IMSI—Guidelines for Sperm Quality Assessment

Intracytoplasmic sperm injection (ICSI) is a widely used and accepted treatment of choice for oocyte fertilization. However, the quality of sperm selection depends on the accurate visualization of the morphology, which can be achieved with a high image resolution. We aim to correct the conviction, shown in a myriad of publications, that an ultra-high magnification in the range of 6000×–10,000× can be achieved with an optical microscope. The goal of observing sperm under the microscope is not to simply get a larger image, but rather to obtain more detail—therefore, we indicate that the optical system’s resolution is what should be primarily considered. We provide specific microscope system setup recommendations sufficient for most clinical cases that are based on our experience showing that the optical resolution of 0.5 μm allows appropriate visualization of sperm defects. Last but not least, we suggest that mixed research results regarding the clinical value of IMSI, comparing to ICSI, can stem from a lack of standardization of microscopy techniques used for both ICSI and IMSI.

Implementation of engineering and geodetic works in the framework of research work, which are aimed at developing proposals for the development of settlements of the municipality

At any stage of urban planning activities, it may be necessary to plan the territory. The territory planning project can be created on topographic plans of a scale of 1: 2000. Modern needs for cadastre maintenance, land management, and design of territories require high quality topographic material obtained in almost real time with high image resolution. This problem is effectively solved by aerial photography from unmanned aerial vehicles (UAVs). Airborne laser scanning allows you to survey difficult terrain and large areas. To clarify the position of some terrain objects, a GNSS survey in RTK mode was used. The combined survey method provides the necessary accuracy of surveying the situation and the terrain, the measurement accuracy corresponds to the possibility of creating engineering-topographic plans at a scale of 1: 2000. In the course of the work, engineering digital terrain models and orthophotomaps were formed. Based on the data obtained, a digital topographic plan of 1: 2000 scale was compiled.

Sequential SEM imaging of microbial calcite precipitation consolidation treatment

Cultural heritage built from limestone is prone to deterioration by chemical weathering, a natural process, that is enhanced by pollution. There are many historic monuments built from calcareous rocks that suffer from deterioration, and thus there have been a number of approaches over the last few decades to consolidate these types of rocks and surfaces. Using natural biological processes by fostering the activity of calcite-producing bacteria, also referred to as biomineralization, is one strategy that has also been commercialized. The base of proving the effectiveness of any surface treatment is the observation of the surface at sequential stages before and after treatment, as well as after exposure to weathering. Due to the heterogeneity of natural materials and processes, our aim was to observe identical test areas at the micron scale throughout the observation period. In order to achieve this on a tungsten SEM, we employed a beam deceleration accessory that allowed low kV imaging on non-conductive surfaces at a sufficiently high image resolution with a modified sample holder accommodating drill cores of 25 mm diameter and up to 15 mm height. The presented method is capable of producing time-sequenced images on the same test area on natural rock surface samples without manipulation for imaging purposes. This offers interesting perspectives for effective documentation of such processes in various fields.

Tissue quantification of radioiodine thyroid uptake in humans by an isotopic imaging technique on slides

BACKGROUND: Thyroid metabolism involves iodine, which allows us to use radioactive iodine for diagnostic and therapy purposes. The efficiency of radioiodine therapy depends on several parameters; the ability of thyroid tissue to uptake radioactive iodine is one of them. OBJECTIVE: The objective of this work is to quantify the radioactive iodine uptake on thyroid tissue. METHODS: In this work, we developed a method to quantify the in vivo uptake of iodine-131 on sections of thyroid glands removed by thyroidectomies. We performed an analysis of histological sections of the thyroid tissue by beta imaging. We had the opportunity to quantify the fixed radioactivity and to analyze its distribution in the thyroid gland, thanks to the good spatial resolution available with the type of detector used. RESULTS: The results gave a high image resolution showing the heterogeneity of iodine-131 fixation by the thyroid tissue. We were able to quantify the tissue radioactivity in mega Becquerel (MBq) per volume unit. CONCLUSION: This work has shown that the direct quantification of the thyroid tissue uptake is possible using the beta imaging system.

Deep Neural Frameworks Improve the Accuracy of General Practitioners in the Classification of Pigmented Skin Lesions

This study evaluated whether deep learning frameworks trained in large datasets can help non-dermatologist physicians improve their accuracy in categorizing the seven most common pigmented skin lesions. Open-source skin images were downloaded from the International Skin Imaging Collaboration (ISIC) archive. Different deep neural networks (DNNs) (n = 8) were trained based on a random dataset constituted of 8015 images. A test set of 2003 images was used to assess the classifiers’ performance at low (300 × 224 RGB) and high (600 × 450 RGB) image resolution and aggregated data (age, sex and lesion localization). We also organized two different contests to compare the DNN performance to that of general practitioners by means of unassisted image observation. Both at low and high image resolution, the DNN framework differentiated dermatological images with appreciable performance. In all cases, the accuracy was improved when adding clinical data to the framework. Finally, the least accurate DNN outperformed general practitioners. The physician’s accuracy was statistically improved when allowed to use the output of this algorithmic framework as guidance. DNNs are proven to be high performers as skin lesion classifiers and can improve general practitioner diagnosis accuracy in a routine clinical scenario.

APPLICATION OF SEMANTIC SEGMENTATION WITH FEW LABELS IN THE DETECTION OF WATER BODIES FROM PERUSAT-1 SATELLITE’S IMAGES

Remote sensing is widely used to monitor earth surfaces with the main objective of extracting information from it. Such is the case of water surface, which is one of the most affected extensions when flood events occur, and its monitoring helps in the analysis of detecting such affected areas, considering that adequately defining water surfaces is one of the biggest problems that Peruvian authorities are concerned with. In this regard, semiautomatic mapping methods improve this monitoring, but this process remains a time-consuming task and into the subjectivity of the experts.In this work, we present a new approach for segmenting water surfaces from satellite images based on the application of convolutional neural networks. First, we explore the application of a U-Net model and then a transfer knowledge-based model. Our results show that both approaches are comparable when trained using an 680-labelled satellite image dataset; however, as the number of training samples is reduced, the performance of the transfer knowledge-based model, which combines high and very high image resolution characteristics, is improved.

Oil Palm Tree Detection from High Resolution Drone Image Using Convolutional Neural Network

We propose a method to combine several image processing methods with Convolutional Neural Network (CNN) to perform palm tree detection and counting. This paper focuses on drone imaging, which has a high image resolution and is widely deployed in the plantation industry. Analyzing drone images is challenging due to variable drone flying altitudes, resulting in inconsistent tree sizes in images captured. Counting by template matching or fixed sliding window size method often produces an inaccurate count. Instead, our method employs frequency domain analysis to estimate tree size before CNN. The method is evaluated using two images, ranging from a few thousand trees to a few hundred thousand trees per image. We have summarized the accuracy of the proposed method by comparing the results with manually labelled ground truth.

Oil Palm Tree Detection from High Resolution Drone Image Using Convolutional Neural Network

We propose a method to combine several image processing methods with Convolutional Neural Network (CNN) to perform palm tree detection and counting. This paper focuses on drone imaging, which has a high image resolution and is widely deployed in the plantation industry. Analyzing drone images is challenging due to variable drone flying altitudes, resulting in inconsistent tree sizes in images captured. Counting by template matching or fixed sliding window size method often produces an inaccurate count. Instead, our method employs frequency domain analysis to estimate tree size before CNN. The method is evaluated using two images, ranging from a few thousand trees to a few hundred thousand trees per image. We have summarized the accuracy of the proposed method by comparing the results with manually labelled ground truth.