Lung Pattern Classification for Interstitial Lung Diseases Using a Deep Convolutional Neural Network

Automated tissue characterization is one of the most crucial components of a computer aided diagnosis (CAD) system for interstitial lung diseases (ILDs). Although much research has been conducted in this field, the problem remains challenging. Deep learning techniques have recently achieved impressive results in a variety of computer vision problems, raising expectations that they might be applied in other domains, such as medical image analysis. In this paper, we propose and evaluate a convolutional neural network (CNN), designed for the classification of ILD patterns. The proposed network consists of 5 convolutional layers with 2×2 kernels and LeakyReLU activations, followed by average pooling with size equal to the size of the final feature maps and three dense layers. The last dense layer has 7 outputs, equivalent to the classes considered: healthy, ground glass opacity (GGO), micronodules, consolidation, reticulation, honeycombing and a combination of GGO/reticulation. To train and evaluate the CNN, we used a dataset of 14696 image patches, derived by 120 CT scans from different scanners and hospitals. To the best of our knowledge, this is the first deep CNN designed for the specific problem. A comparative analysis proved the effectiveness of the proposed CNN against previous methods in a challenging dataset. The classification performance (~85.5%) demonstrated the potential of CNNs in analyzing lung patterns. Future work includes, extending the CNN to three-dimensional data provided by CT volume scans and integrating the proposed method into a CAD system that aims to provide differential diagnosis for ILDs as a supportive tool for radiologists.

The Effects of Density, Spatial Pattern, and Competitive Symmetry on Size Variation in Simulated Plant Populations

Patterns of size inequality in crowded plant populations are often taken to be indicative of the degree of size asymmetry of competition, but recent research suggests that some of the patterns attributed to size‐asymmetric competition could be due to spatial structure. To investigate the theoretical relationships between plant density, spatial pattern, and competitive size asymmetry in determining size variation in crowded plant populations, we developed a spatially explicit, individual‐based plant competition model based on overlapping zones of influence. The zone of influence of each plant is modeled as a circle, growing in two dimensions, and is allometrically related to plant biomass. The area of the circle represents resources potentially available to the plant, and plants compete for resources in areas in which they overlap. The size asymmetry of competition is reflected in the rules for dividing up the overlapping areas. Theoretical plant populations were grown in random and in perfectly uniform spatial patterns at four densities under size‐asymmetric and size‐symmetric competition. Both spatial pattern and size asymmetry contributed to size variation, but their relative importance varied greatly over density and over time. Early in stand development, spatial pattern was more important than the symmetry of competition in determining the degree of size variation within the population, but after plants grew and competition intensified, the size asymmetry of competition became a much more important source of size variation. Size variability was slightly higher at higher densities when competition was symmetric and plants were distributed nonuniformly in space. In a uniform spatial pattern, size variation increased with density only when competition was size asymmetric. Our results suggest that when competition is size asymmetric and intense, it will be more important in generating size variation than is local variation in density. Our results and the available data are consistent with the hypothesis that high levels of size inequality commonly observed within crowded plant populations are largely due to size‐asymmetric competition, not to variation in local density.

Virulence-associated gene pattern of porcine and human Yersinia enterocolitica biotype 4 isolates.

Yersinia enterocolitica 4/O:3 is the most important human pathogenic bioserotype in Europe and the predominant pathogenic bioserotype in slaughter pigs. Although many studies on the virulence of Y. enterocolitica strains have showed a broad spectrum of detectable factors in pigs and humans, an analysis based on a strict comparative approach and serving to verify the virulence capability of porcine Y. enterocolitica as a source for human yersiniosis is lacking. Therefore, in the present study, strains of biotype (BT) 4 isolated from Swiss slaughter pig tonsils and feces and isolates from human clinical cases were compared in terms of their spectrum of virulence-associated genes (yadA, virF, ail, inv, rovA, ymoA, ystA, ystB and myfA). An analysis of the associated antimicrobial susceptibility pattern completed the characterization. All analyzed BT 4 strains showed a nearly similar pattern, comprising the known fundamental virulence-associated genes yadA, virF, ail, inv, rovA, ymoA, ystA and myfA. Only ystB was not detectable among all analyzed isolates. Importantly, neither the source of the isolates (porcine tonsils and feces, humans) nor the serotype (ST) had any influence on the gene pattern. From these findings, it can be concluded that the presence of the full complement of virulence genes necessary for human infection is common among porcine BT 4 strains. Swiss porcine BT 4 strains not only showed antimicrobial susceptibility to chloramphenicol, cefotaxime, ceftazidime, ciprofloxacin, colistin, florfenicol, gentamicin, kanamycin, nalidixic acid, sulfamethoxazole, streptomycin, tetracycline and trimethoprim but also showed 100% antibiotic resistance to ampicillin. The human BT 4 strains revealed comparable results. However, in addition to 100% antibiotic resistance to ampicillin, 2 strains were resistant to chloramphenicol and nalidixic acid. Additionally, 1 of these strains was resistant to sulfamethoxazole. The results demonstrated that Y. enterocolitica BT 4 isolates from porcine tonsils, as well as from feces, show the same virulence-associated gene pattern and antibiotic resistance properties as human isolates from clinical cases, consistent with the etiological role of porcine BT 4 in human yersiniosis. Thus, cross-contamination of carcasses and organs at slaughter with porcine Y. enterocolitica BT 4 strains, either from tonsils or feces, must be prevented to reduce human yersiniosis.