Evolutionary 3D Image Segmentation of Curve Epithelial Tissues of Drosophila melanogaster

Analysing biological images coming from the microscope is challenging; not only is it complex to acquire the images, but also the three-dimensional shapes found on them. Thus, using automatic approaches that could learn and embrace that variance would be highly interesting for the field. Here, we use an evolutionary algorithm to obtain the 3D cell shape of curve epithelial tissues. Our approach is based on the application of a 3D segmentation algorithm called LimeSeg, which is a segmentation software that uses a particle-based active contour method. This program needs the fine-tuning of some hyperparameters that could present a long number of combinations, with the selection of the best parametrisation being highly time-consuming. Our evolutionary algorithm automatically selects the best possible parametrisation with which it can perform an accurate and non-supervised segmentation of 3D curved epithelial tissues. This way, we combine the segmentation potential of LimeSeg and optimise the parameters selection by adding automatisation. This methodology has been applied to three datasets of confocal images from Drosophila melanogaster, where a good convergence has been observed in the evaluation of the solutions. Our experimental results confirm the proper performing of the algorithm, whose segmented images have been compared to those manually obtained for the same tissues

Evolutionary 3D Image Segmentation of Curve Epithelial Tissues of Drosophila melanogaster

Analysing biological images coming from the microscope is challenging; not only is it complex to acquire the images, but also the three-dimensional shapes found on them. Thus, using automatic approaches that could learn and embrace that variance would be highly interesting for the field. Here, we use an evolutionary algorithm to obtain the 3D cell shape of curve epithelial tissues. Our approach is based on the application of a 3D segmentation algorithm called LimeSeg, which is a segmentation software that uses a particle-based active contour method. This program needs the fine tuning of some hyperparameters that could present a long number of combinations, with the selection of the best parametrisation being highly time-consuming. Our evolutionary algorithm automatically selects the best possible parametrisation with which it can perform an accurate and non-supervised segmentation of 3D curved epithelial tissues. This way, we combine the segmentation potential of LimeSeg and optimise the parameters selection by adding automatisation. This methodology has been applied to three datasets of confocal images from Drosophila melanogaster, where a good convergence has been observed in the evaluation of the solutions. Our experimental results confirm the proper performing of the algorithm, whose segmented images have been compared to those manually obtained for the same tissues.Ministerio de Ciencia, Innovación y Universidades TIN2017-88209-C2Junta de Andalucía US-1263341Junta de Andalucía P18-RT-2778Ministerio de Economía, Industria y Competitividad BFU2016-74975-PMinisterio de Ciencia e Innovación PID2019-103900GB-10

Particle Swarm Optimization

Particle swarm optimization (PSO) is a population based stochastic optimization technique influenced by the social behavior of bird flocking or fish schooling.PSO shares many similarities with evolutionary computation techniques such as Genetic Algorithms (GA). The system is initialized with a population of random solutions and searches for optima by updating generations. However, unlike GA, PSO has no evolution operators such as crossover and mutation. In PSO, the potential solutions, called particles, fly through the problem space by following the current optimum particles. This book represents the contributions of the top researchers in this field and will serve as a valuable tool for professionals in this interdisciplinary field

On Similarities between Inference in Game Theory and Machine Learning

In this paper, we elucidate the equivalence between inference in game theory and machine learning. Our aim in so doing is to establish an equivalent vocabulary between the two domains so as to facilitate developments at the intersection of both fields, and as proof of the usefulness of this approach, we use recent developments in each field to make useful improvements to the other. More specifically, we consider the analogies between smooth best responses in fictitious play and Bayesian inference methods. Initially, we use these insights to develop and demonstrate an improved algorithm for learning in games based on probabilistic moderation. That is, by integrating over the distribution of opponent strategies (a Bayesian approach within machine learning) rather than taking a simple empirical average (the approach used in standard fictitious play) we derive a novel moderated fictitious play algorithm and show that it is more likely than standard fictitious play to converge to a payoff-dominant but risk-dominated Nash equilibrium in a simple coordination game. Furthermore we consider the converse case, and show how insights from game theory can be used to derive two improved mean field variational learning algorithms. We first show that the standard update rule of mean field variational learning is analogous to a Cournot adjustment within game theory. By analogy with fictitious play, we then suggest an improved update rule, and show that this results in fictitious variational play, an improved mean field variational learning algorithm that exhibits better convergence in highly or strongly connected graphical models. Second, we use a recent advance in fictitious play, namely dynamic fictitious play, to derive a derivative action variational learning algorithm, that exhibits superior convergence properties on a canonical machine learning problem (clustering a mixture distribution)

A Survey on Evolutionary Computation for Computer Vision and Image Analysis: Past, Present, and Future Trends

Computer vision (CV) is a big and important field in artificial intelligence covering a wide range of applications. Image analysis is a major task in CV aiming to extract, analyse and understand the visual content of images. However, imagerelated tasks are very challenging due to many factors, e.g., high variations across images, high dimensionality, domain expertise requirement, and image distortions. Evolutionary computation (EC) approaches have been widely used for image analysis with significant achievement. However, there is no comprehensive survey of existing EC approaches to image analysis. To fill this gap, this paper provides a comprehensive survey covering all essential EC approaches to important image analysis tasks including edge detection, image segmentation, image feature analysis, image classification, object detection, and others. This survey aims to provide a better understanding of evolutionary computer vision (ECV) by discussing the contributions of different approaches and exploring how and why EC is used for CV and image analysis. The applications, challenges, issues, and trends associated to this research field are also discussed and summarised to provide further guidelines and opportunities for future research

Naval Mine Detection and Seabed Segmentation in Sonar Images with Deep Learning

Underwater mines are a cost-effective method in asymmetric warfare, and are commonly used to block shipping lanes and restrict naval operations. Consequently, they threaten commercial and military vessels, disrupt humanitarian aids, and damage sea environments. There is a strong international interest in using sonars and AI for mine countermeasures and undersea surveillance. High-resolution imaging sonars are well-suited for detecting underwater mines and other targets. Compared to other sensors, sonars are more effective for undersea environments with low visibility. This project aims to investigate deep learning algorithms for two important tasks in undersea surveillance: naval mine detection and seabed terrain segmentation. Our goal is to automatically classify the composition of the seabed and localise naval mines. This research utilises the real sonar data provided by the Defence Science and Technology Group (DSTG). To conduct the experiments, we annotated 150 sonar images for semantic segmentation; the annotation is guided by experts from the DSTG.We also used 152 sonar images with mine detection annotations prepared by members of Centre for Signal and Information Processing at the University of Wollongong. Our results show Faster-RCNN to achieve the highest performance in object detection. We evaluated transfer learning and data augmentation for object detection. Each method improved our detection models mAP by 11.9% and 16.9% and mAR by 17.8% and 21.1%, respectively. Furthermore, we developed a data augmentation algorithm called Evolutionary Cut-Paste which yielded a 20.2% increase in performance. For segmentation, we found highly-tuned DeepLabV3 and U-Nett++models perform best. We evaluate various configurations of optimisers, learning rate schedules and encoder networks for each model architecture. Additionally, model hyper-parameters are tuned prior to training using various tests. Finally, we apply Median Frequency Balancing to mitigate model bias towards frequently occurring classes. We favour DeepLabV3 due to its reliable detection of underrepresented classes as opposed to the accurate boundaries produced by U-Nett++. All of the models satisfied the constraint of real-time operation when running on an NVIDIA GTX 1070