Informatica

Intonation is a complex suprasegmental phenomenon essential for speech processing. However, it is still largely understudied, especially in the case of under-resourced languages, such as Lithuanian. The current paper focuses on intonation in Lithuanian, a Baltic pitch-accent language with free stress and tonal variations on accented heavy syllables. Due to historical circumstances, the description and analysis of Lithuanian intonation were carried out within different theoretical frameworks and in several languages, which makes them hardly accessible to the international research community. This paper is the first attempt to gather research on Lithuanian intonation from both the Lithuanian and the Western traditions, the structuralist and generativist points of view, and the linguistic and modelling perspectives. The paper identifies issues in existing research that require special attention and proposes directions for future investigations both in linguistics and modelling.

Anti-cancer immunotherapy dramatically changes the clinical management of many types of tumours towards less harmful and more personalized treatment plans than conventional chemotherapy or radiation. Precise analysis of the spatial distribution of immune cells in the tumourous tissue is necessary to select patients that would best respond to the treatment. Here, we introduce a deep learning-based workflow for cell nuclei segmentation and subsequent immune cell identification in routine diagnostic images. We applied our workflow on a set of hematoxylin and eosin (H&E) stained breast cancer and colorectal cancer tissue images to detect tumour-infiltrating lymphocytes. Firstly, to segment all nuclei in the tissue, we applied the multiple-image input layer architecture (Micro-Net, Dice coefficient (DC) $0.79\pm 0.02$). We supplemented the Micro-Net with an introduced texture block to increase segmentation accuracy (DC = $0.80\pm 0.02$). We preserved the shallow architecture of the segmentation network with only 280 K trainable parameters (e.g. U-net with ∼1900 K parameters, DC = $0.78\pm 0.03$). Subsequently, we added an active contour layer to the ground truth images to further increase the performance (DC = $0.81\pm 0.02$). Secondly, to discriminate lymphocytes from the set of all segmented nuclei, we explored multilayer perceptron and achieved a 0.70 classification f-score. Remarkably, the binary classification of segmented nuclei was significantly improved (f-score = 0.80) by colour normalization. To inspect model generalization, we have evaluated trained models on a public dataset that was not put to use during training. We conclude that the proposed workflow achieved promising results and, with little effort, can be employed in multi-class nuclei segmentation and identification tasks.

Eye fundus imaging is a useful, non-invasive tool in disease progress tracking, in early detection of disease and other cases. Often, the disease diagnosis is made by an ophthalmologist and automatic analysis systems are used only for support. There are several commonly used features for disease detection, one of them is the artery and vein ratio measured according to the width of the main vessels. Arteries must be separated from veins automatically in order to calculate the ratio, therefore, vessel classification is a vital step. For most analysis methods high quality images are required for correct classification. This paper presents an adaptive algorithm for vessel measurements without the necessity to tune the algorithm for concrete imaging equipment or a specific situation. The main novelty of the proposed method is the extraction of blood vessel features based on vessel width measurement algorithm and vessel spatial dependency. Vessel classification accuracy rates of 0.855 and 0.859 are obtained on publicly available eye fundus image databases used for comparison with another state of the art algorithms for vessel classification in order to evaluate artery-vein ratio ($AVR$). The method is also evaluated with images that represent artery and vein size changes before and after physical load. Optomed OY digital mobile eye fundus camera Smartscope M5 PRO is used for image gathering.

The recent introduction of whole-slide scanning systems enabled accumulation of high-quality pathology images into large collections, thus opening new perspectives in cancer research, as well as new analysis challenges. Automated identification of tumour tissue in the whole-slide image enables further use of developed grading systems that classify tumour cell abnormalities and predict tumour developments. In this article, we describe several possibilities to achieve epithelium-stroma classification of tumour tissues in digital pathology images by employing annotated superpixels to train machine learning algorithms. We emphasize that annotating superpixels rather than manually outlining tissue classes in raw images is less time consuming, and more effective way of producing ground truth for computational pathology pipelines. In our approach feature space for supervised learning is created from tissue class assigned superpixels by extracting colour and texture parameters, and applying dimensionality reduction methods. Alternatively, to train convolutional neural network, labelled superpixels are used to generate square image patches by moving fixed size window around each superpixel centroid. The proposed method simplifies the process of ground truth data collection and should minimize the time spent by a skilled expert to perform manual annotation of whole-slide images. We evaluate our method on a private data set of colorectal cancer images. Obtained results confirm that a method produces accurate reference data suitable for the use of different machine learning based classification algorithms.

In recent years, the growth of marine traffic in ports and their surroundings raise the traffic and security control problems and increase the workload for traffic control operators. The automated identification system of vessel movement generates huge amounts of data that need to be analysed to make the proper decision. Thus, rapid self-learning algorithms for the decision support system have to be developed to detect the abnormal vessel movement in intense marine traffic areas. The paper presents a new self-learning adaptive classification algorithm based on the combination of a self-organizing map (SOM) and a virtual pheromone for abnormal vessel movement detection in maritime traffic. To improve the quality of classification results, Mexican hat neighbourhood function has been used as a SOM neighbourhood function. To estimate the classification results of the proposed algorithm, an experimental investigation has been performed using the real data set, provided by the Klaipėda seaport and that obtained from the automated identification system. The results of the research show that the proposed algorithm provides rapid self-learning characteristics and classification.

Glaucoma is one of the most insidious eye diseases the occurrence and progression of which a human does not feel. This article provides a brief overview of the eye nerve parameterization methods and algorithms. Parameterization itself is an important task that provides and uniquely defines the structure of the optic nerve disc and further can be used in disease detection or other studies that require a parametric estimate of the eye fundus pattern. So far, planimetric completely automated parameterization of excavation from eye fundus images has not been investigated in detail in the scientific literature. In this article, the authors describe an automated excavation and parameterization algorithm and make the correlation analysis of parameters obtained by both automated and interactive techniques. The obtained results are then compared with those produced by Optical Coherence and Heidelberg Retina Tomography. Finally, the article discusses glaucoma disease detection abilities using the estimated parameters of the eye fundus structures, obtained by different parameterization techniques.

In this article, a method is proposed for analysing the thermovision-based video data that characterize the dynamics of temperature anisotropy of the heart tissue in a spatial domain. Many cardiac rhythm disturbances at present time are treated by applying destructive energy sources. One of the most common source and the related methodology is to use radio-frequency ablation procedure. However, the rate of the risk of complications including arrhythmia recurrence remains enough high. The drawback of the methodology used is that the suchlike destruction procedure cannot be monitored by visual spectra and results in the inability to control the ablation efficiency. To the end of understanding the nature of possible complications and controlling the treating process, the means of thermovision could be used. The aim of the study was to analyse possible mechanisms of these complications, measure and determine optimal radio-frequency ablation parameters, according to the analysis of video data, acquired using thermovision.

New information technologies provide a possibility of collecting a large amount of fundus images into databases. It allows us to use automated processing and classification of images for clinical decisions. Automated localization and parameterization of the optic nerve disc is particularly important in making a diagnosis of glaucoma, because the main symptoms in these cases are relations between the optic nerve and cupping parameters. This article describes the automated algorithm for the optic nerve disc localization and parameterization by an ellipse within colour retinal images. The testing results are discussed as well.