The Assessment of Activity of Antiseptic Agents against Biofilm of Staphylococcus aureus Measured with the Use of Processed Microscopic Images.

Staphylococcal biofilms are major causative factors of non-healing wound infections. Their treatment algorithms recommend the use of locally applied antiseptic agents to counteract the spread of infection. The efficacy of antiseptics against biofilm is assessed in vitro by a set of standard quantitative and semi-quantitative methods. The development of software for image processing additionally allowed for the obtainment of quantitative data from microscopic images of biofilm dyed with propidium iodine and SYTO-9 reagents, differentiating dead cells from live ones. In this work, the method of assessment of the impact of antiseptic agents on staphylococcal biofilm in vitro, based on biofilms' processed images, was proposed and scrutinized with regard to clinically relevant antiseptics, polyhexanide, povidone-iodine and hypochlorite. The standard quantitative culturing method was applied to validate the obtained data from processed images. The results indicated significantly higher activity of polyhexanide and povidone-iodine than hypochlorite against staphylococcal biofilm. Taking into account the fact that in vitro results of the efficacy of antiseptic agents against staphylococcal biofilm are frequently applied to back up their use in hospitals and ambulatory units, our work should be considered an important tool; providing reliable, quantitative data in this regard.

High-Frequency Ultrasound Dataset for Deep Learning-Based Image Quality Assessment.

This study aims at high-frequency ultrasound image quality assessment for computer-aided diagnosis of skin. In recent decades, high-frequency ultrasound imaging opened up new opportunities in dermatology, utilizing the most recent deep learning-based algorithms for automated image analysis. An individual dermatological examination contains either a single image, a couple of pictures, or an image series acquired during the probe movement. The estimated skin parameters might depend on the probe position, orientation, or acquisition setup. Consequently, the more images analyzed, the more precise the obtained measurements. Therefore, for the automated measurements, the best choice is to acquire the image series and then analyze its parameters statistically. However, besides the correctly received images, the resulting series contains plenty of non-informative data: Images with different artifacts, noise, or the images acquired for the time stamp when the ultrasound probe has no contact with the patient skin. All of them influence further analysis, leading to misclassification or incorrect image segmentation. Therefore, an automated image selection step is crucial. To meet this need, we collected and shared 17,425 high-frequency images of the facial skin from 516 measurements of 44 patients. Two experts annotated each image as correct or not. The proposed framework utilizes a deep convolutional neural network followed by a fuzzy reasoning system to assess the acquired data's quality automatically. Different approaches to binary and multi-class image analysis, based on the VGG-16 model, were developed and compared. The best classification results reach 91.7% accuracy for the first, and 82.3% for the second analysis, respectively.