscle fibers play an important role as they are often used as biomarkers to evaluate the progress of underlying diseases and the effects of potential treatments. Such analysis involves assessing histopathological changes of muscle fibers as indicators for disease severity and also as a criterion in evaluating whether or not potential treatments work. However, quantifying morphological features is time-consuming, as it is usually performed manually, and error-prone. To replace this standard method, we developed an image processing approach to automatically detect and measure the cross-sections of muscle fibers observed under microscopy that produces faster and more objective results. As such, it is well-suited to processing the large number of muscle fiber images acquired in typical experiments, such as those from studies with pre-clinical models that often create many images. Tests on real images showed that the approach can segment and detect muscle fiber membranes and extract morphological features from highly complex images to generate quantitative results that are readily available for statistical analysis.

An image processing approach to analyze morphological features of microscopic images of muscle fibers.

We present an image processing approach to automatically analyze duo-channel microscopic images of muscular fiber nuclei and cytoplasm. Nuclei and cytoplasm play a critical role in determining the health and functioning of muscular fibers as changes of nuclei and cytoplasm manifest in many diseases such as muscular dystrophy and hypertrophy. Quantitative evaluation of muscle fiber nuclei and cytoplasm thus is of great importance to researchers in musculoskeletal studies. The proposed computational approach consists of steps of image processing to segment and delineate cytoplasm and identify nuclei in two-channel images. Morphological operations like skeletonization is applied to extract the length of cytoplasm for quantification. We tested the approach on real images and found that it can achieve high accuracy, objectivity, and robustness.