Detection of urinary bladder mass in CT urography with SPAN

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134872/1/mp2503.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/134872/2/mp2503_am.pd

Mass detection in digital breast tomosynthesis: Deep convolutional neural network with transfer learning from mammography

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/135545/1/mp7345_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/135545/2/mp7345.pd

Urinary bladder segmentation in CT urography using deepâ learning convolutional neural network and level sets

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134923/1/mp4498.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/134923/2/mp4498_am.pd

Deep learning in medical imaging and radiation therapy

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/146980/1/mp13264_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/146980/2/mp13264.pd

Radiomics of Lung Nodules: A Multi-Institutional Study of Robustness and Agreement of Quantitative Imaging Features.

Radiomics is to provide quantitative descriptors of normal and abnormal tissues during classification and prediction tasks in radiology and oncology. Quantitative Imaging Network members are developing radiomic "feature" sets to characterize tumors, in general, the size, shape, texture, intensity, margin, and other aspects of the imaging features of nodules and lesions. Efforts are ongoing for developing an ontology to describe radiomic features for lung nodules, with the main classes consisting of size, local and global shape descriptors, margin, intensity, and texture-based features, which are based on wavelets, Laplacian of Gaussians, Law's features, gray-level co-occurrence matrices, and run-length features. The purpose of this study is to investigate the sensitivity of quantitative descriptors of pulmonary nodules to segmentations and to illustrate comparisons across different feature types and features computed by different implementations of feature extraction algorithms. We calculated the concordance correlation coefficients of the features as a measure of their stability with the underlying segmentation; 68% of the 830 features in this study had a concordance CC of ≥0.75. Pairwise correlation coefficients between pairs of features were used to uncover associations between features, particularly as measured by different participants. A graphical model approach was used to enumerate the number of uncorrelated feature groups at given thresholds of correlation. At a threshold of 0.75 and 0.95, there were 75 and 246 subgroups, respectively, providing a measure for the features' redundancy

Ureter tracking and segmentation in CT urography (CTU) using COMPASS

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134875/1/mp1412_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/134875/2/mp1412.pd

spa typing and enterotoxin gene profile of Staphylococcus aureus isolated from bovine raw milk in Korea

Staphylococcus aureus is a major etiological pathogen of bovine mastitis, which triggers significant economic losses in dairy herds worldwide. In this study, S. aureus strains isolated from the milk of cows suffering from mastitis in Korea were investigated by spa typing and staphylococcal enterotoxin (SE) gene profiling. Forty-four S. aureus strains were isolated from 26 farms in five provinces. All isolates grouped into five clusters and two singletons based on 14 spa types. Cluster 1 and 2 isolates comprised 38.6% and 36.4% of total isolates, respectively, which were distributed in more than four provinces. SE and SE-like toxin genes were detected in 34 (77.3%) isolates and the most frequently detected SE gene profile was seg, sei, selm, seln, and selo genes (16 isolates, 36.3%), which was comparable to one of the genomic islands, Type I νSaβ. This is a first report of spa types and the prevalence of the recently described SE and SE-like toxin genes among S. aureus isolates from bovine raw milk in Korea. Two predominant spa groups were distributed widely and recently described SE and SE-like toxin genes were detected frequently

Erratum: Semiâ automated pulmonary nodule interval segmentation using the NLST data

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/144604/1/mp12905_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/144604/2/mp12905.pd

Human matrix metalloproteinases: An ubiquitarian class of enzymes involved in several pathological processes

Human matrix metalloproteinases (MMPs) belong to the M10 family of the MA clan of endopeptidases. They are ubiquitarian enzymes, structurally characterized by an active site where a Zn(2+) atom, coordinated by three histidines, plays the catalytic role, assisted by a glutamic acid as a general base. Various MMPs display different domain composition, which is very important for macromolecular substrates recognition. Substrate specificity is very different among MMPs, being often associated to their cellular compartmentalization and/or cellular type where they are expressed. An extensive review of the different MMPs structural and functional features is integrated with their pathological role in several types of diseases, spanning from cancer to cardiovascular diseases and to neurodegeneration. It emerges a very complex and crucial role played by these enzymes in many physiological and pathological processes