Mathematical modelling to compute ground truth from 3D images is an area of research that can strongly benefit from machine learning methods. Deep neural networks (DNNs) are state-of-the-art methods design for solving these kinds of difficulties. Convolutional neural networks (CNNs), as one class of DNNs, can overcome special requirements of quantitative analysis especially when image segmentation is needed. This article presents a system that uses a cascade of CNNs with symmetric blocks of layers in chain, dedicated to 3D image segmentation from microscopic images of 3D nuclei. The system is designed through eight experiments that differ in following aspects: number of training slices and 3D samples for training, usage of pre-trained CNNs and number of slices and 3D samples for validation. CNNs parameters are optimized using linear, brute force, and random combinatorics, followed by voter and median operations. Data augmentation techniques such as reflection, translation and rotation are used in order to produce sufficient training set for CNNs. Optimal CNN parameters are reached by defining 11 standard and two proposed metrics. Finally, benchmarking demonstrates that CNNs improve segmentation accuracy, reliability and increased annotation accuracy, confirming the relevance of CNNs to generate high-throughput mathematical ground truth 3D images.

The aim of this research is to automate an analysis of the EGFR gene as a whole, and especially an analysis of those exons with clinically identified microdeletion mutations which are recorded with non-mutated nucleotides in a long chains of a, c, t, g nucleotides, and “-“ (microdeletion) in the NCBI database or other sites. In addition, the developed system can analyze data resulting from EGFR gene DNA sequencing or DNA extraction for a new patient and identify regions potential microdeletion mutations that clinicians need to develop new

Solving complex combinatorial optimization problems using classical algorithms is not efficient related to resources and time. To overcome the problem, we used optimal parameters selection based on Ant Colony Optimization (ACO) algorithms. In this paper, we present algorithm for solving telecommunication network using ACO for searching optimal ring star network topology. We analyzed ant's optimization ability based on shortest path between the nest and food location. In our research we used: Ant System, Elitist Ant System, Rank-Based Ant System, and MAX-MIN Ant System. The program is developed using GNU C++ to prove the algorithm theoretical convergence through simulation on variety of topologies regarding to node numbers. The algorithm was adapted to solve design of telecommunication network, which connects terminals to concentrators using point-to-point connections. The algorithm's output is a star topology showing connections of concentrators in a ring creating Digital Data Service. Algorithm uses seventeen parameters, with thirteen metrics to evaluate configurations. Program validation is performed using three different network node configurations for all four ACO algorithms, only changing two control parameters: speed of pheromone evaporation and existence of local search. The best path was evaluated based on: total time, number of iterations, ring size, and value of topology.